handle

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

Class description:
Reassign permission to the model when permissions are changed

Method signatures and docstrings:
- def handle(self, *args, **options): Reassign permission to the model when permissions are changed
- def reassign_perms(self, user, app, model, new_perm): Gets all the permissions the user has on objects and assigns the new permission to them :param user: :param app: :param model: :param new_perm: :return:
- def check_role(self, role_class, user, obj, new_perm=None): Test if the user has the role for the object provided :param role_class: :param user: :param obj: :param new_perm: :return:

Implement the Python class `Command` described below.

Class description:
Reassign permission to the model when permissions are changed

Method signatures and docstrings:
- def handle(self, *args, **options): Reassign permission to the model when permissions are changed
- def reassign_perms(self, user, app, model, new_perm): Gets all the permissions the user has on objects and assigns the new permission to them :param user: :param app: :param model: :param new_perm: :return:
- def check_role(self, role_class, user, obj, new_perm=None): Test if the user has the role for the object provided :param role_class: :param user: :param obj: :param new_perm: :return:

<|skeleton|>
class Command:
    """Reassign permission to the model when permissions are changed"""

    def handle(self, *args, **options):
        """Reassign permission to the model when permissions are changed"""
        <|body_0|>

    def reassign_perms(self, user, app, model, new_perm):
        """Gets all the permissions the user has on objects and assigns the new permission to them :param user: :param app: :param model: :param new_perm: :return:"""
        <|body_1|>

    def check_role(self, role_class, user, obj, new_perm=None):
        """Test if the user has the role for the object provided :param role_class: :param user: :param obj: :param new_perm: :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.stdout.write('Re-assigining started', ending='\n')
        if not args:
            raise CommandError('Param not set. <app model [created_perm]>')
        if len(args) < 3:
            raise CommandError('Param not set. <app model [created_perm]>')
        app = args[0]
        model = args[1]
        username = args[2]
        new_perms = list(args[3:])
        if username == 'all':
            users = User.objects.exclude(username__iexact=settings.ANONYMOUS_DEFAULT_USERNAME)
            teams = Team.objects.all()
        else:
            users = User.objects.filter(username=username)
            teams = Team.objects.filter(organization__username=username)
        for user in queryset_iterator(users):
            self.reassign_perms(user, app, model, new_perms)
        for team in queryset_iterator(teams):
            self.reassign_perms(team, app, model, new_perms)
        self.stdout.write('Re-assigining finished', ending='\n')
<|end_body_0|>

<|body_start_1|>
        if isinstance(user, Team):
            if model == 'project':
                objects = user.projectgroupobjectpermission_set.filter(group_id=user.pk).distinct('content_object_id')
            else:
                objects = user.xformgroupobjectpermission_set.filter(group_id=user.pk).distinct('content_object_id')
        elif model == 'project':
            objects = user.projectuserobjectpermission_set.all()
        else:
            objects = user.xformuserobjectpermission_set.all()
        for perm_obj in objects:
            obj = perm_obj.content_object
            roles = [ReadOnlyRoleNoDownload, ReadOnlyRole, DataEntryOnlyRole, DataEntryMinorRole, DataEntryRole, EditorMinorRole, EditorRole, ManagerRole, OwnerRole]
            for role_class in reversed(roles):
                if self.check_role(role_class, user, obj, new_perm):
                    role_class.add(user, obj)
                    break
<|end_body_1|>

<|body_start_2|>
        new_perm = [] if new_perm is None else new_perm
        perm_list = role_class.class_to_permissions[type(obj)]
        old_perm_set = set(perm_list)
        newly_added_perm = set(new_perm)
        if newly_added_perm.issubset(old_perm_set):
            diff_set = old_perm_set.difference(newly_added_perm)
            if isinstance(user, Team):
                return set(get_perms(user, obj)) == diff_set
            return user.has_perms(list(diff_set), obj)
        return False
<|end_body_2|>

Solution:
class Command:
    """Reassign permission to the model when permissions are changed"""

    def handle(self, *args, **options):
        """Reassign permission to the model when permissions are changed"""
        self.stdout.write('Re-assigining started', ending='\n')
        if not args:
            raise CommandError('Param not set. <app model [created_perm]>')
        if len(args) < 3:
            raise CommandError('Param not set. <app model [created_perm]>')
        app = args[0]
        model = args[1]
        username = args[2]
        new_perms = list(args[3:])
        if username == 'all':
            users = User.objects.exclude(username__iexact=settings.ANONYMOUS_DEFAULT_USERNAME)
            teams = Team.objects.all()
        else:
            users = User.objects.filter(username=username)
            teams = Team.objects.filter(organization__username=username)
        for user in queryset_iterator(users):
            self.reassign_perms(user, app, model, new_perms)
        for team in queryset_iterator(teams):
            self.reassign_perms(team, app, model, new_perms)
        self.stdout.write('Re-assigining finished', ending='\n')

    def reassign_perms(self, user, app, model, new_perm):
        """Gets all the permissions the user has on objects and assigns the new permission to them :param user: :param app: :param model: :param new_perm: :return:"""
        if isinstance(user, Team):
            if model == 'project':
                objects = user.projectgroupobjectpermission_set.filter(group_id=user.pk).distinct('content_object_id')
            else:
                objects = user.xformgroupobjectpermission_set.filter(group_id=user.pk).distinct('content_object_id')
        elif model == 'project':
            objects = user.projectuserobjectpermission_set.all()
        else:
            objects = user.xformuserobjectpermission_set.all()
        for perm_obj in objects:
            obj = perm_obj.content_object
            roles = [ReadOnlyRoleNoDownload, ReadOnlyRole, DataEntryOnlyRole, DataEntryMinorRole, DataEntryRole, EditorMinorRole, EditorRole, ManagerRole, OwnerRole]
            for role_class in reversed(roles):
                if self.check_role(role_class, user, obj, new_perm):
                    role_class.add(user, obj)
                    break

    def check_role(self, role_class, user, obj, new_perm=None):
        """Test if the user has the role for the object provided :param role_class: :param user: :param obj: :param new_perm: :return:"""
        new_perm = [] if new_perm is None else new_perm
        perm_list = role_class.class_to_permissions[type(obj)]
        old_perm_set = set(perm_list)
        newly_added_perm = set(new_perm)
        if newly_added_perm.issubset(old_perm_set):
            diff_set = old_perm_set.difference(newly_added_perm)
            if isinstance(user, Team):
                return set(get_perms(user, obj)) == diff_set
            return user.has_perms(list(diff_set), obj)
        return False


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

Class description:
Implement the DestinationData class.

Method signatures and docstrings:
- def get_destinations(self): Returns all destinations in a list
- def create_destination(self, dest): Creates a new destination in database

Implement the Python class `DestinationData` described below.

Class description:
Implement the DestinationData class.

Method signatures and docstrings:
- def get_destinations(self): Returns all destinations in a list
- def create_destination(self, dest): Creates a new destination in database

<|skeleton|>
class DestinationData:

    def get_destinations(self):
        """Returns all destinations in a list"""
        <|body_0|>

    def create_destination(self, dest):
        """Creates a new destination in database"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        dest_list = []
        with open('data_layer/data_files/destinations.csv', encoding='utf-8') as file_stream:
            dest_reader = csv.DictReader(file_stream)
            for row in dest_reader:
                dest = Destination(row['country'], row['airport'], row['phone'], row['hours'], row['iata'])
                dest_list.append(dest)
            return dest_list
<|end_body_0|>

<|body_start_1|>
        new_dest_list = [dest.country, dest.airport, dest.phone, dest.hours, dest.iata]
        with open('data_layer/data_files/destinations.csv', 'a+', encoding='utf-8', newline='') as file_stream:
            destwriter = csv.writer(file_stream)
            destwriter.writerow(new_dest_list)
<|end_body_1|>

Solution:
class DestinationData:
    def get_destinations(self):
        """Returns all destinations in a list"""
        dest_list = []
        with open('data_layer/data_files/destinations.csv', encoding='utf-8') as file_stream:
            dest_reader = csv.DictReader(file_stream)
            for row in dest_reader:
                dest = Destination(row['country'], row['airport'], row['phone'], row['hours'], row['iata'])
                dest_list.append(dest)
            return dest_list

    def create_destination(self, dest):
        """Creates a new destination in database"""
        new_dest_list = [dest.country, dest.airport, dest.phone, dest.hours, dest.iata]
        with open('data_layer/data_files/destinations.csv', 'a+', encoding='utf-8', newline='') as file_stream:
            destwriter = csv.writer(file_stream)
            destwriter.writerow(new_dest_list)


Execute

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

Class description:
Returns per-product labels and values usage data. After execution, the 'rows' list will contain dictionaries of labels, values and counts. This raw data will be processed by a LabelManager into a queryable structure.

Method signatures and docstrings:
- def _Initialize(self): Set up the query configuration (fields, tables, etc.).
- def Execute(self, start_date=None, end_date=None, product_name=None, test=None, metric=None): Retrieves a list of labels and values for a specific product.
- def _ProcessReply(self, reply): Transforms the raw labels data into a JSON list.

Implement the Python class `ProductLabelsQuery` described below.

Class description:
Returns per-product labels and values usage data. After execution, the 'rows' list will contain dictionaries of labels, values and counts. This raw data will be processed by a LabelManager into a queryable structure.

Method signatures and docstrings:
- def _Initialize(self): Set up the query configuration (fields, tables, etc.).
- def Execute(self, start_date=None, end_date=None, product_name=None, test=None, metric=None): Retrieves a list of labels and values for a specific product.
- def _ProcessReply(self, reply): Transforms the raw labels data into a JSON list.

<|skeleton|>
class ProductLabelsQuery:
    """Returns per-product labels and values usage data. After execution, the 'rows' list will contain dictionaries of labels, values and counts. This raw data will be processed by a LabelManager into a queryable structure."""

    def _Initialize(self):
        """Set up the query configuration (fields, tables, etc.)."""
        <|body_0|>

    def Execute(self, start_date=None, end_date=None, product_name=None, test=None, metric=None):
        """Retrieves a list of labels and values for a specific product."""
        <|body_1|>

    def _ProcessReply(self, reply):
        """Transforms the raw labels data into a JSON list."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(ProductLabelsQuery, self)._Initialize()
        self.tables = ['metadata_cube']
        self.fields = ['label', 'value', 'SUM(count) AS count']
        self.groups = ['label', 'value']
        self.orders = ['label', 'value']
        self.reply_processors += [self._ProcessReply]
        self.max_rows = 2000
<|end_body_0|>

<|body_start_1|>
        if start_date:
            start_date_expr = self.GetTimestampFromFilterExpression(start_date)
            self.wheres.append('day_timestamp >= %s' % start_date_expr)
        if end_date:
            end_date_expr = self.GetTimestampFromFilterExpression(end_date, True)
            self.wheres.append('day_timestamp <= %s' % end_date_expr)
        if product_name:
            self.wheres.append('product_name = "%s"' % product_name)
        if test:
            self.wheres.append('test = "%s"' % test)
        if metric:
            self.wheres.append('metric = "%s"' % metric)
        return super(ProductLabelsQuery, self).Execute()
<|end_body_1|>

<|body_start_2|>
        rows = reply['rows']
        labels = label_manager.LabelManager()
        for row in rows:
            labels.AddLabel(row['label'], row['value'], row['count'])
        reply['labels'] = labels.labels
<|end_body_2|>

Solution:
class ProductLabelsQuery:
    """Returns per-product labels and values usage data. After execution, the 'rows' list will contain dictionaries of labels, values and counts. This raw data will be processed by a LabelManager into a queryable structure."""

    def _Initialize(self):
        """Set up the query configuration (fields, tables, etc.)."""
        super(ProductLabelsQuery, self)._Initialize()
        self.tables = ['metadata_cube']
        self.fields = ['label', 'value', 'SUM(count) AS count']
        self.groups = ['label', 'value']
        self.orders = ['label', 'value']
        self.reply_processors += [self._ProcessReply]
        self.max_rows = 2000

    def Execute(self, start_date=None, end_date=None, product_name=None, test=None, metric=None):
        """Retrieves a list of labels and values for a specific product."""
        if start_date:
            start_date_expr = self.GetTimestampFromFilterExpression(start_date)
            self.wheres.append('day_timestamp >= %s' % start_date_expr)
        if end_date:
            end_date_expr = self.GetTimestampFromFilterExpression(end_date, True)
            self.wheres.append('day_timestamp <= %s' % end_date_expr)
        if product_name:
            self.wheres.append('product_name = "%s"' % product_name)
        if test:
            self.wheres.append('test = "%s"' % test)
        if metric:
            self.wheres.append('metric = "%s"' % metric)
        return super(ProductLabelsQuery, self).Execute()

    def _ProcessReply(self, reply):
        """Transforms the raw labels data into a JSON list."""
        rows = reply['rows']
        labels = label_manager.LabelManager()
        for row in rows:
            labels.AddLabel(row['label'], row['value'], row['count'])
        reply['labels'] = labels.labels


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

Class description:
Implement the PrivunitTest class.

Method signatures and docstrings:
- def test_gamma_is_in_range(self): Test whether gamma adheres to (16a) or (16b) in the original paper.
- def test_c2_is_less_equal_c1(self): Tests if c2 is less than or equal to c1.
- def test_m_is_less_equal_one(self): Tests whether the inverse norm m is less than or equal to 1.
- def test_bias_and_norm_privunit(self): Checks whether the privatized x is unbiased and has the right norm.

Implement the Python class `PrivunitTest` described below.

Class description:
Implement the PrivunitTest class.

Method signatures and docstrings:
- def test_gamma_is_in_range(self): Test whether gamma adheres to (16a) or (16b) in the original paper.
- def test_c2_is_less_equal_c1(self): Tests if c2 is less than or equal to c1.
- def test_m_is_less_equal_one(self): Tests whether the inverse norm m is less than or equal to 1.
- def test_bias_and_norm_privunit(self): Checks whether the privatized x is unbiased and has the right norm.

<|skeleton|>
class PrivunitTest:

    def test_gamma_is_in_range(self):
        """Test whether gamma adheres to (16a) or (16b) in the original paper."""
        <|body_0|>

    def test_c2_is_less_equal_c1(self):
        """Tests if c2 is less than or equal to c1."""
        <|body_1|>

    def test_m_is_less_equal_one(self):
        """Tests whether the inverse norm m is less than or equal to 1."""
        <|body_2|>

    def test_bias_and_norm_privunit(self):
        """Checks whether the privatized x is unbiased and has the right norm."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                self.assertLessEqual(0, gamma)
                self.assertLessEqual(gamma, 1)
                if gamma <= np.sqrt(np.pi / (2 * (d - 1))) * (np.exp(eps) - 1) / (np.exp(eps) + 1):
                    flag_16a = True
                if eps >= 0.5 * np.log(d) + np.log(6) + np.log(gamma) - (d - 1) * np.log(1 - gamma ** 2) / 2 and gamma >= np.sqrt(2 / d):
                    flag_16b = True
                self.assertTrue(flag_16a or flag_16b)
<|end_body_0|>

<|body_start_1|>
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        p_space = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                for p in p_space:
                    c1, c2 = privunit.get_privunit_densities(d, gamma, p)
                    self.assertLessEqual(c2, c1)
<|end_body_1|>

<|body_start_2|>
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        p_space = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                for p in p_space:
                    m = privunit.getm(d, gamma, p)
                    self.assertLessEqual(m, 1)
<|end_body_2|>

<|body_start_3|>
        eps_space = [4, 5]
        d_space = [100]
        n = 50000
        budget = 0.5
        for eps in eps_space:
            for d in d_space:
                x = np.random.normal(0, 1, (d, 1))
                x = np.divide(x, np.linalg.norm(x, axis=0).reshape(1, -1))
                x = np.repeat(x, n, axis=1)
                x_privunit, m = privunit.apply_privunit(x, eps, budget)
                x_avg_privunit = np.mean(x_privunit, axis=1).reshape(-1, 1)
                x_mse = np.linalg.norm(np.mean(x, axis=1).reshape(-1, 1) - x_avg_privunit) ** 2
                self.assertLessEqual(x_mse, 0.01)
                x_norm = np.linalg.norm(x_privunit, axis=0) - np.ones(n) / m
                self.assertLessEqual(np.max(x_norm), 1e-06)
<|end_body_3|>

Solution:
class PrivunitTest:
    def test_gamma_is_in_range(self):
        """Test whether gamma adheres to (16a) or (16b) in the original paper."""
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                self.assertLessEqual(0, gamma)
                self.assertLessEqual(gamma, 1)
                if gamma <= np.sqrt(np.pi / (2 * (d - 1))) * (np.exp(eps) - 1) / (np.exp(eps) + 1):
                    flag_16a = True
                if eps >= 0.5 * np.log(d) + np.log(6) + np.log(gamma) - (d - 1) * np.log(1 - gamma ** 2) / 2 and gamma >= np.sqrt(2 / d):
                    flag_16b = True
                self.assertTrue(flag_16a or flag_16b)

    def test_c2_is_less_equal_c1(self):
        """Tests if c2 is less than or equal to c1."""
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        p_space = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                for p in p_space:
                    c1, c2 = privunit.get_privunit_densities(d, gamma, p)
                    self.assertLessEqual(c2, c1)

    def test_m_is_less_equal_one(self):
        """Tests whether the inverse norm m is less than or equal to 1."""
        eps_space = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        d_space = [100, 1000, 10000]
        p_space = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
        for eps in eps_space:
            for d in d_space:
                gamma, _ = privunit.find_best_gamma(d, eps)
                for p in p_space:
                    m = privunit.getm(d, gamma, p)
                    self.assertLessEqual(m, 1)

    def test_bias_and_norm_privunit(self):
        """Checks whether the privatized x is unbiased and has the right norm."""
        eps_space = [4, 5]
        d_space = [100]
        n = 50000
        budget = 0.5
        for eps in eps_space:
            for d in d_space:
                x = np.random.normal(0, 1, (d, 1))
                x = np.divide(x, np.linalg.norm(x, axis=0).reshape(1, -1))
                x = np.repeat(x, n, axis=1)
                x_privunit, m = privunit.apply_privunit(x, eps, budget)
                x_avg_privunit = np.mean(x_privunit, axis=1).reshape(-1, 1)
                x_mse = np.linalg.norm(np.mean(x, axis=1).reshape(-1, 1) - x_avg_privunit) ** 2
                self.assertLessEqual(x_mse, 0.01)
                x_norm = np.linalg.norm(x_privunit, axis=0) - np.ones(n) / m
                self.assertLessEqual(np.max(x_norm), 1e-06)


twoSum

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def twoSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum1(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum2(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def twoSum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum1(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def twoSum2(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]

<|skeleton|>
class Solution:

    def twoSum(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        <|body_0|>

    def twoSum1(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        <|body_1|>

    def twoSum2(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        result = []
        for idx1, init_val in enumerate(nums):
            updated_arr = nums[:idx1] + nums[idx1 + 1:]
            for indx2, val in enumerate(updated_arr):
                if val + init_val == target:
                    result.extend([idx1, indx2 + 1])
                    return result
<|end_body_0|>

<|body_start_1|>
        hash_t = {}
        for idx1, init_val in enumerate(nums):
            hash_t[idx1] = init_val
        for idx, val in enumerate(nums):
            balance = target - hash_t[idx]
            if balance in hash_t.values() and hash_t.keys()[hash_t.values().index(balance)] != idx:
                return [idx, hash_t.keys()[hash_t.values().index(balance)]]
<|end_body_1|>

<|body_start_2|>
        hash_t = {}
        for idx1, init_val in enumerate(nums):
            balance = target - init_val
            if balance in hash_t:
                return [hash_t[balance], idx1]
            hash_t[init_val] = idx1
<|end_body_2|>

Solution:
class Solution:
    def twoSum(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        result = []
        for idx1, init_val in enumerate(nums):
            updated_arr = nums[:idx1] + nums[idx1 + 1:]
            for indx2, val in enumerate(updated_arr):
                if val + init_val == target:
                    result.extend([idx1, indx2 + 1])
                    return result

    def twoSum1(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        hash_t = {}
        for idx1, init_val in enumerate(nums):
            hash_t[idx1] = init_val
        for idx, val in enumerate(nums):
            balance = target - hash_t[idx]
            if balance in hash_t.values() and hash_t.keys()[hash_t.values().index(balance)] != idx:
                return [idx, hash_t.keys()[hash_t.values().index(balance)]]

    def twoSum2(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        hash_t = {}
        for idx1, init_val in enumerate(nums):
            balance = target - init_val
            if balance in hash_t:
                return [hash_t[balance], idx1]
            hash_t[init_val] = idx1


search

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

Class description:
A simple text index from a corpus of text using tf-idf similarity.

Method signatures and docstrings:
- def __init__(self, documents, text_getter, ngram_range=(1, 2), analyzer=Analyzer.WORD, min_df=1, max_df=0.9): Init parameters for TextIndex. Args: documents: Corpus of documents to be indexed and retrieved. text_getter: Function to extract text from documents. ngram_range: tuple (min_n, max_n), default=(1, 2) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. analyzer: Analyzer, {‘word’, ‘char’, ‘char_wb’}. Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at
- def search(self, query, retrieval_threshold=0.0, num_results=5): Retrieve matching text in the corpus. Args: query: Text used to search for candidates in the corpus.s retrieval_threshold: Filter results above this threshold. num_results: Number of results to return. Returns: Tuple of text and float score. Top `num_results` elements in the corpus.

Implement the Python class `TextIndex` described below.

Class description:
A simple text index from a corpus of text using tf-idf similarity.

Method signatures and docstrings:
- def __init__(self, documents, text_getter, ngram_range=(1, 2), analyzer=Analyzer.WORD, min_df=1, max_df=0.9): Init parameters for TextIndex. Args: documents: Corpus of documents to be indexed and retrieved. text_getter: Function to extract text from documents. ngram_range: tuple (min_n, max_n), default=(1, 2) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. analyzer: Analyzer, {‘word’, ‘char’, ‘char_wb’}. Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at
- def search(self, query, retrieval_threshold=0.0, num_results=5): Retrieve matching text in the corpus. Args: query: Text used to search for candidates in the corpus.s retrieval_threshold: Filter results above this threshold. num_results: Number of results to return. Returns: Tuple of text and float score. Top `num_results` elements in the corpus.

<|skeleton|>
class TextIndex:
    """A simple text index from a corpus of text using tf-idf similarity."""

    def __init__(self, documents, text_getter, ngram_range=(1, 2), analyzer=Analyzer.WORD, min_df=1, max_df=0.9):
        """Init parameters for TextIndex. Args: documents: Corpus of documents to be indexed and retrieved. text_getter: Function to extract text from documents. ngram_range: tuple (min_n, max_n), default=(1, 2) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. analyzer: Analyzer, {‘word’, ‘char’, ‘char_wb’}. Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at"""
        <|body_0|>

    def search(self, query, retrieval_threshold=0.0, num_results=5):
        """Retrieve matching text in the corpus. Args: query: Text used to search for candidates in the corpus.s retrieval_threshold: Filter results above this threshold. num_results: Number of results to return. Returns: Tuple of text and float score. Top `num_results` elements in the corpus."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self._vectorizer = TfidfVectorizer(ngram_range=ngram_range, min_df=min_df, max_df=max_df, analyzer=analyzer.value)
        self._documents = documents
        self._index = self._vectorizer.fit_transform(map(text_getter, self._documents))
<|end_body_0|>

<|body_start_1|>
        query_vector = self._vectorizer.transform([query])
        scores = zip(self._documents, self._index.dot(query_vector.T).T.toarray()[0])
        filtered_scores = (SearchResult(doc, score) for doc, score in scores if score > retrieval_threshold)
        return heapq.nlargest(num_results, filtered_scores, key=lambda p: p.score)
<|end_body_1|>

Solution:
class TextIndex:
    """A simple text index from a corpus of text using tf-idf similarity."""

    def __init__(self, documents, text_getter, ngram_range=(1, 2), analyzer=Analyzer.WORD, min_df=1, max_df=0.9):
        """Init parameters for TextIndex. Args: documents: Corpus of documents to be indexed and retrieved. text_getter: Function to extract text from documents. ngram_range: tuple (min_n, max_n), default=(1, 2) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. analyzer: Analyzer, {‘word’, ‘char’, ‘char_wb’}. Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at"""
        self._vectorizer = TfidfVectorizer(ngram_range=ngram_range, min_df=min_df, max_df=max_df, analyzer=analyzer.value)
        self._documents = documents
        self._index = self._vectorizer.fit_transform(map(text_getter, self._documents))

    def search(self, query, retrieval_threshold=0.0, num_results=5):
        """Retrieve matching text in the corpus. Args: query: Text used to search for candidates in the corpus.s retrieval_threshold: Filter results above this threshold. num_results: Number of results to return. Returns: Tuple of text and float score. Top `num_results` elements in the corpus."""
        query_vector = self._vectorizer.transform([query])
        scores = zip(self._documents, self._index.dot(query_vector.T).T.toarray()[0])
        filtered_scores = (SearchResult(doc, score) for doc, score in scores if score > retrieval_threshold)
        return heapq.nlargest(num_results, filtered_scores, key=lambda p: p.score)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lowestCommonAncestor_recursively(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def lowestCommonAncestor_BSTway(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def lowestCommonAncestor_pathway(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lowestCommonAncestor_recursively(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def lowestCommonAncestor_BSTway(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def lowestCommonAncestor_pathway(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode

<|skeleton|>
class Solution:

    def lowestCommonAncestor_recursively(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        <|body_0|>

    def lowestCommonAncestor_BSTway(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        <|body_1|>

    def lowestCommonAncestor_pathway(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if root in (None, p, q):
            return root
        left = self.lowestCommonAncestor(root.left, p, q)
        right = self.lowestCommonAncestor(root.right, p, q)
        return root if left and right else left or right
<|end_body_0|>

<|body_start_1|>
        ans = root
        while ans:
            if min(p.val, q.val) > ans.val and ans.right:
                ans = ans.right
                continue
            elif max(p.val, q.val) < ans.val and ans.left:
                ans = ans.left
                continue
            return ans
<|end_body_1|>

<|body_start_2|>
        def path(root, p):
            s = []
            now = root
            while now and now.val != p.val:
                s.append(now)
                now = now.left if now.val > p.val else now.right
            s.append(now)
            return s
        pathP, pathQ = (path(root, p), path(root, q))
        ans, i = (None, 0)
        minlen = min(len(pathP), len(pathQ))
        while i < minlen and pathP[i] == pathQ[i]:
            ans = pathP[i]
            i += 1
        return ans
<|end_body_2|>

Solution:
class Solution:
    def lowestCommonAncestor_recursively(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        if root in (None, p, q):
            return root
        left = self.lowestCommonAncestor(root.left, p, q)
        right = self.lowestCommonAncestor(root.right, p, q)
        return root if left and right else left or right

    def lowestCommonAncestor_BSTway(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        ans = root
        while ans:
            if min(p.val, q.val) > ans.val and ans.right:
                ans = ans.right
                continue
            elif max(p.val, q.val) < ans.val and ans.left:
                ans = ans.left
                continue
            return ans

    def lowestCommonAncestor_pathway(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        def path(root, p):
            s = []
            now = root
            while now and now.val != p.val:
                s.append(now)
                now = now.left if now.val > p.val else now.right
            s.append(now)
            return s
        pathP, pathQ = (path(root, p), path(root, q))
        ans, i = (None, 0)
        minlen = min(len(pathP), len(pathQ))
        while i < minlen and pathP[i] == pathQ[i]:
            ans = pathP[i]
            i += 1
        return ans


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

Class description:
Implementation of the 'PhysicalBackupSourceParams' model. Message to capture additional backup params for a Physical type source. Attributes: enable_system_backup (bool): Allows Magneto to drive a "system" backup using a 3rd-party tool installed on the Agent host. file_backup_params (PhysicalFileBackupParams): If the backup job type is kPhysicalFiles, this field should be set to specify the files to be backed up from the volumes on this source. snapshot_params (PhysicalSnapshotParams): This captures params to use for taking the snapshot. source_app_params (SourceAppParams): This message will capture params for applications that are running as part of the server. volume_guid_vec (list of stri

Method signatures and docstrings:
- def __init__(self, enable_system_backup=None, file_backup_params=None, snapshot_params=None, source_app_params=None, volume_guid_vec=None): Constructor for the PhysicalBackupSourceParams 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 `PhysicalBackupSourceParams` described below.

Class description:
Implementation of the 'PhysicalBackupSourceParams' model. Message to capture additional backup params for a Physical type source. Attributes: enable_system_backup (bool): Allows Magneto to drive a "system" backup using a 3rd-party tool installed on the Agent host. file_backup_params (PhysicalFileBackupParams): If the backup job type is kPhysicalFiles, this field should be set to specify the files to be backed up from the volumes on this source. snapshot_params (PhysicalSnapshotParams): This captures params to use for taking the snapshot. source_app_params (SourceAppParams): This message will capture params for applications that are running as part of the server. volume_guid_vec (list of stri

Method signatures and docstrings:
- def __init__(self, enable_system_backup=None, file_backup_params=None, snapshot_params=None, source_app_params=None, volume_guid_vec=None): Constructor for the PhysicalBackupSourceParams 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 PhysicalBackupSourceParams:
    """Implementation of the 'PhysicalBackupSourceParams' model. Message to capture additional backup params for a Physical type source. Attributes: enable_system_backup (bool): Allows Magneto to drive a "system" backup using a 3rd-party tool installed on the Agent host. file_backup_params (PhysicalFileBackupParams): If the backup job type is kPhysicalFiles, this field should be set to specify the files to be backed up from the volumes on this source. snapshot_params (PhysicalSnapshotParams): This captures params to use for taking the snapshot. source_app_params (SourceAppParams): This message will capture params for applications that are running as part of the server. volume_guid_vec (list of stri"""

    def __init__(self, enable_system_backup=None, file_backup_params=None, snapshot_params=None, source_app_params=None, volume_guid_vec=None):
        """Constructor for the PhysicalBackupSourceParams 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.enable_system_backup = enable_system_backup
        self.file_backup_params = file_backup_params
        self.snapshot_params = snapshot_params
        self.source_app_params = source_app_params
        self.volume_guid_vec = volume_guid_vec
<|end_body_0|>

<|body_start_1|>
        if dictionary is None:
            return None
        enable_system_backup = dictionary.get('enableSystemBackup')
        file_backup_params = cohesity_management_sdk.models.physical_file_backup_params.PhysicalFileBackupParams.from_dictionary(dictionary.get('fileBackupParams')) if dictionary.get('fileBackupParams') else None
        snapshot_params = cohesity_management_sdk.models.physical_snapshot_params.PhysicalSnapshotParams.from_dictionary(dictionary.get('snapshotParams')) if dictionary.get('snapshotParams') else None
        source_app_params = cohesity_management_sdk.models.source_app_params.SourceAppParams.from_dictionary(dictionary.get('sourceAppParams')) if dictionary.get('sourceAppParams') else None
        volume_guid_vec = dictionary.get('volumeGuidVec')
        return cls(enable_system_backup, file_backup_params, snapshot_params, source_app_params, volume_guid_vec)
<|end_body_1|>

Solution:
class PhysicalBackupSourceParams:
    """Implementation of the 'PhysicalBackupSourceParams' model. Message to capture additional backup params for a Physical type source. Attributes: enable_system_backup (bool): Allows Magneto to drive a "system" backup using a 3rd-party tool installed on the Agent host. file_backup_params (PhysicalFileBackupParams): If the backup job type is kPhysicalFiles, this field should be set to specify the files to be backed up from the volumes on this source. snapshot_params (PhysicalSnapshotParams): This captures params to use for taking the snapshot. source_app_params (SourceAppParams): This message will capture params for applications that are running as part of the server. volume_guid_vec (list of stri"""

    def __init__(self, enable_system_backup=None, file_backup_params=None, snapshot_params=None, source_app_params=None, volume_guid_vec=None):
        """Constructor for the PhysicalBackupSourceParams class"""
        self.enable_system_backup = enable_system_backup
        self.file_backup_params = file_backup_params
        self.snapshot_params = snapshot_params
        self.source_app_params = source_app_params
        self.volume_guid_vec = volume_guid_vec

    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
        enable_system_backup = dictionary.get('enableSystemBackup')
        file_backup_params = cohesity_management_sdk.models.physical_file_backup_params.PhysicalFileBackupParams.from_dictionary(dictionary.get('fileBackupParams')) if dictionary.get('fileBackupParams') else None
        snapshot_params = cohesity_management_sdk.models.physical_snapshot_params.PhysicalSnapshotParams.from_dictionary(dictionary.get('snapshotParams')) if dictionary.get('snapshotParams') else None
        source_app_params = cohesity_management_sdk.models.source_app_params.SourceAppParams.from_dictionary(dictionary.get('sourceAppParams')) if dictionary.get('sourceAppParams') else None
        volume_guid_vec = dictionary.get('volumeGuidVec')
        return cls(enable_system_backup, file_backup_params, snapshot_params, source_app_params, volume_guid_vec)


update

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

Class description:
Implement the ConfigWrapper class.

Method signatures and docstrings:
- def __init__(self, path): Creates a object which parsed the file 'path' and created Config.parser
- def update(self): Reparses the file to have the latest information. Will replace current parser so save changes beforehand

Implement the Python class `ConfigWrapper` described below.

Class description:
Implement the ConfigWrapper class.

Method signatures and docstrings:
- def __init__(self, path): Creates a object which parsed the file 'path' and created Config.parser
- def update(self): Reparses the file to have the latest information. Will replace current parser so save changes beforehand

<|skeleton|>
class ConfigWrapper:

    def __init__(self, path):
        """Creates a object which parsed the file 'path' and created Config.parser"""
        <|body_0|>

    def update(self):
        """Reparses the file to have the latest information. Will replace current parser so save changes beforehand"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.path = os.path.normpath(path)
        self.name = string.replace(os.path.basename(os.path.normpath(path)), '.cfg', '')
        configdir = os.path.dirname(self.path)
        if not os.path.exists(configdir):
            os.makedirs(configdir)
        if not os.path.exists(self.path):
            f = open(self.path, 'w')
            f.close()
        self.update()
<|end_body_0|>

<|body_start_1|>
        self.parser = SafeConfigParser()
        confighandle = open(self.path, 'r')
        self.parser.readfp(confighandle, self.path)
        confighandle.close()
<|end_body_1|>

Solution:
class ConfigWrapper:
    def __init__(self, path):
        """Creates a object which parsed the file 'path' and created Config.parser"""
        self.path = os.path.normpath(path)
        self.name = string.replace(os.path.basename(os.path.normpath(path)), '.cfg', '')
        configdir = os.path.dirname(self.path)
        if not os.path.exists(configdir):
            os.makedirs(configdir)
        if not os.path.exists(self.path):
            f = open(self.path, 'w')
            f.close()
        self.update()

    def update(self):
        """Reparses the file to have the latest information. Will replace current parser so save changes beforehand"""
        self.parser = SafeConfigParser()
        confighandle = open(self.path, 'r')
        self.parser.readfp(confighandle, self.path)
        confighandle.close()


ReportPortrait

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

Class description:
Implement the IrpClient class.

Method signatures and docstrings:
- def RecommendContent(self, request): 获取推荐结果 :param request: Request instance for RecommendContent. :type request: :class:`tencentcloud.irp.v20220324.models.RecommendContentRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.RecommendContentResponse`
- def ReportAction(self, request): 上报行为 :param request: Request instance for ReportAction. :type request: :class:`tencentcloud.irp.v20220324.models.ReportActionRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportActionResponse`
- def ReportMaterial(self, request): 上报物料 :param request: Request instance for ReportMaterial. :type request: :class:`tencentcloud.irp.v20220324.models.ReportMaterialRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportMaterialResponse`
- def ReportPortrait(self, request): 上报用户画像 :param request: Request instance for ReportPortrait. :type request: :class:`tencentcloud.irp.v20220324.models.ReportPortraitRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportPortraitResponse`

Implement the Python class `IrpClient` described below.

Class description:
Implement the IrpClient class.

Method signatures and docstrings:
- def RecommendContent(self, request): 获取推荐结果 :param request: Request instance for RecommendContent. :type request: :class:`tencentcloud.irp.v20220324.models.RecommendContentRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.RecommendContentResponse`
- def ReportAction(self, request): 上报行为 :param request: Request instance for ReportAction. :type request: :class:`tencentcloud.irp.v20220324.models.ReportActionRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportActionResponse`
- def ReportMaterial(self, request): 上报物料 :param request: Request instance for ReportMaterial. :type request: :class:`tencentcloud.irp.v20220324.models.ReportMaterialRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportMaterialResponse`
- def ReportPortrait(self, request): 上报用户画像 :param request: Request instance for ReportPortrait. :type request: :class:`tencentcloud.irp.v20220324.models.ReportPortraitRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportPortraitResponse`

<|skeleton|>
class IrpClient:

    def RecommendContent(self, request):
        """获取推荐结果 :param request: Request instance for RecommendContent. :type request: :class:`tencentcloud.irp.v20220324.models.RecommendContentRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.RecommendContentResponse`"""
        <|body_0|>

    def ReportAction(self, request):
        """上报行为 :param request: Request instance for ReportAction. :type request: :class:`tencentcloud.irp.v20220324.models.ReportActionRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportActionResponse`"""
        <|body_1|>

    def ReportMaterial(self, request):
        """上报物料 :param request: Request instance for ReportMaterial. :type request: :class:`tencentcloud.irp.v20220324.models.ReportMaterialRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportMaterialResponse`"""
        <|body_2|>

    def ReportPortrait(self, request):
        """上报用户画像 :param request: Request instance for ReportPortrait. :type request: :class:`tencentcloud.irp.v20220324.models.ReportPortraitRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportPortraitResponse`"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('RecommendContent', params, headers=headers)
            response = json.loads(body)
            model = models.RecommendContentResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))
<|end_body_0|>

<|body_start_1|>
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportAction', params, headers=headers)
            response = json.loads(body)
            model = models.ReportActionResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))
<|end_body_1|>

<|body_start_2|>
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportMaterial', params, headers=headers)
            response = json.loads(body)
            model = models.ReportMaterialResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))
<|end_body_2|>

<|body_start_3|>
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportPortrait', params, headers=headers)
            response = json.loads(body)
            model = models.ReportPortraitResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))
<|end_body_3|>

Solution:
class IrpClient:
    def RecommendContent(self, request):
        """获取推荐结果 :param request: Request instance for RecommendContent. :type request: :class:`tencentcloud.irp.v20220324.models.RecommendContentRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.RecommendContentResponse`"""
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('RecommendContent', params, headers=headers)
            response = json.loads(body)
            model = models.RecommendContentResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))

    def ReportAction(self, request):
        """上报行为 :param request: Request instance for ReportAction. :type request: :class:`tencentcloud.irp.v20220324.models.ReportActionRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportActionResponse`"""
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportAction', params, headers=headers)
            response = json.loads(body)
            model = models.ReportActionResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))

    def ReportMaterial(self, request):
        """上报物料 :param request: Request instance for ReportMaterial. :type request: :class:`tencentcloud.irp.v20220324.models.ReportMaterialRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportMaterialResponse`"""
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportMaterial', params, headers=headers)
            response = json.loads(body)
            model = models.ReportMaterialResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))

    def ReportPortrait(self, request):
        """上报用户画像 :param request: Request instance for ReportPortrait. :type request: :class:`tencentcloud.irp.v20220324.models.ReportPortraitRequest` :rtype: :class:`tencentcloud.irp.v20220324.models.ReportPortraitResponse`"""
        try:
            params = request._serialize()
            headers = request.headers
            body = self.call('ReportPortrait', params, headers=headers)
            response = json.loads(body)
            model = models.ReportPortraitResponse()
            model._deserialize(response['Response'])
            return model
        except Exception as e:
            if isinstance(e, TencentCloudSDKException):
                raise
            else:
                raise TencentCloudSDKException(type(e).__name__, str(e))


move

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

Class description:
Implement the GameBall class.

Method signatures and docstrings:
- def __init__(self, pace=1, size=10, width=500, height=500): Constructor This method creates a ball with params that are pre-picked for now to keep it simple.
- def move(self, width=None, height=None): Description: move - updates a balls location based on current position and current direction Params: width [int] : width of window height [int] : height of window

Implement the Python class `GameBall` described below.

Class description:
Implement the GameBall class.

Method signatures and docstrings:
- def __init__(self, pace=1, size=10, width=500, height=500): Constructor This method creates a ball with params that are pre-picked for now to keep it simple.
- def move(self, width=None, height=None): Description: move - updates a balls location based on current position and current direction Params: width [int] : width of window height [int] : height of window

<|skeleton|>
class GameBall:

    def __init__(self, pace=1, size=10, width=500, height=500):
        """Constructor This method creates a ball with params that are pre-picked for now to keep it simple."""
        <|body_0|>

    def move(self, width=None, height=None):
        """Description: move - updates a balls location based on current position and current direction Params: width [int] : width of window height [int] : height of window"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.width = width
        self.height = height
        self.pace = 1
        self.size = 10
        self.red = 0
        self.green = 0
        self.blue = 0
        self.x = int(random.random() * self.width)
        self.y = int(random.random() * self.height)
        direction = [1, -1]
        random.shuffle(direction)
        self.dx = direction[0]
        random.shuffle(direction)
        self.dy = direction[0]
<|end_body_0|>

<|body_start_1|>
        if not width is None:
            self.setWidth(width)
        if not height is None:
            self.setHeight(height)
        half = int(self.size * 1.6 / 2)
        self.x = self.x + self.pace * self.dx
        self.y = self.y + self.pace * self.dy
        if self.y <= 0 + half:
            self.dy *= -1
        elif self.y >= self.height - half:
            self.dy *= -1
        if self.x <= 0 + half:
            self.dx *= -1
        elif self.x >= self.height - half:
            self.dx *= -1
        return (self.x, self.y)
<|end_body_1|>

Solution:
class GameBall:
    def __init__(self, pace=1, size=10, width=500, height=500):
        """Constructor This method creates a ball with params that are pre-picked for now to keep it simple."""
        self.width = width
        self.height = height
        self.pace = 1
        self.size = 10
        self.red = 0
        self.green = 0
        self.blue = 0
        self.x = int(random.random() * self.width)
        self.y = int(random.random() * self.height)
        direction = [1, -1]
        random.shuffle(direction)
        self.dx = direction[0]
        random.shuffle(direction)
        self.dy = direction[0]

    def move(self, width=None, height=None):
        """Description: move - updates a balls location based on current position and current direction Params: width [int] : width of window height [int] : height of window"""
        if not width is None:
            self.setWidth(width)
        if not height is None:
            self.setHeight(height)
        half = int(self.size * 1.6 / 2)
        self.x = self.x + self.pace * self.dx
        self.y = self.y + self.pace * self.dy
        if self.y <= 0 + half:
            self.dy *= -1
        elif self.y >= self.height - half:
            self.dy *= -1
        if self.x <= 0 + half:
            self.dx *= -1
        elif self.x >= self.height - half:
            self.dx *= -1
        return (self.x, self.y)


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

Class description:
Handle a config flow for Uptime Kuma.

Method signatures and docstrings:
- async def _validate_input(self, data: dict[str, Any]) -> tuple[dict[str, str], None]: Validate the user input allows us to connect.
- async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult: Handle the initial step.

Implement the Python class `ConfigFlow` described below.

Class description:
Handle a config flow for Uptime Kuma.

Method signatures and docstrings:
- async def _validate_input(self, data: dict[str, Any]) -> tuple[dict[str, str], None]: Validate the user input allows us to connect.
- async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult: Handle the initial step.

<|skeleton|>
class ConfigFlow:
    """Handle a config flow for Uptime Kuma."""

    async def _validate_input(self, data: dict[str, Any]) -> tuple[dict[str, str], None]:
        """Validate the user input allows us to connect."""
        <|body_0|>

    async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
        """Handle the initial step."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        errors: dict[str, str] = {}
        host: str = data[CONF_HOST]
        port: int = data[CONF_PORT]
        username: str = data[CONF_USERNAME]
        password: str = data[CONF_PASSWORD]
        verify_ssl: bool = data[CONF_VERIFY_SSL]
        uptime_robot_api = UptimeKuma(async_get_clientsession(self.hass), f'{host}:{port}', username, password, verify_ssl)
        try:
            await uptime_robot_api.async_get_monitors()
        except UptimeKumaException as exception:
            LOGGER.error(exception)
            errors['base'] = 'cannot_connect'
        except Exception as exception:
            LOGGER.exception(exception)
            errors['base'] = 'unknown'
        return errors
<|end_body_0|>

<|body_start_1|>
        if user_input is None:
            return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA)
        errors = await self._validate_input(user_input)
        if not errors:
            unique_id = f'{user_input[CONF_HOST]}:{user_input[CONF_PORT]}'
            await self.async_set_unique_id(unique_id)
            self._abort_if_unique_id_configured()
            return self.async_create_entry(title=unique_id, data=user_input)
        return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA, errors=errors)
<|end_body_1|>

Solution:
class ConfigFlow:
    """Handle a config flow for Uptime Kuma."""

    async def _validate_input(self, data: dict[str, Any]) -> tuple[dict[str, str], None]:
        """Validate the user input allows us to connect."""
        errors: dict[str, str] = {}
        host: str = data[CONF_HOST]
        port: int = data[CONF_PORT]
        username: str = data[CONF_USERNAME]
        password: str = data[CONF_PASSWORD]
        verify_ssl: bool = data[CONF_VERIFY_SSL]
        uptime_robot_api = UptimeKuma(async_get_clientsession(self.hass), f'{host}:{port}', username, password, verify_ssl)
        try:
            await uptime_robot_api.async_get_monitors()
        except UptimeKumaException as exception:
            LOGGER.error(exception)
            errors['base'] = 'cannot_connect'
        except Exception as exception:
            LOGGER.exception(exception)
            errors['base'] = 'unknown'
        return errors

    async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
        """Handle the initial step."""
        if user_input is None:
            return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA)
        errors = await self._validate_input(user_input)
        if not errors:
            unique_id = f'{user_input[CONF_HOST]}:{user_input[CONF_PORT]}'
            await self.async_set_unique_id(unique_id)
            self._abort_if_unique_id_configured()
            return self.async_create_entry(title=unique_id, data=user_input)
        return self.async_show_form(step_id='user', data_schema=STEP_USER_DATA_SCHEMA, errors=errors)


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

Class description:
Generate a new convolution and maxpool layer.

Method signatures and docstrings:
- def __init__(self, filtW, filtH, fmapsIn, fmapsOut, pool_sz=2): Initialize filters
- def forward(self, X): Apply convolutions and max pooling

Implement the Python class `ConvPoolLayer` described below.

Class description:
Generate a new convolution and maxpool layer.

Method signatures and docstrings:
- def __init__(self, filtW, filtH, fmapsIn, fmapsOut, pool_sz=2): Initialize filters
- def forward(self, X): Apply convolutions and max pooling

<|skeleton|>
class ConvPoolLayer:
    """Generate a new convolution and maxpool layer."""

    def __init__(self, filtW, filtH, fmapsIn, fmapsOut, pool_sz=2):
        """Initialize filters"""
        <|body_0|>

    def forward(self, X):
        """Apply convolutions and max pooling"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.shape = (filtW, filtH, fmapsIn, fmapsOut)
        self.pool_sz = pool_sz
        '\n        NOTE: the * in *self.shape unpacks the tuple, since np.random.randn\n        takes dimensions as seperate integer arguments, not a tuple like zeros.\n        '
        W = np.random.randn(*self.shape) * np.sqrt(2.0 / np.prod(self.shape[:-1]))
        self.W = tf.Variable(W.astype(np.float32))
        b = np.zeros(fmapsOut, dtype=np.float32)
        self.b = tf.Variable(b)
        self.params = [self.W, self.b]
<|end_body_0|>

<|body_start_1|>
        conv_out = tf.nn.conv2d(X, self.W, [1, 1, 1, 1], 'SAME')
        conv_out = tf.nn.bias_add(conv_out, self.b)
        p = self.pool_sz
        pool_out = tf.nn.max_pool(conv_out, [1, p, p, 1], [1, p, p, 1], 'SAME')
        return pool_out
<|end_body_1|>

Solution:
class ConvPoolLayer:
    """Generate a new convolution and maxpool layer."""

    def __init__(self, filtW, filtH, fmapsIn, fmapsOut, pool_sz=2):
        """Initialize filters"""
        self.shape = (filtW, filtH, fmapsIn, fmapsOut)
        self.pool_sz = pool_sz
        '\n        NOTE: the * in *self.shape unpacks the tuple, since np.random.randn\n        takes dimensions as seperate integer arguments, not a tuple like zeros.\n        '
        W = np.random.randn(*self.shape) * np.sqrt(2.0 / np.prod(self.shape[:-1]))
        self.W = tf.Variable(W.astype(np.float32))
        b = np.zeros(fmapsOut, dtype=np.float32)
        self.b = tf.Variable(b)
        self.params = [self.W, self.b]

    def forward(self, X):
        """Apply convolutions and max pooling"""
        conv_out = tf.nn.conv2d(X, self.W, [1, 1, 1, 1], 'SAME')
        conv_out = tf.nn.bias_add(conv_out, self.b)
        p = self.pool_sz
        pool_out = tf.nn.max_pool(conv_out, [1, p, p, 1], [1, p, p, 1], 'SAME')
        return pool_out


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

Class description:
Implement the MultipleFileBrowseButton class.

Method signatures and docstrings:
- def OnBrowse(self, event=None): Going to browse for file...
- def SetValue(self, value, callBack=1): set current value of text control

Implement the Python class `MultipleFileBrowseButton` described below.

Class description:
Implement the MultipleFileBrowseButton class.

Method signatures and docstrings:
- def OnBrowse(self, event=None): Going to browse for file...
- def SetValue(self, value, callBack=1): set current value of text control

<|skeleton|>
class MultipleFileBrowseButton:

    def OnBrowse(self, event=None):
        """Going to browse for file..."""
        <|body_0|>

    def SetValue(self, value, callBack=1):
        """set current value of text control"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        current = self.GetValue()
        directory = os.path.split(current)
        if os.path.isdir(current):
            directory = current
            current = ''
        elif directory and os.path.isdir(directory[0]):
            current = directory[1]
            directory = directory[0]
        else:
            directory = self.startDirectory
            current = ''
        dlg = wx.FileDialog(self, self.dialogTitle, directory, current, self.fileMask, self.fileMode)
        if dlg.ShowModal() == wx.ID_OK:
            self.SetValue(dlg.GetPaths())
        dlg.Destroy()
<|end_body_0|>

<|body_start_1|>
        save = self.callCallback
        self.callCallback = callBack
        self.textControl.SetValue(' '.join(['"' + _ + '"' for _ in value]))
        self.callCallback = save
<|end_body_1|>

Solution:
class MultipleFileBrowseButton:
    def OnBrowse(self, event=None):
        """Going to browse for file..."""
        current = self.GetValue()
        directory = os.path.split(current)
        if os.path.isdir(current):
            directory = current
            current = ''
        elif directory and os.path.isdir(directory[0]):
            current = directory[1]
            directory = directory[0]
        else:
            directory = self.startDirectory
            current = ''
        dlg = wx.FileDialog(self, self.dialogTitle, directory, current, self.fileMask, self.fileMode)
        if dlg.ShowModal() == wx.ID_OK:
            self.SetValue(dlg.GetPaths())
        dlg.Destroy()

    def SetValue(self, value, callBack=1):
        """set current value of text control"""
        save = self.callCallback
        self.callCallback = callBack
        self.textControl.SetValue(' '.join(['"' + _ + '"' for _ in value]))
        self.callCallback = save


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

Class description:
Class that manages the training loop. See speechbrain.core.Brain.

Method signatures and docstrings:
- def compute_forward(self, batch, stage): Apply masking to convert from noisy waveforms to enhanced signals. Arguments --------- batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- predictions : dict A dictionary with keys {"spec", "wav"} with predicted features.
- def compute_feats(self, wavs): Returns corresponding log-spectral features of the input waveforms. Arguments --------- wavs : torch.Tensor The batch of waveforms to convert to log-spectral features.
- def compute_objectives(self, predictions, batch, stage): Computes the loss given the predicted and targeted outputs. Arguments --------- predictions : dict The output dict from `compute_forward`. batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- loss : torch.Tensor A one-element tensor used for backpropagating the gradient.
- def on_stage_start(self, stage, epoch=None): Gets called at the beginning of each epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. epoch : int The currently-starting epoch. This is passed `None` during the test stage.
- def on_stage_end(self, stage, stage_loss, epoch=None): Gets called at the end of an epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, sb.Stage.TEST stage_loss : float The average loss for all of the data processed in this stage. epoch : int The currently-starting epoch. This is passed `None` during the test stage.

Implement the Python class `SEBrain` described below.

Class description:
Class that manages the training loop. See speechbrain.core.Brain.

Method signatures and docstrings:
- def compute_forward(self, batch, stage): Apply masking to convert from noisy waveforms to enhanced signals. Arguments --------- batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- predictions : dict A dictionary with keys {"spec", "wav"} with predicted features.
- def compute_feats(self, wavs): Returns corresponding log-spectral features of the input waveforms. Arguments --------- wavs : torch.Tensor The batch of waveforms to convert to log-spectral features.
- def compute_objectives(self, predictions, batch, stage): Computes the loss given the predicted and targeted outputs. Arguments --------- predictions : dict The output dict from `compute_forward`. batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- loss : torch.Tensor A one-element tensor used for backpropagating the gradient.
- def on_stage_start(self, stage, epoch=None): Gets called at the beginning of each epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. epoch : int The currently-starting epoch. This is passed `None` during the test stage.
- def on_stage_end(self, stage, stage_loss, epoch=None): Gets called at the end of an epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, sb.Stage.TEST stage_loss : float The average loss for all of the data processed in this stage. epoch : int The currently-starting epoch. This is passed `None` during the test stage.

<|skeleton|>
class SEBrain:
    """Class that manages the training loop. See speechbrain.core.Brain."""

    def compute_forward(self, batch, stage):
        """Apply masking to convert from noisy waveforms to enhanced signals. Arguments --------- batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- predictions : dict A dictionary with keys {"spec", "wav"} with predicted features."""
        <|body_0|>

    def compute_feats(self, wavs):
        """Returns corresponding log-spectral features of the input waveforms. Arguments --------- wavs : torch.Tensor The batch of waveforms to convert to log-spectral features."""
        <|body_1|>

    def compute_objectives(self, predictions, batch, stage):
        """Computes the loss given the predicted and targeted outputs. Arguments --------- predictions : dict The output dict from `compute_forward`. batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- loss : torch.Tensor A one-element tensor used for backpropagating the gradient."""
        <|body_2|>

    def on_stage_start(self, stage, epoch=None):
        """Gets called at the beginning of each epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. epoch : int The currently-starting epoch. This is passed `None` during the test stage."""
        <|body_3|>

    def on_stage_end(self, stage, stage_loss, epoch=None):
        """Gets called at the end of an epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, sb.Stage.TEST stage_loss : float The average loss for all of the data processed in this stage. epoch : int The currently-starting epoch. This is passed `None` during the test stage."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        batch = batch.to(self.device)
        noisy_wavs, lens = batch.noisy_sig
        noisy_feats = self.compute_feats(noisy_wavs)
        mask = self.modules.model(noisy_feats)
        predict_spec = torch.mul(mask, noisy_feats)
        predict_wav = self.hparams.resynth(torch.expm1(predict_spec), noisy_wavs)
        return {'spec': predict_spec, 'wav': predict_wav}
<|end_body_0|>

<|body_start_1|>
        feats = self.hparams.compute_STFT(wavs)
        feats = sb.processing.features.spectral_magnitude(feats, power=0.5)
        feats = torch.log1p(feats)
        return feats
<|end_body_1|>

<|body_start_2|>
        clean_wavs, lens = batch.clean_sig
        clean_spec = self.compute_feats(clean_wavs)
        loss = sb.nnet.losses.mse_loss(predictions['spec'], clean_spec, lens)
        self.loss_metric.append(batch.id, predictions['spec'], clean_spec, lens, reduction='batch')
        if stage != sb.Stage.TRAIN:
            self.stoi_metric.append(batch.id, predictions['wav'], clean_wavs, lens, reduction='batch')
        return loss
<|end_body_2|>

<|body_start_3|>
        self.loss_metric = sb.utils.metric_stats.MetricStats(metric=sb.nnet.losses.mse_loss)
        if stage != sb.Stage.TRAIN:
            self.stoi_metric = sb.utils.metric_stats.MetricStats(metric=sb.nnet.loss.stoi_loss.stoi_loss)
<|end_body_3|>

<|body_start_4|>
        if stage == sb.Stage.TRAIN:
            self.train_loss = stage_loss
        else:
            stats = {'loss': stage_loss, 'stoi': -self.stoi_metric.summarize('average')}
        if stage == sb.Stage.VALID:
            self.hparams.train_logger.log_stats({'Epoch': epoch}, train_stats={'loss': self.train_loss}, valid_stats=stats)
            self.checkpointer.save_and_keep_only(meta=stats, max_keys=['stoi'])
        if stage == sb.Stage.TEST:
            self.hparams.train_logger.log_stats({'Epoch loaded': self.hparams.epoch_counter.current}, test_stats=stats)
<|end_body_4|>

Solution:
class SEBrain:
    """Class that manages the training loop. See speechbrain.core.Brain."""

    def compute_forward(self, batch, stage):
        """Apply masking to convert from noisy waveforms to enhanced signals. Arguments --------- batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- predictions : dict A dictionary with keys {"spec", "wav"} with predicted features."""
        batch = batch.to(self.device)
        noisy_wavs, lens = batch.noisy_sig
        noisy_feats = self.compute_feats(noisy_wavs)
        mask = self.modules.model(noisy_feats)
        predict_spec = torch.mul(mask, noisy_feats)
        predict_wav = self.hparams.resynth(torch.expm1(predict_spec), noisy_wavs)
        return {'spec': predict_spec, 'wav': predict_wav}

    def compute_feats(self, wavs):
        """Returns corresponding log-spectral features of the input waveforms. Arguments --------- wavs : torch.Tensor The batch of waveforms to convert to log-spectral features."""
        feats = self.hparams.compute_STFT(wavs)
        feats = sb.processing.features.spectral_magnitude(feats, power=0.5)
        feats = torch.log1p(feats)
        return feats

    def compute_objectives(self, predictions, batch, stage):
        """Computes the loss given the predicted and targeted outputs. Arguments --------- predictions : dict The output dict from `compute_forward`. batch : PaddedBatch This batch object contains all the relevant tensors for computation. stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. Returns ------- loss : torch.Tensor A one-element tensor used for backpropagating the gradient."""
        clean_wavs, lens = batch.clean_sig
        clean_spec = self.compute_feats(clean_wavs)
        loss = sb.nnet.losses.mse_loss(predictions['spec'], clean_spec, lens)
        self.loss_metric.append(batch.id, predictions['spec'], clean_spec, lens, reduction='batch')
        if stage != sb.Stage.TRAIN:
            self.stoi_metric.append(batch.id, predictions['wav'], clean_wavs, lens, reduction='batch')
        return loss

    def on_stage_start(self, stage, epoch=None):
        """Gets called at the beginning of each epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, or sb.Stage.TEST. epoch : int The currently-starting epoch. This is passed `None` during the test stage."""
        self.loss_metric = sb.utils.metric_stats.MetricStats(metric=sb.nnet.losses.mse_loss)
        if stage != sb.Stage.TRAIN:
            self.stoi_metric = sb.utils.metric_stats.MetricStats(metric=sb.nnet.loss.stoi_loss.stoi_loss)

    def on_stage_end(self, stage, stage_loss, epoch=None):
        """Gets called at the end of an epoch. Arguments --------- stage : sb.Stage One of sb.Stage.TRAIN, sb.Stage.VALID, sb.Stage.TEST stage_loss : float The average loss for all of the data processed in this stage. epoch : int The currently-starting epoch. This is passed `None` during the test stage."""
        if stage == sb.Stage.TRAIN:
            self.train_loss = stage_loss
        else:
            stats = {'loss': stage_loss, 'stoi': -self.stoi_metric.summarize('average')}
        if stage == sb.Stage.VALID:
            self.hparams.train_logger.log_stats({'Epoch': epoch}, train_stats={'loss': self.train_loss}, valid_stats=stats)
            self.checkpointer.save_and_keep_only(meta=stats, max_keys=['stoi'])
        if stage == sb.Stage.TEST:
            self.hparams.train_logger.log_stats({'Epoch loaded': self.hparams.epoch_counter.current}, test_stats=stats)


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

Class description:
Implement the FuzzyDate class.

Method signatures and docstrings:
- def _serialize(self, value, attr, obj): Convert a Python object into an outside-world object
- def _deserialize(self, value, attr, obj): Convert a outside-world value into a Python object

Implement the Python class `FuzzyDate` described below.

Class description:
Implement the FuzzyDate class.

Method signatures and docstrings:
- def _serialize(self, value, attr, obj): Convert a Python object into an outside-world object
- def _deserialize(self, value, attr, obj): Convert a outside-world value into a Python object

<|skeleton|>
class FuzzyDate:

    def _serialize(self, value, attr, obj):
        """Convert a Python object into an outside-world object"""
        <|body_0|>

    def _deserialize(self, value, attr, obj):
        """Convert a outside-world value into a Python object"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not value:
            return None
        return value.isoformat()
<|end_body_0|>

<|body_start_1|>
        if not value:
            return None
        return parse(value)
<|end_body_1|>

Solution:
class FuzzyDate:
    def _serialize(self, value, attr, obj):
        """Convert a Python object into an outside-world object"""
        if not value:
            return None
        return value.isoformat()

    def _deserialize(self, value, attr, obj):
        """Convert a outside-world value into a Python object"""
        if not value:
            return None
        return parse(value)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def partitionDisjoint(self, A): :type A: List[int] :rtype: int
- def partitionDisjoint2(self, A): :type A: List[int] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def partitionDisjoint(self, A): :type A: List[int] :rtype: int
- def partitionDisjoint2(self, A): :type A: List[int] :rtype: int

<|skeleton|>
class Solution:

    def partitionDisjoint(self, A):
        """:type A: List[int] :rtype: int"""
        <|body_0|>

    def partitionDisjoint2(self, A):
        """:type A: List[int] :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        pivot = 0
        maxL = A[0]
        max_last = 0
        for i, num in enumerate(A):
            max_last = max(max_last, num)
            if num < maxL:
                pivot = i
                maxL = max_last
        return pivot + 1
<|end_body_0|>

<|body_start_1|>
        info = sorted(([num, i] for i, num in enumerate(A)))
        s = 0
        for index, (num, i) in enumerate(info):
            s += i
            if s == (index + 1) * index / 2:
                return index + 1
<|end_body_1|>

Solution:
class Solution:
    def partitionDisjoint(self, A):
        """:type A: List[int] :rtype: int"""
        pivot = 0
        maxL = A[0]
        max_last = 0
        for i, num in enumerate(A):
            max_last = max(max_last, num)
            if num < maxL:
                pivot = i
                maxL = max_last
        return pivot + 1

    def partitionDisjoint2(self, A):
        """:type A: List[int] :rtype: int"""
        info = sorted(([num, i] for i, num in enumerate(A)))
        s = 0
        for index, (num, i) in enumerate(info):
            s += i
            if s == (index + 1) * index / 2:
                return index + 1


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

Class description:
Implement the GenericDataSource class.

Method signatures and docstrings:
- def __init__(self, data_set: pd.Series, missing_data_strategy: MissingDataStrategy=MissingDataStrategy.fail): A data source which holds a pandas series indexed by date or datetime :param data_set: a pandas dataframe indexed by date or datetime :param missing_data_strategy: MissingDataStrategy which defines behaviour if data is missing, will only take effect if using get_data, gat_data_range has no expectations of the number of expected data points.
- def get_data(self, state: Union[dt.date, dt.datetime, Iterable]): Get the value of the dataset at a time or date. If a list of dates or times is provided return the avg value :param state: a date, datetime or a list of dates or datetimes :return: float value
- def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int]): get a range of values from the dataset. :param start: a date or datetime :param end: a date, datetime or an int. If an int is provided we return that many data points back from the start date :return: pd.Series

Implement the Python class `GenericDataSource` described below.

Class description:
Implement the GenericDataSource class.

Method signatures and docstrings:
- def __init__(self, data_set: pd.Series, missing_data_strategy: MissingDataStrategy=MissingDataStrategy.fail): A data source which holds a pandas series indexed by date or datetime :param data_set: a pandas dataframe indexed by date or datetime :param missing_data_strategy: MissingDataStrategy which defines behaviour if data is missing, will only take effect if using get_data, gat_data_range has no expectations of the number of expected data points.
- def get_data(self, state: Union[dt.date, dt.datetime, Iterable]): Get the value of the dataset at a time or date. If a list of dates or times is provided return the avg value :param state: a date, datetime or a list of dates or datetimes :return: float value
- def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int]): get a range of values from the dataset. :param start: a date or datetime :param end: a date, datetime or an int. If an int is provided we return that many data points back from the start date :return: pd.Series

<|skeleton|>
class GenericDataSource:

    def __init__(self, data_set: pd.Series, missing_data_strategy: MissingDataStrategy=MissingDataStrategy.fail):
        """A data source which holds a pandas series indexed by date or datetime :param data_set: a pandas dataframe indexed by date or datetime :param missing_data_strategy: MissingDataStrategy which defines behaviour if data is missing, will only take effect if using get_data, gat_data_range has no expectations of the number of expected data points."""
        <|body_0|>

    def get_data(self, state: Union[dt.date, dt.datetime, Iterable]):
        """Get the value of the dataset at a time or date. If a list of dates or times is provided return the avg value :param state: a date, datetime or a list of dates or datetimes :return: float value"""
        <|body_1|>

    def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int]):
        """get a range of values from the dataset. :param start: a date or datetime :param end: a date, datetime or an int. If an int is provided we return that many data points back from the start date :return: pd.Series"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self._data_set = data_set
        self._missing_data_strategy = missing_data_strategy
        self._tz_aware = isinstance(self._data_set.index[0], dt.datetime) and self._data_set.index[0].tzinfo is not None
        if self._missing_data_strategy == MissingDataStrategy.interpolate:
            self._data_set.interpolate()
        elif self._missing_data_strategy == MissingDataStrategy.fill_forward:
            self._data_set.ffill()
<|end_body_0|>

<|body_start_1|>
        if isinstance(state, Iterable):
            return [self.get_data(i) for i in state]
        if self._tz_aware and (state.tzinfo is None or state.tzinfo.utcoffset(state) is None):
            state = pytz.utc.localize(state)
        if pd.Timestamp(state) in self._data_set:
            return self._data_set[pd.Timestamp(state)]
        elif state in self._data_set or self._missing_data_strategy == MissingDataStrategy.fail:
            return self._data_set[state]
        else:
            if isinstance(self._data_set.index, pd.DatetimeIndex):
                self._data_set.at[pd.to_datetime(state)] = np.nan
                self._data_set.sort_index(inplace=True)
            else:
                self._data_set.at[state] = np.nan
            self._data_set.sort_index()
            if self._missing_data_strategy == MissingDataStrategy.interpolate:
                self._data_set = self._data_set.interpolate()
            elif self._missing_data_strategy == MissingDataStrategy.fill_forward:
                self._data_set = self._data_set.ffill()
            else:
                raise RuntimeError(f'unrecognised missing data strategy: {str(self._missing_data_strategy)}')
            return self._data_set[pd.to_datetime(state)] if isinstance(self._data_set.index, pd.DatetimeIndex) else self._data_set[state]
<|end_body_1|>

<|body_start_2|>
        if isinstance(end, int):
            return self._data_set.loc[self._data_set.index < start].tail(end)
        return self._data_set.loc[(start < self._data_set.index) & (self._data_set.index <= end)]
<|end_body_2|>

Solution:
class GenericDataSource:
    def __init__(self, data_set: pd.Series, missing_data_strategy: MissingDataStrategy=MissingDataStrategy.fail):
        """A data source which holds a pandas series indexed by date or datetime :param data_set: a pandas dataframe indexed by date or datetime :param missing_data_strategy: MissingDataStrategy which defines behaviour if data is missing, will only take effect if using get_data, gat_data_range has no expectations of the number of expected data points."""
        self._data_set = data_set
        self._missing_data_strategy = missing_data_strategy
        self._tz_aware = isinstance(self._data_set.index[0], dt.datetime) and self._data_set.index[0].tzinfo is not None
        if self._missing_data_strategy == MissingDataStrategy.interpolate:
            self._data_set.interpolate()
        elif self._missing_data_strategy == MissingDataStrategy.fill_forward:
            self._data_set.ffill()

    def get_data(self, state: Union[dt.date, dt.datetime, Iterable]):
        """Get the value of the dataset at a time or date. If a list of dates or times is provided return the avg value :param state: a date, datetime or a list of dates or datetimes :return: float value"""
        if isinstance(state, Iterable):
            return [self.get_data(i) for i in state]
        if self._tz_aware and (state.tzinfo is None or state.tzinfo.utcoffset(state) is None):
            state = pytz.utc.localize(state)
        if pd.Timestamp(state) in self._data_set:
            return self._data_set[pd.Timestamp(state)]
        elif state in self._data_set or self._missing_data_strategy == MissingDataStrategy.fail:
            return self._data_set[state]
        else:
            if isinstance(self._data_set.index, pd.DatetimeIndex):
                self._data_set.at[pd.to_datetime(state)] = np.nan
                self._data_set.sort_index(inplace=True)
            else:
                self._data_set.at[state] = np.nan
            self._data_set.sort_index()
            if self._missing_data_strategy == MissingDataStrategy.interpolate:
                self._data_set = self._data_set.interpolate()
            elif self._missing_data_strategy == MissingDataStrategy.fill_forward:
                self._data_set = self._data_set.ffill()
            else:
                raise RuntimeError(f'unrecognised missing data strategy: {str(self._missing_data_strategy)}')
            return self._data_set[pd.to_datetime(state)] if isinstance(self._data_set.index, pd.DatetimeIndex) else self._data_set[state]

    def get_data_range(self, start: Union[dt.date, dt.datetime], end: Union[dt.date, dt.datetime, int]):
        """get a range of values from the dataset. :param start: a date or datetime :param end: a date, datetime or an int. If an int is provided we return that many data points back from the start date :return: pd.Series"""
        if isinstance(end, int):
            return self._data_set.loc[self._data_set.index < start].tail(end)
        return self._data_set.loc[(start < self._data_set.index) & (self._data_set.index <= end)]