Prompt:
Implement the Python class `JobPlanner` described below.

Class description:
Handles workflow execution requirements. This class keeps track of which hardware is needed for each available step, then analyses a workflow and decides which resources it needs based on this.

Method signatures and docstrings:
- def __init__(self, job_store: SQLiteJobStore, local_api_dir: cerulean.Path): Create a JobPlanner. Args: job_store: The job store to act on. local_api_dir: Path of local api directory.
- def plan_job(self, job_id: str) -> None: Figures out which resources a job needs. Resources are identified by strings. Currently, there is ``num_cores``, the number of cores to run on, and ``time_limit``, the amount of time to reserve in seconds. Args: job_id: Id of the job to plan.
- def _get_steps_resource_requirements(self, local_api_dir: cerulean.Path) -> None: Scan CWL steps and extract resource requirements. Args: local_api_dir: The local directory with the API

Implement the Python class `JobPlanner` described below.

Class description:
Handles workflow execution requirements. This class keeps track of which hardware is needed for each available step, then analyses a workflow and decides which resources it needs based on this.

Method signatures and docstrings:
- def __init__(self, job_store: SQLiteJobStore, local_api_dir: cerulean.Path): Create a JobPlanner. Args: job_store: The job store to act on. local_api_dir: Path of local api directory.
- def plan_job(self, job_id: str) -> None: Figures out which resources a job needs. Resources are identified by strings. Currently, there is ``num_cores``, the number of cores to run on, and ``time_limit``, the amount of time to reserve in seconds. Args: job_id: Id of the job to plan.
- def _get_steps_resource_requirements(self, local_api_dir: cerulean.Path) -> None: Scan CWL steps and extract resource requirements. Args: local_api_dir: The local directory with the API

<|skeleton|>
class JobPlanner:
    """Handles workflow execution requirements. This class keeps track of which hardware is needed for each available step, then analyses a workflow and decides which resources it needs based on this."""

    def __init__(self, job_store: SQLiteJobStore, local_api_dir: cerulean.Path):
        """Create a JobPlanner. Args: job_store: The job store to act on. local_api_dir: Path of local api directory."""
        <|body_0|>

    def plan_job(self, job_id: str) -> None:
        """Figures out which resources a job needs. Resources are identified by strings. Currently, there is ``num_cores``, the number of cores to run on, and ``time_limit``, the amount of time to reserve in seconds. Args: job_id: Id of the job to plan."""
        <|body_1|>

    def _get_steps_resource_requirements(self, local_api_dir: cerulean.Path) -> None:
        """Scan CWL steps and extract resource requirements. Args: local_api_dir: The local directory with the API"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self._logger = logging.getLogger(__name__)
        'A logger for this object.'
        self._job_store = job_store
        'The job store to act on.'
        self._steps_requirements = dict()
        'Requirements per step, keyed by step name and requirement\n                name.\n        '
        self._get_steps_resource_requirements(local_api_dir)
<|end_body_0|>

<|body_start_1|>
        with self._job_store:
            job = self._job_store.get_job(job_id)
            steps = get_workflow_step_names(cast(bytes, job.workflow_content))
            for step in steps:
                if step not in self._steps_requirements:
                    job.error('Found invalid step {} in workflow'.format(step))
                    raise InvalidJobError('Invalid step in workflow')
            job.required_num_cores = get_required_num_cores(cast(bytes, job.workflow_content))
            num_cores_steps = [self._steps_requirements[step]['num_cores'] for step in steps]
            if max(num_cores_steps) > 0:
                job.required_num_cores = max(num_cores_steps)
            job.time_limit = get_time_limit(cast(bytes, job.workflow_content))
            time_limit_steps = [self._steps_requirements[step]['time_limit'] for step in steps]
            job.time_limit = max(job.time_limit, sum(time_limit_steps))
<|end_body_1|>

<|body_start_2|>
        for project_dir in local_api_dir.iterdir():
            local_steps_dir = project_dir / 'steps'
            for this_dir, _, files in local_steps_dir.walk():
                for filename in files:
                    if filename.endswith('.cwl'):
                        self._logger.debug('Scanning file for requirements: {}'.format(this_dir / filename))
                        rel_this_dir = this_dir.relative_to(str(local_steps_dir))
                        step_name = str(rel_this_dir / filename)
                        step_contents = (this_dir / filename).read_bytes()
                        step_num_cores = get_required_num_cores(step_contents)
                        step_time_limit = get_time_limit(step_contents)
                        if step_name not in self._steps_requirements:
                            self._steps_requirements[step_name] = dict()
                        self._steps_requirements[step_name]['num_cores'] = step_num_cores
                        self._steps_requirements[step_name]['time_limit'] = step_time_limit
                        self._logger.debug('Step {} requires {} cores'.format(step_name, step_num_cores))
<|end_body_2|>

Solution:
class JobPlanner:
    """Handles workflow execution requirements. This class keeps track of which hardware is needed for each available step, then analyses a workflow and decides which resources it needs based on this."""

    def __init__(self, job_store: SQLiteJobStore, local_api_dir: cerulean.Path):
        """Create a JobPlanner. Args: job_store: The job store to act on. local_api_dir: Path of local api directory."""
        self._logger = logging.getLogger(__name__)
        'A logger for this object.'
        self._job_store = job_store
        'The job store to act on.'
        self._steps_requirements = dict()
        'Requirements per step, keyed by step name and requirement\n                name.\n        '
        self._get_steps_resource_requirements(local_api_dir)

    def plan_job(self, job_id: str) -> None:
        """Figures out which resources a job needs. Resources are identified by strings. Currently, there is ``num_cores``, the number of cores to run on, and ``time_limit``, the amount of time to reserve in seconds. Args: job_id: Id of the job to plan."""
        with self._job_store:
            job = self._job_store.get_job(job_id)
            steps = get_workflow_step_names(cast(bytes, job.workflow_content))
            for step in steps:
                if step not in self._steps_requirements:
                    job.error('Found invalid step {} in workflow'.format(step))
                    raise InvalidJobError('Invalid step in workflow')
            job.required_num_cores = get_required_num_cores(cast(bytes, job.workflow_content))
            num_cores_steps = [self._steps_requirements[step]['num_cores'] for step in steps]
            if max(num_cores_steps) > 0:
                job.required_num_cores = max(num_cores_steps)
            job.time_limit = get_time_limit(cast(bytes, job.workflow_content))
            time_limit_steps = [self._steps_requirements[step]['time_limit'] for step in steps]
            job.time_limit = max(job.time_limit, sum(time_limit_steps))

    def _get_steps_resource_requirements(self, local_api_dir: cerulean.Path) -> None:
        """Scan CWL steps and extract resource requirements. Args: local_api_dir: The local directory with the API"""
        for project_dir in local_api_dir.iterdir():
            local_steps_dir = project_dir / 'steps'
            for this_dir, _, files in local_steps_dir.walk():
                for filename in files:
                    if filename.endswith('.cwl'):
                        self._logger.debug('Scanning file for requirements: {}'.format(this_dir / filename))
                        rel_this_dir = this_dir.relative_to(str(local_steps_dir))
                        step_name = str(rel_this_dir / filename)
                        step_contents = (this_dir / filename).read_bytes()
                        step_num_cores = get_required_num_cores(step_contents)
                        step_time_limit = get_time_limit(step_contents)
                        if step_name not in self._steps_requirements:
                            self._steps_requirements[step_name] = dict()
                        self._steps_requirements[step_name]['num_cores'] = step_num_cores
                        self._steps_requirements[step_name]['time_limit'] = step_time_limit
                        self._logger.debug('Step {} requires {} cores'.format(step_name, step_num_cores))


[summary] コンストラクタのテストメソッド

Prompt:
Implement the Python class `Fractiontest` described below.

Class description:
[summary] Fractionクラスのユニットテストクラス

Method signatures and docstrings:
- def test_constructor(self): [summary] コンストラクタのテストメソッド
- def test_add(self): [summary] __add__と__iadd__のテストメソッド
- def test_sub(self): [summary] __sub__と__isub__のテストメソッド
- def test_mul(self): [summary] __mul__と__imul__のテストメソッド
- def test_truediv(self): [summary] __truediv__と__itruediv__のテストメソッド

Implement the Python class `Fractiontest` described below.

Class description:
[summary] Fractionクラスのユニットテストクラス

Method signatures and docstrings:
- def test_constructor(self): [summary] コンストラクタのテストメソッド
- def test_add(self): [summary] __add__と__iadd__のテストメソッド
- def test_sub(self): [summary] __sub__と__isub__のテストメソッド
- def test_mul(self): [summary] __mul__と__imul__のテストメソッド
- def test_truediv(self): [summary] __truediv__と__itruediv__のテストメソッド

<|skeleton|>
class Fractiontest:
    """[summary] Fractionクラスのユニットテストクラス"""

    def test_constructor(self):
        """[summary] コンストラクタのテストメソッド"""
        <|body_0|>

    def test_add(self):
        """[summary] __add__と__iadd__のテストメソッド"""
        <|body_1|>

    def test_sub(self):
        """[summary] __sub__と__isub__のテストメソッド"""
        <|body_2|>

    def test_mul(self):
        """[summary] __mul__と__imul__のテストメソッド"""
        <|body_3|>

    def test_truediv(self):
        """[summary] __truediv__と__itruediv__のテストメソッド"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        bunsi, bunbo = (3, 7)
        fra = Fraction(bunsi, bunbo)
        self.assertEqual(bunsi, fra.numerator)
        self.assertEqual(bunbo, fra.denominator)
<|end_body_0|>

<|body_start_1|>
        fra_base = Fraction(1, 2)
        fra_add = Fraction(1, 3)
        fra_exp = Fraction(5, 6)
        fra_calc = fra_base + fra_add
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base += fra_add
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)
<|end_body_1|>

<|body_start_2|>
        fra_base = Fraction(1, 2)
        fra_sub = Fraction(1, 3)
        fra_exp = Fraction(1, 6)
        fra_calc = fra_base - fra_sub
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base -= fra_sub
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)
<|end_body_2|>

<|body_start_3|>
        fra_base = Fraction(1, 2)
        fra_mul = Fraction(1, 3)
        fra_exp = Fraction(1, 6)
        fra_calc = fra_base * fra_mul
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base *= fra_mul
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)
<|end_body_3|>

<|body_start_4|>
        fra_base = Fraction(1, 2)
        fra_truediv = Fraction(1, 3)
        fra_exp = Fraction(3, 2)
        fra_calc = fra_base / fra_truediv
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base /= fra_truediv
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)
<|end_body_4|>

Solution:
class Fractiontest:
    """[summary] Fractionクラスのユニットテストクラス"""

    def test_constructor(self):
        """[summary] コンストラクタのテストメソッド"""
        bunsi, bunbo = (3, 7)
        fra = Fraction(bunsi, bunbo)
        self.assertEqual(bunsi, fra.numerator)
        self.assertEqual(bunbo, fra.denominator)

    def test_add(self):
        """[summary] __add__と__iadd__のテストメソッド"""
        fra_base = Fraction(1, 2)
        fra_add = Fraction(1, 3)
        fra_exp = Fraction(5, 6)
        fra_calc = fra_base + fra_add
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base += fra_add
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)

    def test_sub(self):
        """[summary] __sub__と__isub__のテストメソッド"""
        fra_base = Fraction(1, 2)
        fra_sub = Fraction(1, 3)
        fra_exp = Fraction(1, 6)
        fra_calc = fra_base - fra_sub
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base -= fra_sub
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)

    def test_mul(self):
        """[summary] __mul__と__imul__のテストメソッド"""
        fra_base = Fraction(1, 2)
        fra_mul = Fraction(1, 3)
        fra_exp = Fraction(1, 6)
        fra_calc = fra_base * fra_mul
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base *= fra_mul
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)

    def test_truediv(self):
        """[summary] __truediv__と__itruediv__のテストメソッド"""
        fra_base = Fraction(1, 2)
        fra_truediv = Fraction(1, 3)
        fra_exp = Fraction(3, 2)
        fra_calc = fra_base / fra_truediv
        self.assertEqual(fra_exp.numerator, fra_calc.numerator)
        self.assertEqual(fra_exp.denominator, fra_calc.denominator)
        fra_base /= fra_truediv
        self.assertEqual(fra_exp.numerator, fra_base.numerator)
        self.assertEqual(fra_exp.denominator, fra_base.denominator)


Prompt:
Implement the Python class `Decorators` described below.

Class description:
Implement the Decorators class.

Method signatures and docstrings:
- def decor0(self, func): декоратор 0, позволяющий вместе с результатом функции возвращать время ее работы
- def decor1(self, f): декоратор 1, позволяющий вместе с результатом функции возвращать время ее работы
- def decor2(self, f): декоратор, позволяющий записывать время работы функции, имя функции и переданные ей параметры в текстовый файл
- def decor3(self, f): декоратор, проверяющий типы, переданных декорируемой функции, аргументов
- def decor4(self, f): декоратор, который кэширует результат работы функции, тем самым обеспечивает единственный вызов функции

Implement the Python class `Decorators` described below.

Class description:
Implement the Decorators class.

Method signatures and docstrings:
- def decor0(self, func): декоратор 0, позволяющий вместе с результатом функции возвращать время ее работы
- def decor1(self, f): декоратор 1, позволяющий вместе с результатом функции возвращать время ее работы
- def decor2(self, f): декоратор, позволяющий записывать время работы функции, имя функции и переданные ей параметры в текстовый файл
- def decor3(self, f): декоратор, проверяющий типы, переданных декорируемой функции, аргументов
- def decor4(self, f): декоратор, который кэширует результат работы функции, тем самым обеспечивает единственный вызов функции

<|skeleton|>
class Decorators:

    def decor0(self, func):
        """декоратор 0, позволяющий вместе с результатом функции возвращать время ее работы"""
        <|body_0|>

    def decor1(self, f):
        """декоратор 1, позволяющий вместе с результатом функции возвращать время ее работы"""
        <|body_1|>

    def decor2(self, f):
        """декоратор, позволяющий записывать время работы функции, имя функции и переданные ей параметры в текстовый файл"""
        <|body_2|>

    def decor3(self, f):
        """декоратор, проверяющий типы, переданных декорируемой функции, аргументов"""
        <|body_3|>

    def decor4(self, f):
        """декоратор, который кэширует результат работы функции, тем самым обеспечивает единственный вызов функции"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        def wrapper(*arg):
            t1 = time.clock()
            res = func(*arg)
            t2 = time.clock()
            print('%0.3fms' % ((t2 - t1) * 1000.0))
            return res
        return wrapper
<|end_body_0|>

<|body_start_1|>
        @wraps(f)
        def wrapp(*args, **kwargs):
            time_start = round(time.time(), 5)
            result = f(*args, **kwargs)
            time_end = round(time.time(), 5)
            print('Time for func {} is {}'.format(f.__name__, time_end - time_start))
            print('The', f.__name__, 'result is: ', result)
            return result
        return wrapp
<|end_body_1|>

<|body_start_2|>
        def wrapp(*args, **kwargs):
            time_start = round(time.time(), 5)
            result = f(*args, **kwargs)
            time_end = round(time.time(), 5)
            with open('decor_data.txt', 'w') as fl:
                fl.write(' Func name is: {}\n Work time is: {}\n Args, kwargs are: {}\n The result: {}'.format(f.__name__, time_end - time_start, str(*args) + ', ' + str(kwargs), result))
                return result
        return wrapp
<|end_body_2|>

<|body_start_3|>
        @wraps(f)
        def wrapp(*args, **kwargs):
            result = f(*args, **kwargs)
            print(*args)
            print('Type of args, values for func {} are args: {},\n                  values:{}'.format(f.__name__, type(*args), [type(v) for v in kwargs.values()]))
            return result
        return wrapp
<|end_body_3|>

<|body_start_4|>
        @wraps(f)
        def wrapp(*args, **kwargs):
            if wrapp.value is False:
                print(f.__name__ + ' исполняется')
                f(*args, **kwargs)
                print(f.__name__ + ' была исполнена')
                wrapp.value = True
                return wrapp.value
            return print("It's already finished")
        wrapp.value = False
        return wrapp
<|end_body_4|>

Solution:
class Decorators:
    def decor0(self, func):
        """декоратор 0, позволяющий вместе с результатом функции возвращать время ее работы"""
        def wrapper(*arg):
            t1 = time.clock()
            res = func(*arg)
            t2 = time.clock()
            print('%0.3fms' % ((t2 - t1) * 1000.0))
            return res
        return wrapper

    def decor1(self, f):
        """декоратор 1, позволяющий вместе с результатом функции возвращать время ее работы"""
        @wraps(f)
        def wrapp(*args, **kwargs):
            time_start = round(time.time(), 5)
            result = f(*args, **kwargs)
            time_end = round(time.time(), 5)
            print('Time for func {} is {}'.format(f.__name__, time_end - time_start))
            print('The', f.__name__, 'result is: ', result)
            return result
        return wrapp

    def decor2(self, f):
        """декоратор, позволяющий записывать время работы функции, имя функции и переданные ей параметры в текстовый файл"""
        def wrapp(*args, **kwargs):
            time_start = round(time.time(), 5)
            result = f(*args, **kwargs)
            time_end = round(time.time(), 5)
            with open('decor_data.txt', 'w') as fl:
                fl.write(' Func name is: {}\n Work time is: {}\n Args, kwargs are: {}\n The result: {}'.format(f.__name__, time_end - time_start, str(*args) + ', ' + str(kwargs), result))
                return result
        return wrapp

    def decor3(self, f):
        """декоратор, проверяющий типы, переданных декорируемой функции, аргументов"""
        @wraps(f)
        def wrapp(*args, **kwargs):
            result = f(*args, **kwargs)
            print(*args)
            print('Type of args, values for func {} are args: {},\n                  values:{}'.format(f.__name__, type(*args), [type(v) for v in kwargs.values()]))
            return result
        return wrapp

    def decor4(self, f):
        """декоратор, который кэширует результат работы функции, тем самым обеспечивает единственный вызов функции"""
        @wraps(f)
        def wrapp(*args, **kwargs):
            if wrapp.value is False:
                print(f.__name__ + ' исполняется')
                f(*args, **kwargs)
                print(f.__name__ + ' была исполнена')
                wrapp.value = True
                return wrapp.value
            return print("It's already finished")
        wrapp.value = False
        return wrapp


serialize

Prompt:
Implement the Python class `AccessPackageAssignmentRequest` described below.

Class description:
Implement the AccessPackageAssignmentRequest class.

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessPackageAssignmentRequest: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AccessPackageAssignmentRequest
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

Implement the Python class `AccessPackageAssignmentRequest` described below.

Class description:
Implement the AccessPackageAssignmentRequest class.

Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessPackageAssignmentRequest: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AccessPackageAssignmentRequest
- def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]: The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]
- def serialize(self, writer: SerializationWriter) -> None: Serializes information the current object Args: writer: Serialization writer to use to serialize this model

<|skeleton|>
class AccessPackageAssignmentRequest:

    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessPackageAssignmentRequest:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AccessPackageAssignmentRequest"""
        <|body_0|>

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        <|body_1|>

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return AccessPackageAssignmentRequest()
<|end_body_0|>

<|body_start_1|>
        from .access_package import AccessPackage
        from .access_package_answer import AccessPackageAnswer
        from .access_package_assignment import AccessPackageAssignment
        from .access_package_request_state import AccessPackageRequestState
        from .access_package_request_type import AccessPackageRequestType
        from .access_package_subject import AccessPackageSubject
        from .custom_extension_callout_instance import CustomExtensionCalloutInstance
        from .entitlement_management_schedule import EntitlementManagementSchedule
        from .entity import Entity
        from .access_package import AccessPackage
        from .access_package_answer import AccessPackageAnswer
        from .access_package_assignment import AccessPackageAssignment
        from .access_package_request_state import AccessPackageRequestState
        from .access_package_request_type import AccessPackageRequestType
        from .access_package_subject import AccessPackageSubject
        from .custom_extension_callout_instance import CustomExtensionCalloutInstance
        from .entitlement_management_schedule import EntitlementManagementSchedule
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'accessPackage': lambda n: setattr(self, 'access_package', n.get_object_value(AccessPackage)), 'answers': lambda n: setattr(self, 'answers', n.get_collection_of_object_values(AccessPackageAnswer)), 'assignment': lambda n: setattr(self, 'assignment', n.get_object_value(AccessPackageAssignment)), 'completedDateTime': lambda n: setattr(self, 'completed_date_time', n.get_datetime_value()), 'createdDateTime': lambda n: setattr(self, 'created_date_time', n.get_datetime_value()), 'customExtensionCalloutInstances': lambda n: setattr(self, 'custom_extension_callout_instances', n.get_collection_of_object_values(CustomExtensionCalloutInstance)), 'requestType': lambda n: setattr(self, 'request_type', n.get_enum_value(AccessPackageRequestType)), 'requestor': lambda n: setattr(self, 'requestor', n.get_object_value(AccessPackageSubject)), 'schedule': lambda n: setattr(self, 'schedule', n.get_object_value(EntitlementManagementSchedule)), 'state': lambda n: setattr(self, 'state', n.get_enum_value(AccessPackageRequestState)), 'status': lambda n: setattr(self, 'status', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_object_value('accessPackage', self.access_package)
        writer.write_collection_of_object_values('answers', self.answers)
        writer.write_object_value('assignment', self.assignment)
        writer.write_datetime_value('completedDateTime', self.completed_date_time)
        writer.write_datetime_value('createdDateTime', self.created_date_time)
        writer.write_collection_of_object_values('customExtensionCalloutInstances', self.custom_extension_callout_instances)
        writer.write_enum_value('requestType', self.request_type)
        writer.write_object_value('requestor', self.requestor)
        writer.write_object_value('schedule', self.schedule)
        writer.write_enum_value('state', self.state)
        writer.write_str_value('status', self.status)
<|end_body_2|>

Solution:
class AccessPackageAssignmentRequest:
    def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessPackageAssignmentRequest:
        """Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AccessPackageAssignmentRequest"""
        if not parse_node:
            raise TypeError('parse_node cannot be null.')
        return AccessPackageAssignmentRequest()

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .access_package import AccessPackage
        from .access_package_answer import AccessPackageAnswer
        from .access_package_assignment import AccessPackageAssignment
        from .access_package_request_state import AccessPackageRequestState
        from .access_package_request_type import AccessPackageRequestType
        from .access_package_subject import AccessPackageSubject
        from .custom_extension_callout_instance import CustomExtensionCalloutInstance
        from .entitlement_management_schedule import EntitlementManagementSchedule
        from .entity import Entity
        from .access_package import AccessPackage
        from .access_package_answer import AccessPackageAnswer
        from .access_package_assignment import AccessPackageAssignment
        from .access_package_request_state import AccessPackageRequestState
        from .access_package_request_type import AccessPackageRequestType
        from .access_package_subject import AccessPackageSubject
        from .custom_extension_callout_instance import CustomExtensionCalloutInstance
        from .entitlement_management_schedule import EntitlementManagementSchedule
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'accessPackage': lambda n: setattr(self, 'access_package', n.get_object_value(AccessPackage)), 'answers': lambda n: setattr(self, 'answers', n.get_collection_of_object_values(AccessPackageAnswer)), 'assignment': lambda n: setattr(self, 'assignment', n.get_object_value(AccessPackageAssignment)), 'completedDateTime': lambda n: setattr(self, 'completed_date_time', n.get_datetime_value()), 'createdDateTime': lambda n: setattr(self, 'created_date_time', n.get_datetime_value()), 'customExtensionCalloutInstances': lambda n: setattr(self, 'custom_extension_callout_instances', n.get_collection_of_object_values(CustomExtensionCalloutInstance)), 'requestType': lambda n: setattr(self, 'request_type', n.get_enum_value(AccessPackageRequestType)), 'requestor': lambda n: setattr(self, 'requestor', n.get_object_value(AccessPackageSubject)), 'schedule': lambda n: setattr(self, 'schedule', n.get_object_value(EntitlementManagementSchedule)), 'state': lambda n: setattr(self, 'state', n.get_enum_value(AccessPackageRequestState)), 'status': lambda n: setattr(self, 'status', n.get_str_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_object_value('accessPackage', self.access_package)
        writer.write_collection_of_object_values('answers', self.answers)
        writer.write_object_value('assignment', self.assignment)
        writer.write_datetime_value('completedDateTime', self.completed_date_time)
        writer.write_datetime_value('createdDateTime', self.created_date_time)
        writer.write_collection_of_object_values('customExtensionCalloutInstances', self.custom_extension_callout_instances)
        writer.write_enum_value('requestType', self.request_type)
        writer.write_object_value('requestor', self.requestor)
        writer.write_object_value('schedule', self.schedule)
        writer.write_enum_value('state', self.state)
        writer.write_str_value('status', self.status)


Prompt:
Implement the Python class `MovingAverage` described below.

Class description:
Implement the MovingAverage class.

Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float

Implement the Python class `MovingAverage` described below.

Class description:
Implement the MovingAverage class.

Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float

<|skeleton|>
class MovingAverage:

    def __init__(self, size):
        """Initialize your data structure here. :type size: int"""
        <|body_0|>

    def next(self, val):
        """:type val: int :rtype: float"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.d = deque()
        self.size = size
        self.presum = 0
<|end_body_0|>

<|body_start_1|>
        if len(self.d) < self.size:
            self.d.append(val)
            self.presum = self.presum + val
            return self.presum / len(self.d)
        else:
            pop = self.d.popleft()
            self.presum = self.presum - pop
            self.d.append(val)
            self.presum = self.presum + val
            return self.presum / len(self.d)
<|end_body_1|>

Solution:
class MovingAverage:
    def __init__(self, size):
        """Initialize your data structure here. :type size: int"""
        self.d = deque()
        self.size = size
        self.presum = 0

    def next(self, val):
        """:type val: int :rtype: float"""
        if len(self.d) < self.size:
            self.d.append(val)
            self.presum = self.presum + val
            return self.presum / len(self.d)
        else:
            pop = self.d.popleft()
            self.presum = self.presum - pop
            self.d.append(val)
            self.presum = self.presum + val
            return self.presum / len(self.d)


isSameTree

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isSameTree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool
- def isSubtree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isSameTree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool
- def isSubtree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool

<|skeleton|>
class Solution:

    def isSameTree(self, s, t):
        """:type s: TreeNode :type t: TreeNode :rtype: bool"""
        <|body_0|>

    def isSubtree(self, s, t):
        """:type s: TreeNode :type t: TreeNode :rtype: bool"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if s is None:
            return t is None
        if t is None:
            return False
        if s.val != t.val:
            return False
        return self.isSameTree(s.left, t.left) and self.isSameTree(s.right, t.right)
<|end_body_0|>

<|body_start_1|>
        if s is None:
            return t is None
        if t is None:
            return False
        if s.val == t.val:
            return self.isSameTree(s, t) or self.isSubtree(s.left, t) or self.isSubtree(s.right, t)
        else:
            return self.isSubtree(s.left, t) or self.isSubtree(s.right, t)
<|end_body_1|>

Solution:
class Solution:
    def isSameTree(self, s, t):
        """:type s: TreeNode :type t: TreeNode :rtype: bool"""
        if s is None:
            return t is None
        if t is None:
            return False
        if s.val != t.val:
            return False
        return self.isSameTree(s.left, t.left) and self.isSameTree(s.right, t.right)

    def isSubtree(self, s, t):
        """:type s: TreeNode :type t: TreeNode :rtype: bool"""
        if s is None:
            return t is None
        if t is None:
            return False
        if s.val == t.val:
            return self.isSameTree(s, t) or self.isSubtree(s.left, t) or self.isSubtree(s.right, t)
        else:
            return self.isSubtree(s.left, t) or self.isSubtree(s.right, t)

Python Solutions/572 Subtree of Another Tree.py


post

Prompt:
Implement the Python class `ModelTrainData` described below.

Class description:
Implement the ModelTrainData class.

Method signatures and docstrings:
- def get(self, request): Return all data from ModelTrainData :param request: :return:
- def post(self, request): Create record in model_train_data :param request: :return: Record if created, otherwise error

Implement the Python class `ModelTrainData` described below.

Class description:
Implement the ModelTrainData class.

Method signatures and docstrings:
- def get(self, request): Return all data from ModelTrainData :param request: :return:
- def post(self, request): Create record in model_train_data :param request: :return: Record if created, otherwise error

<|skeleton|>
class ModelTrainData:

    def get(self, request):
        """Return all data from ModelTrainData :param request: :return:"""
        <|body_0|>

    def post(self, request):
        """Create record in model_train_data :param request: :return: Record if created, otherwise error"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = queries.get_all_model_train_data(reverse=True)
        paginator = Paginator(data, 10)
        page_number = request.GET.get('page')
        page_obj = paginator.get_page(page_number)
        return render(request, 'data_manager_app/modelTrainData.html', {'form': self.form_class, 'datas': page_obj, 'search_form': self.search_form})
<|end_body_0|>

<|body_start_1|>
        url_name = return_url_name(request.path)
        if not has_permission(request.user.role_id, url_name, request.method):
            return JsonResponse({'error': 'No permission'}, status=403)
        if request.is_ajax():
            form = self.form_class(request.POST)
            if form.is_valid():
                instance = form.save(commit=False)
                instance.added_by = request.user
                instance.save()
                ser_instance = serializers.serialize('json', [instance])
                return JsonResponse({'Added data': ser_instance}, status=200)
            else:
                str_error = ''
                for field, errors in form.errors.items():
                    str_error += field + ': '
                    for error in errors:
                        str_error += error + ', '
                return JsonResponse({'error': str_error[:-2]}, status=400)
        else:
            return JsonResponse({'Error': 'Something when terribly wrong'}, status=400)
<|end_body_1|>

Solution:
class ModelTrainData:
    def get(self, request):
        """Return all data from ModelTrainData :param request: :return:"""
        data = queries.get_all_model_train_data(reverse=True)
        paginator = Paginator(data, 10)
        page_number = request.GET.get('page')
        page_obj = paginator.get_page(page_number)
        return render(request, 'data_manager_app/modelTrainData.html', {'form': self.form_class, 'datas': page_obj, 'search_form': self.search_form})

    def post(self, request):
        """Create record in model_train_data :param request: :return: Record if created, otherwise error"""
        url_name = return_url_name(request.path)
        if not has_permission(request.user.role_id, url_name, request.method):
            return JsonResponse({'error': 'No permission'}, status=403)
        if request.is_ajax():
            form = self.form_class(request.POST)
            if form.is_valid():
                instance = form.save(commit=False)
                instance.added_by = request.user
                instance.save()
                ser_instance = serializers.serialize('json', [instance])
                return JsonResponse({'Added data': ser_instance}, status=200)
            else:
                str_error = ''
                for field, errors in form.errors.items():
                    str_error += field + ': '
                    for error in errors:
                        str_error += error + ', '
                return JsonResponse({'error': str_error[:-2]}, status=400)
        else:
            return JsonResponse({'Error': 'Something when terribly wrong'}, status=400)


Prompt:
Implement the Python class `RecommendEngine` described below.

Class description:
Implement the RecommendEngine class.

Method signatures and docstrings:
- def __init__(self, user_id, n_article=3): :param user_id: 用户ID :param n_article:int, 推荐n_article新闻
- def _get_user_action(self): 从msyql获取用户的用户点击行为 :return:
- def _recall(self): 召回模块 :return:
- def _rank(self): 排序模块 :return:
- def recommend_news(self): 给用户推荐新闻 :return:

Implement the Python class `RecommendEngine` described below.

Class description:
Implement the RecommendEngine class.

Method signatures and docstrings:
- def __init__(self, user_id, n_article=3): :param user_id: 用户ID :param n_article:int, 推荐n_article新闻
- def _get_user_action(self): 从msyql获取用户的用户点击行为 :return:
- def _recall(self): 召回模块 :return:
- def _rank(self): 排序模块 :return:
- def recommend_news(self): 给用户推荐新闻 :return:

<|skeleton|>
class RecommendEngine:

    def __init__(self, user_id, n_article=3):
        """:param user_id: 用户ID :param n_article:int, 推荐n_article新闻"""
        <|body_0|>

    def _get_user_action(self):
        """从msyql获取用户的用户点击行为 :return:"""
        <|body_1|>

    def _recall(self):
        """召回模块 :return:"""
        <|body_2|>

    def _rank(self):
        """排序模块 :return:"""
        <|body_3|>

    def recommend_news(self):
        """给用户推荐新闻 :return:"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.user_id = str(user_id)
        self.click_data = None
        self.recall_data = []
        self.recommend_newsid = []
        self.n_article = n_article
<|end_body_0|>

<|body_start_1|>
        sql = 'select newsid from user_click where userid=%s limit 1 ' % self.user_id
        for item1 in MYSQL_CLIENT.execute_query(sql):
            newsid = item1['newsid']
            sql = 'select content from article where newsid=%s;' % newsid
            for item2 in MYSQL_CLIENT.execute_query(sql):
                self.click_data = (item1['newsid'], item2['content'])
<|end_body_1|>

<|body_start_2|>
        mysql_of_actions = 'user_click'
        user_news = get_click_action(mysql_of_actions)
        data = item_cf_recommend(item_similarity(user_news), [self.user_id], user_news)
        for newsid in data[self.user_id]:
            sql = 'select content from article where newsid=%s;' % newsid
            for item in MYSQL_CLIENT.execute_query(sql):
                self.recall_data.append({'newsid': newsid, 'content': item['content']})
<|end_body_2|>

<|body_start_3|>
        score_list = []
        for recall_data in self.recall_data:
            sim = NewsSimLda.calc_similar(recall_data['content'], self.click_data[-1])
            score_list.append((recall_data['newsid'], sim))
        score_list.sort(key=lambda x: x[-1], reverse=True)
        self.recommend_newsid = [newsid for newsid, _ in score_list[0:self.n_article]]
<|end_body_3|>

<|body_start_4|>
        self._get_user_action()
        self._recall()
        self._rank()
<|end_body_4|>

Solution:
class RecommendEngine:
    def __init__(self, user_id, n_article=3):
        """:param user_id: 用户ID :param n_article:int, 推荐n_article新闻"""
        self.user_id = str(user_id)
        self.click_data = None
        self.recall_data = []
        self.recommend_newsid = []
        self.n_article = n_article

    def _get_user_action(self):
        """从msyql获取用户的用户点击行为 :return:"""
        sql = 'select newsid from user_click where userid=%s limit 1 ' % self.user_id
        for item1 in MYSQL_CLIENT.execute_query(sql):
            newsid = item1['newsid']
            sql = 'select content from article where newsid=%s;' % newsid
            for item2 in MYSQL_CLIENT.execute_query(sql):
                self.click_data = (item1['newsid'], item2['content'])

    def _recall(self):
        """召回模块 :return:"""
        mysql_of_actions = 'user_click'
        user_news = get_click_action(mysql_of_actions)
        data = item_cf_recommend(item_similarity(user_news), [self.user_id], user_news)
        for newsid in data[self.user_id]:
            sql = 'select content from article where newsid=%s;' % newsid
            for item in MYSQL_CLIENT.execute_query(sql):
                self.recall_data.append({'newsid': newsid, 'content': item['content']})

    def _rank(self):
        """排序模块 :return:"""
        score_list = []
        for recall_data in self.recall_data:
            sim = NewsSimLda.calc_similar(recall_data['content'], self.click_data[-1])
            score_list.append((recall_data['newsid'], sim))
        score_list.sort(key=lambda x: x[-1], reverse=True)
        self.recommend_newsid = [newsid for newsid, _ in score_list[0:self.n_article]]

    def recommend_news(self):
        """给用户推荐新闻 :return:"""
        self._get_user_action()
        self._recall()
        self._rank()


Prompt:
Implement the Python class `RecordPermission` described below.

Class description:
Record permission. - Create action given to librarian, admin and specified users. - Read access given to everyone with possibility to hide. - Delete access to admin and specified users.

Method signatures and docstrings:
- def __init__(self, record, action): Constructor.
- def collect_needs(self): Collect permission policy per action.
- def read_permissions(self): Define read permission policy per record.
- def record_explicit_restrictions(self): Return the list of user ids/roles allowed for the given action.
- def record_needs(self): Create needs of the record.
- def is_public(self): Check if the record is fully public. Explicit permission = `_access` field Implicit permission = `restricted` field Explicit, when defined, takes precedence over implicit which is ignored. The record is public when `_access.read` is not defined and `restricted` is False.

Implement the Python class `RecordPermission` described below.

Class description:
Record permission. - Create action given to librarian, admin and specified users. - Read access given to everyone with possibility to hide. - Delete access to admin and specified users.

Method signatures and docstrings:
- def __init__(self, record, action): Constructor.
- def collect_needs(self): Collect permission policy per action.
- def read_permissions(self): Define read permission policy per record.
- def record_explicit_restrictions(self): Return the list of user ids/roles allowed for the given action.
- def record_needs(self): Create needs of the record.
- def is_public(self): Check if the record is fully public. Explicit permission = `_access` field Implicit permission = `restricted` field Explicit, when defined, takes precedence over implicit which is ignored. The record is public when `_access.read` is not defined and `restricted` is False.

<|skeleton|>
class RecordPermission:
    """Record permission. - Create action given to librarian, admin and specified users. - Read access given to everyone with possibility to hide. - Delete access to admin and specified users."""

    def __init__(self, record, action):
        """Constructor."""
        <|body_0|>

    def collect_needs(self):
        """Collect permission policy per action."""
        <|body_1|>

    def read_permissions(self):
        """Define read permission policy per record."""
        <|body_2|>

    def record_explicit_restrictions(self):
        """Return the list of user ids/roles allowed for the given action."""
        <|body_3|>

    def record_needs(self):
        """Create needs of the record."""
        <|body_4|>

    def is_public(self):
        """Check if the record is fully public. Explicit permission = `_access` field Implicit permission = `restricted` field Explicit, when defined, takes precedence over implicit which is ignored. The record is public when `_access.read` is not defined and `restricted` is False."""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        self.record = record
        self.current_action = action
        record_needs = self.collect_needs()
        super().__init__(*record_needs)
<|end_body_0|>

<|body_start_1|>
        if self.current_action == 'read':
            return self.read_permissions()
        elif self.current_action == 'create':
            return [create_records_action, backoffice_access_action]
        elif self.current_action == 'update':
            return self.record_needs() + [backoffice_access_action]
        else:
            return self.record_needs()
<|end_body_1|>

<|body_start_2|>
        if self.is_public():
            return [any_user]
        else:
            return self.record_needs() + [backoffice_access_action]
<|end_body_2|>

<|body_start_3|>
        if current_app.config.get('ILS_RECORDS_EXPLICIT_PERMISSIONS_ENABLED'):
            return self.record.get('_access', {}).get(self.current_action, [])
        return []
<|end_body_3|>

<|body_start_4|>
        needs = []
        for access_entity in self.record_explicit_restrictions():
            try:
                if isinstance(access_entity, string_types):
                    needs.append(UserNeed(int(access_entity)))
                elif isinstance(access_entity, int):
                    needs.append(UserNeed(access_entity))
            except ValueError:
                needs.append(RoleNeed(access_entity.lower()))
        return needs
<|end_body_4|>

<|body_start_5|>
        has_explicit_perm = current_app.config.get('ILS_RECORDS_EXPLICIT_PERMISSIONS_ENABLED') and self.record.get('_access', {}).get('read', [])
        if not has_explicit_perm:
            return self.record.get('restricted', False) is False
        return False
<|end_body_5|>

Solution:
class RecordPermission:
    """Record permission. - Create action given to librarian, admin and specified users. - Read access given to everyone with possibility to hide. - Delete access to admin and specified users."""

    def __init__(self, record, action):
        """Constructor."""
        self.record = record
        self.current_action = action
        record_needs = self.collect_needs()
        super().__init__(*record_needs)

    def collect_needs(self):
        """Collect permission policy per action."""
        if self.current_action == 'read':
            return self.read_permissions()
        elif self.current_action == 'create':
            return [create_records_action, backoffice_access_action]
        elif self.current_action == 'update':
            return self.record_needs() + [backoffice_access_action]
        else:
            return self.record_needs()

    def read_permissions(self):
        """Define read permission policy per record."""
        if self.is_public():
            return [any_user]
        else:
            return self.record_needs() + [backoffice_access_action]

    def record_explicit_restrictions(self):
        """Return the list of user ids/roles allowed for the given action."""
        if current_app.config.get('ILS_RECORDS_EXPLICIT_PERMISSIONS_ENABLED'):
            return self.record.get('_access', {}).get(self.current_action, [])
        return []

    def record_needs(self):
        """Create needs of the record."""
        needs = []
        for access_entity in self.record_explicit_restrictions():
            try:
                if isinstance(access_entity, string_types):
                    needs.append(UserNeed(int(access_entity)))
                elif isinstance(access_entity, int):
                    needs.append(UserNeed(access_entity))
            except ValueError:
                needs.append(RoleNeed(access_entity.lower()))
        return needs

    def is_public(self):
        """Check if the record is fully public. Explicit permission = `_access` field Implicit permission = `restricted` field Explicit, when defined, takes precedence over implicit which is ignored. The record is public when `_access.read` is not defined and `restricted` is False."""
        has_explicit_perm = current_app.config.get('ILS_RECORDS_EXPLICIT_PERMISSIONS_ENABLED') and self.record.get('_access', {}).get('read', [])
        if not has_explicit_perm:
            return self.record.get('restricted', False) is False
        return False


verify

Prompt:
Implement the Python class `FaceVerification` described below.

Class description:
FaceVerification class

Method signatures and docstrings:
- def __init__(self, model_path, database, identities): Class constructor Argumetns: - model_path is the path to where the face verification embedding model is stored - database is a numpy.ndarray of shape (d, e) containing all the face embeddings in the database * d is the number of images in the database * e is the dimensionality of the embedding - identities is a list of length d containing the identities corresponding to the embeddings in database
- def embedding(self, images): Public instance method that calculates the face embedding of images Arguments: - images is a numpy.ndarray of shape (i, n, n, 3) containing the aligned images * i is the number of images * n is the size of the aligned images Returns: A numpy.ndarray of shape (i, e) containing the embeddings where e is the dimensionality of the embeddings
- def verify(self, image, tau=0.5): Public instance method Arguments: - image is a numpy.ndarray of shape (n, n, 3) containing the aligned image of the face to be verify * n is the shape of the aligned image - tau is the maximum euclidean distance used for verification Returns: (identity, distance), or (None, None) on failure - identity is a string containing the identity of the verified face - distance is the euclidean distance between the verified face embedding and the identified database embedding

Implement the Python class `FaceVerification` described below.

Class description:
FaceVerification class

Method signatures and docstrings:
- def __init__(self, model_path, database, identities): Class constructor Argumetns: - model_path is the path to where the face verification embedding model is stored - database is a numpy.ndarray of shape (d, e) containing all the face embeddings in the database * d is the number of images in the database * e is the dimensionality of the embedding - identities is a list of length d containing the identities corresponding to the embeddings in database
- def embedding(self, images): Public instance method that calculates the face embedding of images Arguments: - images is a numpy.ndarray of shape (i, n, n, 3) containing the aligned images * i is the number of images * n is the size of the aligned images Returns: A numpy.ndarray of shape (i, e) containing the embeddings where e is the dimensionality of the embeddings
- def verify(self, image, tau=0.5): Public instance method Arguments: - image is a numpy.ndarray of shape (n, n, 3) containing the aligned image of the face to be verify * n is the shape of the aligned image - tau is the maximum euclidean distance used for verification Returns: (identity, distance), or (None, None) on failure - identity is a string containing the identity of the verified face - distance is the euclidean distance between the verified face embedding and the identified database embedding

<|skeleton|>
class FaceVerification:
    """FaceVerification class"""

    def __init__(self, model_path, database, identities):
        """Class constructor Argumetns: - model_path is the path to where the face verification embedding model is stored - database is a numpy.ndarray of shape (d, e) containing all the face embeddings in the database * d is the number of images in the database * e is the dimensionality of the embedding - identities is a list of length d containing the identities corresponding to the embeddings in database"""
        <|body_0|>

    def embedding(self, images):
        """Public instance method that calculates the face embedding of images Arguments: - images is a numpy.ndarray of shape (i, n, n, 3) containing the aligned images * i is the number of images * n is the size of the aligned images Returns: A numpy.ndarray of shape (i, e) containing the embeddings where e is the dimensionality of the embeddings"""
        <|body_1|>

    def verify(self, image, tau=0.5):
        """Public instance method Arguments: - image is a numpy.ndarray of shape (n, n, 3) containing the aligned image of the face to be verify * n is the shape of the aligned image - tau is the maximum euclidean distance used for verification Returns: (identity, distance), or (None, None) on failure - identity is a string containing the identity of the verified face - distance is the euclidean distance between the verified face embedding and the identified database embedding"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        with tf.keras.utils.CustomObjectScope({'tf': tf}):
            self.model = tf.keras.models.load_model(model)
        self.database = database
        self.identities = identities
<|end_body_0|>

<|body_start_1|>
        em = np.zeros((images.shape[0], 128))
        for i, img in enumerate(images):
            em[i] = self.model.predict(np.expand_dims(img, axis=0))[0]
        return np.array(em)
<|end_body_1|>

<|body_start_2|>
        em = self.model.predict(np.expand_dims(image, axis=0))[0]
        dist = []
        lenght = len(self.identities)
        for i in range(lenght):
            dist.append(np.sum(np.square(em, self.database[i])))
        dist = np.array(dist)
        idx = np.argmin(dist)
        if dist[idx] < tau:
            return (self.identities[idx], dist[idx])
        else:
            return (None, None)
<|end_body_2|>

Solution:
class FaceVerification:
    """FaceVerification class"""

    def __init__(self, model_path, database, identities):
        """Class constructor Argumetns: - model_path is the path to where the face verification embedding model is stored - database is a numpy.ndarray of shape (d, e) containing all the face embeddings in the database * d is the number of images in the database * e is the dimensionality of the embedding - identities is a list of length d containing the identities corresponding to the embeddings in database"""
        with tf.keras.utils.CustomObjectScope({'tf': tf}):
            self.model = tf.keras.models.load_model(model)
        self.database = database
        self.identities = identities

    def embedding(self, images):
        """Public instance method that calculates the face embedding of images Arguments: - images is a numpy.ndarray of shape (i, n, n, 3) containing the aligned images * i is the number of images * n is the size of the aligned images Returns: A numpy.ndarray of shape (i, e) containing the embeddings where e is the dimensionality of the embeddings"""
        em = np.zeros((images.shape[0], 128))
        for i, img in enumerate(images):
            em[i] = self.model.predict(np.expand_dims(img, axis=0))[0]
        return np.array(em)

    def verify(self, image, tau=0.5):
        """Public instance method Arguments: - image is a numpy.ndarray of shape (n, n, 3) containing the aligned image of the face to be verify * n is the shape of the aligned image - tau is the maximum euclidean distance used for verification Returns: (identity, distance), or (None, None) on failure - identity is a string containing the identity of the verified face - distance is the euclidean distance between the verified face embedding and the identified database embedding"""
        em = self.model.predict(np.expand_dims(image, axis=0))[0]
        dist = []
        lenght = len(self.identities)
        for i in range(lenght):
            dist.append(np.sum(np.square(em, self.database[i])))
        dist = np.array(dist)
        idx = np.argmin(dist)
        if dist[idx] < tau:
            return (self.identities[idx], dist[idx])
        else:
            return (None, None)


keyframeextractor

遍历文件夹下所有的视频，进行关键帧提取 :return:

Prompt:
Implement the Python class `KeyFrameExtractor` described below.

Class description:
Implement the KeyFrameExtractor class.

Method signatures and docstrings:
- def __init__(self, videopath, savepath, resizeheight=240, resizewidth=320): 初始化方法 :param videopath: 视频输入路径 :param savepath: 关键帧保存路径 :param resizeheight: 重置视频的大小的高 :param resizewidth: 重置视频的大小的宽
- def keyframeextractor(self): 遍历文件夹下所有的视频，进行关键帧提取 :return:
- def __extractkeyframe(self, filepathandname): 提取关键帧

Implement the Python class `KeyFrameExtractor` described below.

Class description:
Implement the KeyFrameExtractor class.

Method signatures and docstrings:
- def __init__(self, videopath, savepath, resizeheight=240, resizewidth=320): 初始化方法 :param videopath: 视频输入路径 :param savepath: 关键帧保存路径 :param resizeheight: 重置视频的大小的高 :param resizewidth: 重置视频的大小的宽
- def keyframeextractor(self): 遍历文件夹下所有的视频，进行关键帧提取 :return:
- def __extractkeyframe(self, filepathandname): 提取关键帧

<|skeleton|>
class KeyFrameExtractor:

    def __init__(self, videopath, savepath, resizeheight=240, resizewidth=320):
        """初始化方法 :param videopath: 视频输入路径 :param savepath: 关键帧保存路径 :param resizeheight: 重置视频的大小的高 :param resizewidth: 重置视频的大小的宽"""
        <|body_0|>

    def keyframeextractor(self):
        """遍历文件夹下所有的视频，进行关键帧提取 :return:"""
        <|body_1|>

    def __extractkeyframe(self, filepathandname):
        """提取关键帧"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.__videopath = videopath
        self.__savepath = savepath
        self.__resizeheight = resizeheight
        self.__resizewidth = resizewidth
        self.__frames = []
<|end_body_0|>

<|body_start_1|>
        if os.path.isdir(self.__videopath):
            for dirpath, dirnames, filenames in os.walk(self.__videopath):
                for filename in filenames:
                    filepathandname = os.path.join(dirpath, filename)
                    self.__extractkeyframe(filepathandname)
<|end_body_1|>

<|body_start_2|>
        if os.path.exists(filepathandname):
            videocapture = cv2.VideoCapture(filepathandname)
            if videocapture.isOpened():
                wholeframenum = int(videocapture.get(cv2.CAP_PROP_FRAME_COUNT))
                if wholeframenum < 2:
                    print('the image you inputted has not enougth frames!')
                middleframenum = math.ceil(wholeframenum / 2)
                success, frame = videocapture.read()
                frame = cv2.resize(frame, (self.__resizewidth, self.__resizeheight))
                self.__frames = [frame]
                count = 0
                while success:
                    count += 1
                    success, frame = videocapture.read()
                    if success:
                        frame = cv2.resize(frame, (self.__resizewidth, self.__resizeheight))
                        if count == middleframenum:
                            self.__frames.append(frame)
                        elif count == wholeframenum - 1:
                            self.__frames.append(frame)
                if self.__frames is not None:
                    for keyindex in range(len(self.__frames)):
                        currentframe = self.__frames[keyindex]
                        if os.path.isdir(self.__savepath):
                            framename = os.path.abspath(self.__savepath) + os.sep + os.path.basename(os.path.dirname(filepathandname)) + '_' + os.path.basename(filepathandname).split('.')[0] + '_keyFrame_' + str(keyindex) + '.jpg'
                            cv2.imwrite(framename, currentframe)
                        else:
                            print(' Please input the correct save path!')
        else:
            print(' you inputted file is not existed!')
<|end_body_2|>

Solution:
class KeyFrameExtractor:
    def __init__(self, videopath, savepath, resizeheight=240, resizewidth=320):
        """初始化方法 :param videopath: 视频输入路径 :param savepath: 关键帧保存路径 :param resizeheight: 重置视频的大小的高 :param resizewidth: 重置视频的大小的宽"""
        self.__videopath = videopath
        self.__savepath = savepath
        self.__resizeheight = resizeheight
        self.__resizewidth = resizewidth
        self.__frames = []

    def keyframeextractor(self):
        """遍历文件夹下所有的视频，进行关键帧提取 :return:"""
        if os.path.isdir(self.__videopath):
            for dirpath, dirnames, filenames in os.walk(self.__videopath):
                for filename in filenames:
                    filepathandname = os.path.join(dirpath, filename)
                    self.__extractkeyframe(filepathandname)

    def __extractkeyframe(self, filepathandname):
        """提取关键帧"""
        if os.path.exists(filepathandname):
            videocapture = cv2.VideoCapture(filepathandname)
            if videocapture.isOpened():
                wholeframenum = int(videocapture.get(cv2.CAP_PROP_FRAME_COUNT))
                if wholeframenum < 2:
                    print('the image you inputted has not enougth frames!')
                middleframenum = math.ceil(wholeframenum / 2)
                success, frame = videocapture.read()
                frame = cv2.resize(frame, (self.__resizewidth, self.__resizeheight))
                self.__frames = [frame]
                count = 0
                while success:
                    count += 1
                    success, frame = videocapture.read()
                    if success:
                        frame = cv2.resize(frame, (self.__resizewidth, self.__resizeheight))
                        if count == middleframenum:
                            self.__frames.append(frame)
                        elif count == wholeframenum - 1:
                            self.__frames.append(frame)
                if self.__frames is not None:
                    for keyindex in range(len(self.__frames)):
                        currentframe = self.__frames[keyindex]
                        if os.path.isdir(self.__savepath):
                            framename = os.path.abspath(self.__savepath) + os.sep + os.path.basename(os.path.dirname(filepathandname)) + '_' + os.path.basename(filepathandname).split('.')[0] + '_keyFrame_' + str(keyindex) + '.jpg'
                            cv2.imwrite(framename, currentframe)
                        else:
                            print(' Please input the correct save path!')
        else:
            print(' you inputted file is not existed!')


Prompt:
Implement the Python class `TetrisGraphicsBinder` described below.

Class description:
Binds an external player (or set of command) to the tetris graphics class

Method signatures and docstrings:
- def __init__(self, player, graphical_params, game_params): Ctor Parameters ---------- player: function A function that takes the current board as parameters and returns the corresponding action (Controls) to perform graphical_params: GraphicalParametersEu The display parameters for the game. See src.tetris.graphics.GraphicalParameters for more information about those parameters. game_params: GameParameters Parameters for the game. See class documentation for more details
- def on_update(self, events=''): Overrides on_update method, ignoring second parameter Parameters ---------- events: any Not used, just here for the sake of compatibility with main class

Implement the Python class `TetrisGraphicsBinder` described below.

Class description:
Binds an external player (or set of command) to the tetris graphics class

Method signatures and docstrings:
- def __init__(self, player, graphical_params, game_params): Ctor Parameters ---------- player: function A function that takes the current board as parameters and returns the corresponding action (Controls) to perform graphical_params: GraphicalParametersEu The display parameters for the game. See src.tetris.graphics.GraphicalParameters for more information about those parameters. game_params: GameParameters Parameters for the game. See class documentation for more details
- def on_update(self, events=''): Overrides on_update method, ignoring second parameter Parameters ---------- events: any Not used, just here for the sake of compatibility with main class

<|skeleton|>
class TetrisGraphicsBinder:
    """Binds an external player (or set of command) to the tetris graphics class"""

    def __init__(self, player, graphical_params, game_params):
        """Ctor Parameters ---------- player: function A function that takes the current board as parameters and returns the corresponding action (Controls) to perform graphical_params: GraphicalParametersEu The display parameters for the game. See src.tetris.graphics.GraphicalParameters for more information about those parameters. game_params: GameParameters Parameters for the game. See class documentation for more details"""
        <|body_0|>

    def on_update(self, events=''):
        """Overrides on_update method, ignoring second parameter Parameters ---------- events: any Not used, just here for the sake of compatibility with main class"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(graphical_params, game_params)
        self._player = player
        self._mvt = []
<|end_body_0|>

<|body_start_1|>
        if len(self._mvt) == 0:
            mvts = self._player(self.get_current_game_state())
            if type(mvts) is Controls:
                self._mvt = [mvts]
            else:
                self._mvt = mvts
        if len(self._mvt) != 0:
            mvt = self._mvt.pop(0)
        else:
            mvt = Controls.NOTHING
        self.tick(mvt)
<|end_body_1|>

Solution:
class TetrisGraphicsBinder:
    """Binds an external player (or set of command) to the tetris graphics class"""

    def __init__(self, player, graphical_params, game_params):
        """Ctor Parameters ---------- player: function A function that takes the current board as parameters and returns the corresponding action (Controls) to perform graphical_params: GraphicalParametersEu The display parameters for the game. See src.tetris.graphics.GraphicalParameters for more information about those parameters. game_params: GameParameters Parameters for the game. See class documentation for more details"""
        super().__init__(graphical_params, game_params)
        self._player = player
        self._mvt = []

    def on_update(self, events=''):
        """Overrides on_update method, ignoring second parameter Parameters ---------- events: any Not used, just here for the sake of compatibility with main class"""
        if len(self._mvt) == 0:
            mvts = self._player(self.get_current_game_state())
            if type(mvts) is Controls:
                self._mvt = [mvts]
            else:
                self._mvt = mvts
        if len(self._mvt) != 0:
            mvt = self._mvt.pop(0)
        else:
            mvt = Controls.NOTHING
        self.tick(mvt)


Prompt:
Implement the Python class `EqualSumCombinations` described below.

Class description:
Class to generate all combinations of non-negative integers that have equal sum.

Method signatures and docstrings:
- def __init__(self, sum_, size): Constructor to initialize the variables. Parameters ---------- sum_ : int Desired sum (must be greater than 0). size : int Number of integers (must be greater than 1). Raises ------ ValueError If sum is less than 2.
- def get_combinations(self, sum, n): A recursive function to generate the list of all non-negative integer combinations of a given size that have a given sum. Parameters ---------- sum : int Desired sum. n : int Desired size. Returns ------- type : array-like A list containing the lists of combinations.

Implement the Python class `EqualSumCombinations` described below.

Class description:
Class to generate all combinations of non-negative integers that have equal sum.

Method signatures and docstrings:
- def __init__(self, sum_, size): Constructor to initialize the variables. Parameters ---------- sum_ : int Desired sum (must be greater than 0). size : int Number of integers (must be greater than 1). Raises ------ ValueError If sum is less than 2.
- def get_combinations(self, sum, n): A recursive function to generate the list of all non-negative integer combinations of a given size that have a given sum. Parameters ---------- sum : int Desired sum. n : int Desired size. Returns ------- type : array-like A list containing the lists of combinations.

<|skeleton|>
class EqualSumCombinations:
    """Class to generate all combinations of non-negative integers that have equal sum."""

    def __init__(self, sum_, size):
        """Constructor to initialize the variables. Parameters ---------- sum_ : int Desired sum (must be greater than 0). size : int Number of integers (must be greater than 1). Raises ------ ValueError If sum is less than 2."""
        <|body_0|>

    def get_combinations(self, sum, n):
        """A recursive function to generate the list of all non-negative integer combinations of a given size that have a given sum. Parameters ---------- sum : int Desired sum. n : int Desired size. Returns ------- type : array-like A list containing the lists of combinations."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if sum_ <= 0:
            raise ValueError('Sum must be positive.')
        if size < 2:
            raise ValueError('Size must be greater than 1.')
        self.dp = np.zeros((sum_ + 1, size + 1), dtype=object)
        self.combs = self.get_combinations(sum_, size)
<|end_body_0|>

<|body_start_1|>
        if self.dp[sum][n]:
            return self.dp[sum][n]
        tmp_list = []
        if n == 1:
            tmp_list = [[sum]]
        elif sum == 0:
            tmp_list = [[0] * n]
        else:
            for i in range(sum, -1, -1):
                for l in self.get_combinations(sum - i, n - 1):
                    tmp_list.append([i] + l)
        self.dp[sum][n] = tmp_list
        return tmp_list
<|end_body_1|>

Solution:
class EqualSumCombinations:
    """Class to generate all combinations of non-negative integers that have equal sum."""

    def __init__(self, sum_, size):
        """Constructor to initialize the variables. Parameters ---------- sum_ : int Desired sum (must be greater than 0). size : int Number of integers (must be greater than 1). Raises ------ ValueError If sum is less than 2."""
        if sum_ <= 0:
            raise ValueError('Sum must be positive.')
        if size < 2:
            raise ValueError('Size must be greater than 1.')
        self.dp = np.zeros((sum_ + 1, size + 1), dtype=object)
        self.combs = self.get_combinations(sum_, size)

    def get_combinations(self, sum, n):
        """A recursive function to generate the list of all non-negative integer combinations of a given size that have a given sum. Parameters ---------- sum : int Desired sum. n : int Desired size. Returns ------- type : array-like A list containing the lists of combinations."""
        if self.dp[sum][n]:
            return self.dp[sum][n]
        tmp_list = []
        if n == 1:
            tmp_list = [[sum]]
        elif sum == 0:
            tmp_list = [[0] * n]
        else:
            for i in range(sum, -1, -1):
                for l in self.get_combinations(sum - i, n - 1):
                    tmp_list.append([i] + l)
        self.dp[sum][n] = tmp_list
        return tmp_list


Prompt:
Implement the Python class `Rule7Model` described below.

Class description:
Implementation of the 'Rule7' model. TODO: type model description here. Attributes: public_ip (string): The IP address that will be used to access the internal resource from the WAN uplink (Uplink1Enum): The physical WAN interface on which the traffic will arrive ('internet1' or, if available, 'internet2') port_rules (list of PortRuleModel): An array of associated forwarding rules

Method signatures and docstrings:
- def __init__(self, public_ip=None, uplink=None, port_rules=None): Constructor for the Rule7Model class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

Implement the Python class `Rule7Model` described below.

Class description:
Implementation of the 'Rule7' model. TODO: type model description here. Attributes: public_ip (string): The IP address that will be used to access the internal resource from the WAN uplink (Uplink1Enum): The physical WAN interface on which the traffic will arrive ('internet1' or, if available, 'internet2') port_rules (list of PortRuleModel): An array of associated forwarding rules

Method signatures and docstrings:
- def __init__(self, public_ip=None, uplink=None, port_rules=None): Constructor for the Rule7Model class
- def from_dictionary(cls, dictionary): Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class.

<|skeleton|>
class Rule7Model:
    """Implementation of the 'Rule7' model. TODO: type model description here. Attributes: public_ip (string): The IP address that will be used to access the internal resource from the WAN uplink (Uplink1Enum): The physical WAN interface on which the traffic will arrive ('internet1' or, if available, 'internet2') port_rules (list of PortRuleModel): An array of associated forwarding rules"""

    def __init__(self, public_ip=None, uplink=None, port_rules=None):
        """Constructor for the Rule7Model class"""
        <|body_0|>

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.public_ip = public_ip
        self.uplink = uplink
        self.port_rules = port_rules
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        public_ip = dictionary.get('publicIp')
        uplink = dictionary.get('uplink')
        port_rules = None
        if dictionary.get('portRules') != None:
            port_rules = list()
            for structure in dictionary.get('portRules'):
                port_rules.append(meraki_sdk.models.port_rule_model.PortRuleModel.from_dictionary(structure))
        return cls(public_ip, uplink, port_rules)
<|end_body_1|>

Solution:
class Rule7Model:
    """Implementation of the 'Rule7' model. TODO: type model description here. Attributes: public_ip (string): The IP address that will be used to access the internal resource from the WAN uplink (Uplink1Enum): The physical WAN interface on which the traffic will arrive ('internet1' or, if available, 'internet2') port_rules (list of PortRuleModel): An array of associated forwarding rules"""

    def __init__(self, public_ip=None, uplink=None, port_rules=None):
        """Constructor for the Rule7Model class"""
        self.public_ip = public_ip
        self.uplink = uplink
        self.port_rules = port_rules

    def from_dictionary(cls, dictionary):
        """Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object as obtained from the deserialization of the server's response. The keys MUST match property names in the API description. Returns: object: An instance of this structure class."""
        if dictionary is None:
            return None
        public_ip = dictionary.get('publicIp')
        uplink = dictionary.get('uplink')
        port_rules = None
        if dictionary.get('portRules') != None:
            port_rules = list()
            for structure in dictionary.get('portRules'):
                port_rules.append(meraki_sdk.models.port_rule_model.PortRuleModel.from_dictionary(structure))
        return cls(public_ip, uplink, port_rules)


Prompt:
Implement the Python class `IntermRep` described below.

Class description:
Intermediate representation for all kinds of brain data. Notes ----- notes here...

Method signatures and docstrings:
- def __init__(self, arr_voxels, arr_xyz, subj_name=None, cls=None): The intermediate representation is based on two arrays. arr_voxels: containing a list of voxels arr_xyz: containing the x,y,z coordinates for each voxel.
- def load_from_file(cls, file_voxels, file_xyz): Creates an IntermRep object from a file: @file_voxels: A file with the time series. Rows are voxels, and columns are time points. @file_xyz: files are the corresponding x, y, z coordinates of each voxel.

Implement the Python class `IntermRep` described below.

Class description:
Intermediate representation for all kinds of brain data. Notes ----- notes here...

Method signatures and docstrings:
- def __init__(self, arr_voxels, arr_xyz, subj_name=None, cls=None): The intermediate representation is based on two arrays. arr_voxels: containing a list of voxels arr_xyz: containing the x,y,z coordinates for each voxel.
- def load_from_file(cls, file_voxels, file_xyz): Creates an IntermRep object from a file: @file_voxels: A file with the time series. Rows are voxels, and columns are time points. @file_xyz: files are the corresponding x, y, z coordinates of each voxel.

<|skeleton|>
class IntermRep:
    """Intermediate representation for all kinds of brain data. Notes ----- notes here..."""

    def __init__(self, arr_voxels, arr_xyz, subj_name=None, cls=None):
        """The intermediate representation is based on two arrays. arr_voxels: containing a list of voxels arr_xyz: containing the x,y,z coordinates for each voxel."""
        <|body_0|>

    def load_from_file(cls, file_voxels, file_xyz):
        """Creates an IntermRep object from a file: @file_voxels: A file with the time series. Rows are voxels, and columns are time points. @file_xyz: files are the corresponding x, y, z coordinates of each voxel."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.arr_voxels = arr_voxels
        self.arr_xyz = arr_xyz
        self.subj_name = subj_name
        self.cls = cls
<|end_body_0|>

<|body_start_1|>
        arr_voxels = np.genfromtxt(file_voxels, dtype=np.float, delimiter=' ')
        arr_xyz = np.genfromtxt(file_xyz, dtype=np.float, delimiter=' ')
        return cls(arr_voxels, arr_xyz)
<|end_body_1|>

Solution:
class IntermRep:
    """Intermediate representation for all kinds of brain data. Notes ----- notes here..."""

    def __init__(self, arr_voxels, arr_xyz, subj_name=None, cls=None):
        """The intermediate representation is based on two arrays. arr_voxels: containing a list of voxels arr_xyz: containing the x,y,z coordinates for each voxel."""
        self.arr_voxels = arr_voxels
        self.arr_xyz = arr_xyz
        self.subj_name = subj_name
        self.cls = cls

    def load_from_file(cls, file_voxels, file_xyz):
        """Creates an IntermRep object from a file: @file_voxels: A file with the time series. Rows are voxels, and columns are time points. @file_xyz: files are the corresponding x, y, z coordinates of each voxel."""
        arr_voxels = np.genfromtxt(file_voxels, dtype=np.float, delimiter=' ')
        arr_xyz = np.genfromtxt(file_xyz, dtype=np.float, delimiter=' ')
        return cls(arr_voxels, arr_xyz)


ci

Prompt:
Implement the Python class `SEM` described below.

Class description:
Implement the SEM class.

Method signatures and docstrings:
- def __init__(self, y, x=None, match=None): Standard error of the mean (SEM) Parameters ---------- y : array (n, ...) Data, first dimension reflecting cases. x : Categorial Categorial predictor for using pooled variance. match : Categorial Within-subject SEM. Notes ----- See Loftus and Masson (1994).
- def ci(self, confidence): Confidence interval based on the inverse t-test Parameters ---------- confidence : scalar Confidence in the interval (i.e., .95 for 95% CI). Returns ------- ci : array [...] Confidence interval (i.e., the mean of y lies within m +- ci with the specified confidence). Notes ----- See `<http://en.wikipedia.org/wiki/Confidence_interval#Statistical_hypothesis_testing>`_

Implement the Python class `SEM` described below.

Class description:
Implement the SEM class.

Method signatures and docstrings:
- def __init__(self, y, x=None, match=None): Standard error of the mean (SEM) Parameters ---------- y : array (n, ...) Data, first dimension reflecting cases. x : Categorial Categorial predictor for using pooled variance. match : Categorial Within-subject SEM. Notes ----- See Loftus and Masson (1994).
- def ci(self, confidence): Confidence interval based on the inverse t-test Parameters ---------- confidence : scalar Confidence in the interval (i.e., .95 for 95% CI). Returns ------- ci : array [...] Confidence interval (i.e., the mean of y lies within m +- ci with the specified confidence). Notes ----- See `<http://en.wikipedia.org/wiki/Confidence_interval#Statistical_hypothesis_testing>`_

<|skeleton|>
class SEM:

    def __init__(self, y, x=None, match=None):
        """Standard error of the mean (SEM) Parameters ---------- y : array (n, ...) Data, first dimension reflecting cases. x : Categorial Categorial predictor for using pooled variance. match : Categorial Within-subject SEM. Notes ----- See Loftus and Masson (1994)."""
        <|body_0|>

    def ci(self, confidence):
        """Confidence interval based on the inverse t-test Parameters ---------- confidence : scalar Confidence in the interval (i.e., .95 for 95% CI). Returns ------- ci : array [...] Confidence interval (i.e., the mean of y lies within m +- ci with the specified confidence). Notes ----- See `<http://en.wikipedia.org/wiki/Confidence_interval#Statistical_hypothesis_testing>`_"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if x is None:
            n = len(y)
            model = match
        else:
            x = asfactor(x)
            n = x._cellsize()
            if isinstance(n, dict):
                raise NotImplementedError('SEM for unequal cell sizes')
            model = x if match is None else x + match
        sem = residual_mean_square(y, model)
        sem /= n
        np.sqrt(sem, sem)
        self.n = n
        self.model = model
        self.sem = sem
<|end_body_0|>

<|body_start_1|>
        if self.model is None:
            df = self.n - 1
        elif isinstance(self.model, Model):
            df = self.model.df_error
        else:
            df = Model(self.model).df_error
        return self.sem * scipy.stats.t.isf((1 - confidence) / 2, df)
<|end_body_1|>

Solution:
class SEM:
    def __init__(self, y, x=None, match=None):
        """Standard error of the mean (SEM) Parameters ---------- y : array (n, ...) Data, first dimension reflecting cases. x : Categorial Categorial predictor for using pooled variance. match : Categorial Within-subject SEM. Notes ----- See Loftus and Masson (1994)."""
        if x is None:
            n = len(y)
            model = match
        else:
            x = asfactor(x)
            n = x._cellsize()
            if isinstance(n, dict):
                raise NotImplementedError('SEM for unequal cell sizes')
            model = x if match is None else x + match
        sem = residual_mean_square(y, model)
        sem /= n
        np.sqrt(sem, sem)
        self.n = n
        self.model = model
        self.sem = sem

    def ci(self, confidence):
        """Confidence interval based on the inverse t-test Parameters ---------- confidence : scalar Confidence in the interval (i.e., .95 for 95% CI). Returns ------- ci : array [...] Confidence interval (i.e., the mean of y lies within m +- ci with the specified confidence). Notes ----- See `<http://en.wikipedia.org/wiki/Confidence_interval#Statistical_hypothesis_testing>`_"""
        if self.model is None:
            df = self.n - 1
        elif isinstance(self.model, Model):
            df = self.model.df_error
        else:
            df = Model(self.model).df_error
        return self.sem * scipy.stats.t.isf((1 - confidence) / 2, df)


Prompt:
Implement the Python class `test_lint_basic_constraints_not_critical` described below.

Class description:
Test lint_basic_constraints_not_critical.py

Method signatures and docstrings:
- def test_BasicConstNotCrit(self): Test BasicConstNotCrit
- def test_BasicConstCrit(self): Test lint_basic_constraints_critical.py

Implement the Python class `test_lint_basic_constraints_not_critical` described below.

Class description:
Test lint_basic_constraints_not_critical.py

Method signatures and docstrings:
- def test_BasicConstNotCrit(self): Test BasicConstNotCrit
- def test_BasicConstCrit(self): Test lint_basic_constraints_critical.py

<|skeleton|>
class test_lint_basic_constraints_not_critical:
    """Test lint_basic_constraints_not_critical.py"""

    def test_BasicConstNotCrit(self):
        """Test BasicConstNotCrit"""
        <|body_0|>

    def test_BasicConstCrit(self):
        """Test lint_basic_constraints_critical.py"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        certPath = '..\\testCerts\\caBasicConstNotCrit.pem'
        lint_basic_constraints_not_critical.init()
        with open(certPath, 'rb') as f:
            cert = x509.load_pem_x509_certificate(f.read(), default_backend())
            out = base.Lints['e_basic_constraints_not_critical'].Execute(cert)
            self.assertEqual(base.LintStatus.Error, out.Status)
<|end_body_0|>

<|body_start_1|>
        certPath = '..\\testCerts\\caBasicConstCrit.pem'
        lint_basic_constraints_not_critical.init()
        with open(certPath, 'rb') as f:
            cert = x509.load_pem_x509_certificate(f.read(), default_backend())
            out = base.Lints['e_basic_constraints_not_critical'].Execute(cert)
            self.assertEqual(base.LintStatus.Pass, out.Status)
<|end_body_1|>

Solution:
class test_lint_basic_constraints_not_critical:
    """Test lint_basic_constraints_not_critical.py"""

    def test_BasicConstNotCrit(self):
        """Test BasicConstNotCrit"""
        certPath = '..\\testCerts\\caBasicConstNotCrit.pem'
        lint_basic_constraints_not_critical.init()
        with open(certPath, 'rb') as f:
            cert = x509.load_pem_x509_certificate(f.read(), default_backend())
            out = base.Lints['e_basic_constraints_not_critical'].Execute(cert)
            self.assertEqual(base.LintStatus.Error, out.Status)

    def test_BasicConstCrit(self):
        """Test lint_basic_constraints_critical.py"""
        certPath = '..\\testCerts\\caBasicConstCrit.pem'
        lint_basic_constraints_not_critical.init()
        with open(certPath, 'rb') as f:
            cert = x509.load_pem_x509_certificate(f.read(), default_backend())
            out = base.Lints['e_basic_constraints_not_critical'].Execute(cert)
            self.assertEqual(base.LintStatus.Pass, out.Status)


Prompt:
Implement the Python class `Message` described below.

Class description:
Implement the Message class.

Method signatures and docstrings:
- def send_message(self, phone_num, code, templateId='141', time='五'): 发送短信验证码 :param phone_num: str 收件人手机号 :param code: str 6位随机激活码 :param templateId: str 榛子云短信平台模板id :param time: str 过期时间，仅用于显示 :return: json_str 短信发送结果
- def number_of_message(self): 查询剩余短语条数 返回结果是json格式的字符串, code: 查询状态，0为成功， data: 为剩余短信条数。 非0为查询失败，可从data中查看错误信息

Implement the Python class `Message` described below.

Class description:
Implement the Message class.

Method signatures and docstrings:
- def send_message(self, phone_num, code, templateId='141', time='五'): 发送短信验证码 :param phone_num: str 收件人手机号 :param code: str 6位随机激活码 :param templateId: str 榛子云短信平台模板id :param time: str 过期时间，仅用于显示 :return: json_str 短信发送结果
- def number_of_message(self): 查询剩余短语条数 返回结果是json格式的字符串, code: 查询状态，0为成功， data: 为剩余短信条数。 非0为查询失败，可从data中查看错误信息

<|skeleton|>
class Message:

    def send_message(self, phone_num, code, templateId='141', time='五'):
        """发送短信验证码 :param phone_num: str 收件人手机号 :param code: str 6位随机激活码 :param templateId: str 榛子云短信平台模板id :param time: str 过期时间，仅用于显示 :return: json_str 短信发送结果"""
        <|body_0|>

    def number_of_message(self):
        """查询剩余短语条数 返回结果是json格式的字符串, code: 查询状态，0为成功， data: 为剩余短信条数。 非0为查询失败，可从data中查看错误信息"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if phone_num and code:
            params = {}
            params['number'] = phone_num
            params['templateId'] = templateId
            params['templateParams'] = [code, time]
            json_str_data = self.client.send(params)
            return json_str_data
        else:
            raise Exception('手机号和验证码不能为空')
<|end_body_0|>

<|body_start_1|>
        result = json.loads(self.client.balance())
        code = result['code']
        data = result['data']
        if not code:
            print('短信剩余{}条'.format(data))
        else:
            print('错误代码: {}, {}'.format(code, data))
<|end_body_1|>

Solution:
class Message:
    def send_message(self, phone_num, code, templateId='141', time='五'):
        """发送短信验证码 :param phone_num: str 收件人手机号 :param code: str 6位随机激活码 :param templateId: str 榛子云短信平台模板id :param time: str 过期时间，仅用于显示 :return: json_str 短信发送结果"""
        if phone_num and code:
            params = {}
            params['number'] = phone_num
            params['templateId'] = templateId
            params['templateParams'] = [code, time]
            json_str_data = self.client.send(params)
            return json_str_data
        else:
            raise Exception('手机号和验证码不能为空')

    def number_of_message(self):
        """查询剩余短语条数 返回结果是json格式的字符串, code: 查询状态，0为成功， data: 为剩余短信条数。 非0为查询失败，可从data中查看错误信息"""
        result = json.loads(self.client.balance())
        code = result['code']
        data = result['data']
        if not code:
            print('短信剩余{}条'.format(data))
        else:
            print('错误代码: {}, {}'.format(code, data))


Prompt:
Implement the Python class `SSHGEComputingElement` described below.

Class description:
Implement the SSHGEComputingElement class.

Method signatures and docstrings:
- def __init__(self, ceUniqueID): Standard constructor.
- def _getJobOutputFiles(self, jobID): Get output file names for the specific CE

Implement the Python class `SSHGEComputingElement` described below.

Class description:
Implement the SSHGEComputingElement class.

Method signatures and docstrings:
- def __init__(self, ceUniqueID): Standard constructor.
- def _getJobOutputFiles(self, jobID): Get output file names for the specific CE

<|skeleton|>
class SSHGEComputingElement:

    def __init__(self, ceUniqueID):
        """Standard constructor."""
        <|body_0|>

    def _getJobOutputFiles(self, jobID):
        """Get output file names for the specific CE"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        SSHComputingElement.__init__(self, ceUniqueID)
        self.ceType = CE_NAME
        self.controlScript = 'sgece'
        self.mandatoryParameters = MANDATORY_PARAMETERS
<|end_body_0|>

<|body_start_1|>
        result = pfnparse(jobID)
        if not result['OK']:
            return result
        jobStamp = result['Value']['FileName']
        host = result['Value']['Host']
        output = '%s/DIRACPilot.o%s' % (self.batchOutput, jobStamp)
        error = '%s/DIRACPilot.e%s' % (self.batchError, jobStamp)
        return S_OK((jobStamp, host, output, error))
<|end_body_1|>

Solution:
class SSHGEComputingElement:
    def __init__(self, ceUniqueID):
        """Standard constructor."""
        SSHComputingElement.__init__(self, ceUniqueID)
        self.ceType = CE_NAME
        self.controlScript = 'sgece'
        self.mandatoryParameters = MANDATORY_PARAMETERS

    def _getJobOutputFiles(self, jobID):
        """Get output file names for the specific CE"""
        result = pfnparse(jobID)
        if not result['OK']:
            return result
        jobStamp = result['Value']['FileName']
        host = result['Value']['Host']
        output = '%s/DIRACPilot.o%s' % (self.batchOutput, jobStamp)
        error = '%s/DIRACPilot.e%s' % (self.batchError, jobStamp)
        return S_OK((jobStamp, host, output, error))


vk - экземпляр VkApi

check

Возвращает список событий (updates)

Prompt:
Implement the Python class `LP` described below.

Class description:
Implement the LP class.

Method signatures and docstrings:
- def __init__(self, vk, wait=25): vk - экземпляр VkApi
- def check(self): Возвращает список событий (updates)

Implement the Python class `LP` described below.

Class description:
Implement the LP class.

Method signatures and docstrings:
- def __init__(self, vk, wait=25): vk - экземпляр VkApi
- def check(self): Возвращает список событий (updates)

<|skeleton|>
class LP:

    def __init__(self, vk, wait=25):
        """vk - экземпляр VkApi"""
        <|body_0|>

    def check(self):
        """Возвращает список событий (updates)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.vk = vk
        data = vk('messages.getLongPollServer')
        if data.get('error'):
            if data['error']['error_code'] == 5:
                raise Exception('tokenfail')
        self.server = data['server']
        self.key = data['key']
        self.ts = data['ts']
        self.wait = wait
<|end_body_0|>

<|body_start_1|>
        response = requests.get(f'http://{self.server}?act=a_check&key={self.key}&ts={self.ts}&wait={self.wait}&version=3&mode=2')
        if response.status_code != 200:
            logger.error('Ошибка сети')
            return []
        self.time = datetime.now().timestamp()
        data = response.json()
        if 'failed' in data.keys():
            if data['failed'] == 1:
                logger.error('Ошибка истории событий')
                self.ts = data['ts']
            elif data['failed'] == 2:
                self.key = self.vk('messages.getLongPollServer')['key']
            else:
                raise Exception('Информация о пользователе утрачена')
            return []
        else:
            self.ts = data['ts']
            return data['updates']
<|end_body_1|>

Solution:
class LP:
    def __init__(self, vk, wait=25):
        """vk - экземпляр VkApi"""
        self.vk = vk
        data = vk('messages.getLongPollServer')
        if data.get('error'):
            if data['error']['error_code'] == 5:
                raise Exception('tokenfail')
        self.server = data['server']
        self.key = data['key']
        self.ts = data['ts']
        self.wait = wait

    def check(self):
        """Возвращает список событий (updates)"""
        response = requests.get(f'http://{self.server}?act=a_check&key={self.key}&ts={self.ts}&wait={self.wait}&version=3&mode=2')
        if response.status_code != 200:
            logger.error('Ошибка сети')
            return []
        self.time = datetime.now().timestamp()
        data = response.json()
        if 'failed' in data.keys():
            if data['failed'] == 1:
                logger.error('Ошибка истории событий')
                self.ts = data['ts']
            elif data['failed'] == 2:
                self.key = self.vk('messages.getLongPollServer')['key']
            else:
                raise Exception('Информация о пользователе утрачена')
            return []
        else:
            self.ts = data['ts']
            return data['updates']