Prompt:
Implement the Python class `CoreDocumentWriter1` described below.

Class description:
Utility class for writing a CoreSession to XML in the NMF scenPlan schema. The init method builds an xml.dom.minidom.Document, and the writexml() method saves the XML file.

Method signatures and docstrings:
- def __init__(self, session): Create an empty Scenario XML Document, then populate it with objects from the given session.
- def writexml(self, filename): Commit to file

Implement the Python class `CoreDocumentWriter1` described below.

Class description:
Utility class for writing a CoreSession to XML in the NMF scenPlan schema. The init method builds an xml.dom.minidom.Document, and the writexml() method saves the XML file.

Method signatures and docstrings:
- def __init__(self, session): Create an empty Scenario XML Document, then populate it with objects from the given session.
- def writexml(self, filename): Commit to file

<|skeleton|>
class CoreDocumentWriter1:
    """Utility class for writing a CoreSession to XML in the NMF scenPlan schema. The init method builds an xml.dom.minidom.Document, and the writexml() method saves the XML file."""

    def __init__(self, session):
        """Create an empty Scenario XML Document, then populate it with objects from the given session."""
        <|body_0|>

    def writexml(self, filename):
        """Commit to file"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Document.__init__(self)
        session.info('Exporting to NMF XML version 1.0')
        with session._objslock:
            self.scenarioPlan = ScenarioPlan(self, session)
            if session.getstate() == coreapi.CORE_EVENT_RUNTIME_STATE:
                deployment = CoreDeploymentWriter(self, self.scenarioPlan, session)
                deployment.add_deployment()
                self.scenarioPlan.setAttribute('deployed', 'true')
<|end_body_0|>

<|body_start_1|>
        self.scenarioPlan.coreSession.info('saving session XML file %s' % filename)
        f = open(filename, 'w')
        Document.writexml(self, writer=f, indent='', addindent='  ', newl='\n', encoding='UTF-8')
        f.close()
        if self.scenarioPlan.coreSession.user is not None:
            uid = pwd.getpwnam(self.scenarioPlan.coreSession.user).pw_uid
            gid = os.stat(self.scenarioPlan.coreSession.sessiondir).st_gid
            os.chown(filename, uid, gid)
<|end_body_1|>

Solution:
class CoreDocumentWriter1:
    """Utility class for writing a CoreSession to XML in the NMF scenPlan schema. The init method builds an xml.dom.minidom.Document, and the writexml() method saves the XML file."""

    def __init__(self, session):
        """Create an empty Scenario XML Document, then populate it with objects from the given session."""
        Document.__init__(self)
        session.info('Exporting to NMF XML version 1.0')
        with session._objslock:
            self.scenarioPlan = ScenarioPlan(self, session)
            if session.getstate() == coreapi.CORE_EVENT_RUNTIME_STATE:
                deployment = CoreDeploymentWriter(self, self.scenarioPlan, session)
                deployment.add_deployment()
                self.scenarioPlan.setAttribute('deployed', 'true')

    def writexml(self, filename):
        """Commit to file"""
        self.scenarioPlan.coreSession.info('saving session XML file %s' % filename)
        f = open(filename, 'w')
        Document.writexml(self, writer=f, indent='', addindent='  ', newl='\n', encoding='UTF-8')
        f.close()
        if self.scenarioPlan.coreSession.user is not None:
            uid = pwd.getpwnam(self.scenarioPlan.coreSession.user).pw_uid
            gid = os.stat(self.scenarioPlan.coreSession.sessiondir).st_gid
            os.chown(filename, uid, gid)


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

Class description:
Implement the Config class.

Method signatures and docstrings:
- def __init__(self, config_path: str='', config_name: str='config.yaml'): init config :param config_path: local config file path :param config_name: local config file name
- def _get_env_config(config: str): get the configuration in the local environment variable :param config: :return:
- def _loading_config(self, config_path: str, config_name: str): load local configuration file :param config_path: :param config_name: :return:

Implement the Python class `Config` described below.

Class description:
Implement the Config class.

Method signatures and docstrings:
- def __init__(self, config_path: str='', config_name: str='config.yaml'): init config :param config_path: local config file path :param config_name: local config file name
- def _get_env_config(config: str): get the configuration in the local environment variable :param config: :return:
- def _loading_config(self, config_path: str, config_name: str): load local configuration file :param config_path: :param config_name: :return:

<|skeleton|>
class Config:

    def __init__(self, config_path: str='', config_name: str='config.yaml'):
        """init config :param config_path: local config file path :param config_name: local config file name"""
        <|body_0|>

    def _get_env_config(config: str):
        """get the configuration in the local environment variable :param config: :return:"""
        <|body_1|>

    def _loading_config(self, config_path: str, config_name: str):
        """load local configuration file :param config_path: :param config_name: :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.socket = _ConfigSocket()
        self.logging = _ConfigLogging()
        self._loading_config(config_path, config_name)
<|end_body_0|>

<|body_start_1|>
        if isinstance(config, str) and config.find('$env.') != -1:
            env_name = config.replace('$env.', '')
            return os.getenv(env_name)
        return config
<|end_body_1|>

<|body_start_2|>
        if len(config_path) and os.path.exists(config_path):
            abs_path = config_path
        else:
            abs_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
        cf_path = '%s/%s' % (abs_path, config_name)
        if not os.path.exists(cf_path):
            print('ERR: config file `%s` not exists' % cf_path)
            exit(1)
        fs = open(cf_path, encoding='UTF-8')
        configs = yaml.load(fs, Loader=yaml.FullLoader)
        socket = configs.get('socket', {})
        socket_file = self._get_env_config(socket.get('file'))
        if socket_file:
            self.socket.file = socket_file
        logger = configs.get('logging', {})
        logger_level = self._get_env_config(logger.get('level'))
        if logger_level:
            self.logging.level = logger_level
<|end_body_2|>

Solution:
class Config:
    def __init__(self, config_path: str='', config_name: str='config.yaml'):
        """init config :param config_path: local config file path :param config_name: local config file name"""
        self.socket = _ConfigSocket()
        self.logging = _ConfigLogging()
        self._loading_config(config_path, config_name)

    def _get_env_config(config: str):
        """get the configuration in the local environment variable :param config: :return:"""
        if isinstance(config, str) and config.find('$env.') != -1:
            env_name = config.replace('$env.', '')
            return os.getenv(env_name)
        return config

    def _loading_config(self, config_path: str, config_name: str):
        """load local configuration file :param config_path: :param config_name: :return:"""
        if len(config_path) and os.path.exists(config_path):
            abs_path = config_path
        else:
            abs_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
        cf_path = '%s/%s' % (abs_path, config_name)
        if not os.path.exists(cf_path):
            print('ERR: config file `%s` not exists' % cf_path)
            exit(1)
        fs = open(cf_path, encoding='UTF-8')
        configs = yaml.load(fs, Loader=yaml.FullLoader)
        socket = configs.get('socket', {})
        socket_file = self._get_env_config(socket.get('file'))
        if socket_file:
            self.socket.file = socket_file
        logger = configs.get('logging', {})
        logger_level = self._get_env_config(logger.get('level'))
        if logger_level:
            self.logging.level = logger_level


provenance

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

Class description:
Implement the same_zip class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

Implement the Python class `same_zip` described below.

Class description:
Implement the same_zip class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

<|skeleton|>
class same_zip:

    def execute(trial=False):
        """Retrieve some data sets (not using the API here for the sake of simplicity)."""
        <|body_0|>

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
        prop_Val = get_Col('propVal', repo)
        foodPan = get_Col('foodPan', repo)
        hospitals = get_Col('Hospitals', repo)
        master = create_final(hospitals, 'zip_code', 'location_zip', 'hospitals')
        master_hosp = master[0]
        master_zips = master[1]
        master = create_food(foodPan, 'zip_code', 'food_Pantries', master_zips, master_hosp)
        master = create_prop(prop_Val, 'zipcode', 'zip_code', 'propery_value_average', master_zips, master)
        repo.dropPermanent('Food_Prop_Hosp_ZipCodes')
        repo.createPermanent('Food_Prop_Hosp_ZipCodes')
        repo['ckarjadi_johnnyg7.Food_Prop_Hosp_ZipCodes'].insert_many(master)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}
<|end_body_0|>

<|body_start_1|>
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('bdp', 'https://data.cityofboston.gov/resource/')
        this_script = doc.agent('alg:same_zip', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        resource1 = doc.entity('dat:hospital_reduction', {'prov:label': 'Food Pantries, Property Values under average, Hospitals reduced by Zip Code', prov.model.PROV_TYPE: 'ont:DataResource', prov.model.PROV_TYPE: 'ont:Computation'})
        get_FPH = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_FPH, this_script)
        doc.usage(get_FPH, resource1, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '???'})
        FPH = doc.entity('dat:ckarjadi_johnnyg7#FPH', {prov.model.PROV_LABEL: 'Food Pantries, Property Values under average, Hospitals reduced by Zip Code', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(FPH, this_script)
        doc.wasGeneratedBy(FPH, get_FPH, endTime)
        doc.wasDerivedFrom(FPH, resource1, get_FPH, get_FPH, get_FPH)
        repo.record(doc.serialize())
        repo.logout()
        return doc
<|end_body_1|>

Solution:
class same_zip:
    def execute(trial=False):
        """Retrieve some data sets (not using the API here for the sake of simplicity)."""
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
        prop_Val = get_Col('propVal', repo)
        foodPan = get_Col('foodPan', repo)
        hospitals = get_Col('Hospitals', repo)
        master = create_final(hospitals, 'zip_code', 'location_zip', 'hospitals')
        master_hosp = master[0]
        master_zips = master[1]
        master = create_food(foodPan, 'zip_code', 'food_Pantries', master_zips, master_hosp)
        master = create_prop(prop_Val, 'zipcode', 'zip_code', 'propery_value_average', master_zips, master)
        repo.dropPermanent('Food_Prop_Hosp_ZipCodes')
        repo.createPermanent('Food_Prop_Hosp_ZipCodes')
        repo['ckarjadi_johnnyg7.Food_Prop_Hosp_ZipCodes'].insert_many(master)
        repo.logout()
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('ckarjadi_johnnyg7', 'ckarjadi_johnnyg7')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        doc.add_namespace('bdp', 'https://data.cityofboston.gov/resource/')
        this_script = doc.agent('alg:same_zip', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        resource1 = doc.entity('dat:hospital_reduction', {'prov:label': 'Food Pantries, Property Values under average, Hospitals reduced by Zip Code', prov.model.PROV_TYPE: 'ont:DataResource', prov.model.PROV_TYPE: 'ont:Computation'})
        get_FPH = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.wasAssociatedWith(get_FPH, this_script)
        doc.usage(get_FPH, resource1, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval', 'ont:Query': '???'})
        FPH = doc.entity('dat:ckarjadi_johnnyg7#FPH', {prov.model.PROV_LABEL: 'Food Pantries, Property Values under average, Hospitals reduced by Zip Code', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAttributedTo(FPH, this_script)
        doc.wasGeneratedBy(FPH, get_FPH, endTime)
        doc.wasDerivedFrom(FPH, resource1, get_FPH, get_FPH, get_FPH)
        repo.record(doc.serialize())
        repo.logout()
        return doc


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isPowerOfThree(self, n): :type n: int :rtype: bool
- def isPowerOfThree2(self, n): :type n: int :rtype: bool

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isPowerOfThree(self, n): :type n: int :rtype: bool
- def isPowerOfThree2(self, n): :type n: int :rtype: bool

<|skeleton|>
class Solution:

    def isPowerOfThree(self, n):
        """:type n: int :rtype: bool"""
        <|body_0|>

    def isPowerOfThree2(self, n):
        """:type n: int :rtype: bool"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for i in range(10000):
            if 3 ** i == n:
                return True
            elif 3 ** i > n:
                return False
<|end_body_0|>

<|body_start_1|>
        if n < 3:
            if n == 1:
                return True
            else:
                return False
        while n > 1:
            n_ = int(n / 3)
            if n_ * 3 == n:
                n = n_
            else:
                return False
        return True
<|end_body_1|>

Solution:
class Solution:
    def isPowerOfThree(self, n):
        """:type n: int :rtype: bool"""
        for i in range(10000):
            if 3 ** i == n:
                return True
            elif 3 ** i > n:
                return False

    def isPowerOfThree2(self, n):
        """:type n: int :rtype: bool"""
        if n < 3:
            if n == 1:
                return True
            else:
                return False
        while n > 1:
            n_ = int(n / 3)
            if n_ * 3 == n:
                n = n_
            else:
                return False
        return True


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

Class description:
kafka broker信息表视图

Method signatures and docstrings:
- def get_machine_statistics(self, request, *args, **kwargs): POST /kafka/brokers/get_machine_statistics 统计kafka投入已使用的机器数量
- def get_machine_statistics_top_five(self, request, *args, **kwargs): POST /kafka/brokers/get_machine_statistics_top_five 根据用户已有业务权限，查询每个业务的机器投入数量，输出TOP5
- def get_kafka_monitor_data(self, request, *args, **kwargs): POST /kafka/brokers/monitor_data 根据用户返回过滤条件，查询监控平台的监控数据并汇总到前端 返回字典格式
- def create_monitor(self, request, *args, **kwargs): POST /kafka/brokers/create_monitor 部署集群监控

Implement the Python class `KafkaBrokerViewSet` described below.

Class description:
kafka broker信息表视图

Method signatures and docstrings:
- def get_machine_statistics(self, request, *args, **kwargs): POST /kafka/brokers/get_machine_statistics 统计kafka投入已使用的机器数量
- def get_machine_statistics_top_five(self, request, *args, **kwargs): POST /kafka/brokers/get_machine_statistics_top_five 根据用户已有业务权限，查询每个业务的机器投入数量，输出TOP5
- def get_kafka_monitor_data(self, request, *args, **kwargs): POST /kafka/brokers/monitor_data 根据用户返回过滤条件，查询监控平台的监控数据并汇总到前端 返回字典格式
- def create_monitor(self, request, *args, **kwargs): POST /kafka/brokers/create_monitor 部署集群监控

<|skeleton|>
class KafkaBrokerViewSet:
    """kafka broker信息表视图"""

    def get_machine_statistics(self, request, *args, **kwargs):
        """POST /kafka/brokers/get_machine_statistics 统计kafka投入已使用的机器数量"""
        <|body_0|>

    def get_machine_statistics_top_five(self, request, *args, **kwargs):
        """POST /kafka/brokers/get_machine_statistics_top_five 根据用户已有业务权限，查询每个业务的机器投入数量，输出TOP5"""
        <|body_1|>

    def get_kafka_monitor_data(self, request, *args, **kwargs):
        """POST /kafka/brokers/monitor_data 根据用户返回过滤条件，查询监控平台的监控数据并汇总到前端 返回字典格式"""
        <|body_2|>

    def create_monitor(self, request, *args, **kwargs):
        """POST /kafka/brokers/create_monitor 部署集群监控"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        try:
            app_id_list = get_cc_app_id_by_user()
            data_result = machine_statistics(table_set=KafkaBroker, field='ip', app_id_list=app_id_list)
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'kafka机器查询汇总失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': '0', 'message': f'{err}'})
<|end_body_0|>

<|body_start_1|>
        try:
            app_dict = get_cc_app_id_by_user(res='print_name')
            data_result = machine_statistics_group_app_id_top_five(table_set=KafkaBroker, field='ip', app_dict=app_dict)
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'查询前5位业务的kafka机器使用数量汇总失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': [], 'message': f'{err}'})
<|end_body_1|>

<|body_start_2|>
        try:
            post_data = request.data
            bk_username = request.user.username
            get_type = post_data.get('get_type')
            if get_type == 'cluster':
                data_result = get_data_in_cluster_monitor(post_data, bk_username)
            elif get_type == 'topic':
                data_result = get_data_in_topic_monitor(post_data, bk_username)
            elif get_type == 'consumer_group':
                data_result = get_data_in_consumer_group_monitor(post_data, bk_username)
            else:
                logger.warning(f'匹配不到传入的维度，请检测: get_type:{get_type}')
                data_result = []
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'查询并汇总监控数据失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': [], 'message': f'查询并汇总监控数据失败:{err}'})
<|end_body_2|>

<|body_start_3|>
        try:
            post_data = request.data
            bk_username = request.user.username
            info = retrieval_kafka_install_monitor_param(post_data=post_data, bk_username=bk_username)
            if info['code'] == 0:
                return JsonResponse(info['data'])
            install_parameter = info['data']
            if install_kafka_cluster_monitor_flow(install_parameter):
                return JsonResponse({'result': True, 'data': [], 'code': 0, 'message': '监控部署任务已启动'})
            return JsonResponse({'result': False, 'data': [], 'code': 1, 'message': '监控部署任务失败'})
        except Exception as err:
            logger.error(f'创建集群监控失败:{err}')
            return JsonResponse({'result': False, 'data': [], 'message': f'{err}', 'code': 1})
<|end_body_3|>

Solution:
class KafkaBrokerViewSet:
    """kafka broker信息表视图"""

    def get_machine_statistics(self, request, *args, **kwargs):
        """POST /kafka/brokers/get_machine_statistics 统计kafka投入已使用的机器数量"""
        try:
            app_id_list = get_cc_app_id_by_user()
            data_result = machine_statistics(table_set=KafkaBroker, field='ip', app_id_list=app_id_list)
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'kafka机器查询汇总失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': '0', 'message': f'{err}'})

    def get_machine_statistics_top_five(self, request, *args, **kwargs):
        """POST /kafka/brokers/get_machine_statistics_top_five 根据用户已有业务权限，查询每个业务的机器投入数量，输出TOP5"""
        try:
            app_dict = get_cc_app_id_by_user(res='print_name')
            data_result = machine_statistics_group_app_id_top_five(table_set=KafkaBroker, field='ip', app_dict=app_dict)
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'查询前5位业务的kafka机器使用数量汇总失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': [], 'message': f'{err}'})

    def get_kafka_monitor_data(self, request, *args, **kwargs):
        """POST /kafka/brokers/monitor_data 根据用户返回过滤条件，查询监控平台的监控数据并汇总到前端 返回字典格式"""
        try:
            post_data = request.data
            bk_username = request.user.username
            get_type = post_data.get('get_type')
            if get_type == 'cluster':
                data_result = get_data_in_cluster_monitor(post_data, bk_username)
            elif get_type == 'topic':
                data_result = get_data_in_topic_monitor(post_data, bk_username)
            elif get_type == 'consumer_group':
                data_result = get_data_in_consumer_group_monitor(post_data, bk_username)
            else:
                logger.warning(f'匹配不到传入的维度，请检测: get_type:{get_type}')
                data_result = []
            return JsonResponse({'result': True, 'code': 0, 'data': data_result, 'message': 'query success'})
        except Exception as err:
            logger.error(f'查询并汇总监控数据失败:{err}')
            return JsonResponse({'result': False, 'code': 1, 'data': [], 'message': f'查询并汇总监控数据失败:{err}'})

    def create_monitor(self, request, *args, **kwargs):
        """POST /kafka/brokers/create_monitor 部署集群监控"""
        try:
            post_data = request.data
            bk_username = request.user.username
            info = retrieval_kafka_install_monitor_param(post_data=post_data, bk_username=bk_username)
            if info['code'] == 0:
                return JsonResponse(info['data'])
            install_parameter = info['data']
            if install_kafka_cluster_monitor_flow(install_parameter):
                return JsonResponse({'result': True, 'data': [], 'code': 0, 'message': '监控部署任务已启动'})
            return JsonResponse({'result': False, 'data': [], 'code': 1, 'message': '监控部署任务失败'})
        except Exception as err:
            logger.error(f'创建集群监控失败:{err}')
            return JsonResponse({'result': False, 'data': [], 'message': f'{err}', 'code': 1})


forward

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

Class description:
Implement the GATLayer class.

Method signatures and docstrings:
- def __init__(self, c_in, c_out, edge_dim, num_heads, concat_heads=True, alpha=0.2): Inputs: c_in - Dimensionality of input features c_out - Dimensionality of output features num_heads - Number of heads, i.e. attention mechanisms to apply in parallel. The output features are equally split up over the heads if concat_heads=True. concat_heads - If True, the output of the different heads is concatenated instead of averaged. alpha - Negative slope of the LeakyReLU activation.
- def forward(self, node_feats, edge_feats, adj_matrix, print_attn_probs=False): Inputs: node_feats - Input features of the node. Shape: [batch_size, c_in] adj_matrix - Adjacency matrix including self-connections. Shape: [batch_size, num_nodes, num_nodes] print_attn_probs - If True, the attention weights are printed during the forward pass (for debugging purposes)

Implement the Python class `GATLayer` described below.

Class description:
Implement the GATLayer class.

Method signatures and docstrings:
- def __init__(self, c_in, c_out, edge_dim, num_heads, concat_heads=True, alpha=0.2): Inputs: c_in - Dimensionality of input features c_out - Dimensionality of output features num_heads - Number of heads, i.e. attention mechanisms to apply in parallel. The output features are equally split up over the heads if concat_heads=True. concat_heads - If True, the output of the different heads is concatenated instead of averaged. alpha - Negative slope of the LeakyReLU activation.
- def forward(self, node_feats, edge_feats, adj_matrix, print_attn_probs=False): Inputs: node_feats - Input features of the node. Shape: [batch_size, c_in] adj_matrix - Adjacency matrix including self-connections. Shape: [batch_size, num_nodes, num_nodes] print_attn_probs - If True, the attention weights are printed during the forward pass (for debugging purposes)

<|skeleton|>
class GATLayer:

    def __init__(self, c_in, c_out, edge_dim, num_heads, concat_heads=True, alpha=0.2):
        """Inputs: c_in - Dimensionality of input features c_out - Dimensionality of output features num_heads - Number of heads, i.e. attention mechanisms to apply in parallel. The output features are equally split up over the heads if concat_heads=True. concat_heads - If True, the output of the different heads is concatenated instead of averaged. alpha - Negative slope of the LeakyReLU activation."""
        <|body_0|>

    def forward(self, node_feats, edge_feats, adj_matrix, print_attn_probs=False):
        """Inputs: node_feats - Input features of the node. Shape: [batch_size, c_in] adj_matrix - Adjacency matrix including self-connections. Shape: [batch_size, num_nodes, num_nodes] print_attn_probs - If True, the attention weights are printed during the forward pass (for debugging purposes)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.num_heads = num_heads
        self.concat_heads = concat_heads
        if self.concat_heads:
            assert c_out % num_heads == 0, 'Number of output features must be a multiple of the count of heads.'
            c_out = c_out // num_heads
        if c_in != 0:
            self.node_projection = nn.Linear(c_in, c_out * num_heads)
            nn.init.xavier_uniform_(self.node_projection.weight.data, gain=1.414)
        self.edge_projection = nn.Linear(edge_dim, c_out * num_heads)
        if c_in != 0:
            self.a = nn.Parameter(torch.Tensor(num_heads, 3 * c_out))
        else:
            self.a = nn.Parameter(torch.Tensor(num_heads, c_out))
        self.leakyrelu = nn.LeakyReLU(alpha)
        nn.init.xavier_uniform_(self.edge_projection.weight.data, gain=1.414)
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
<|end_body_0|>

<|body_start_1|>
        batch_size, num_nodes = (edge_feats.size(0), adj_matrix.size(-1))
        if node_feats != None:
            node_feats = self.node_projection(node_feats)
            node_feats = node_feats.view(batch_size, num_nodes, self.num_heads, -1)
            node_feats_flat = node_feats.view(batch_size * num_nodes, self.num_heads, -1)
        edge_feats = self.edge_projection(edge_feats)
        edge_feats = edge_feats.view(batch_size, num_nodes, num_nodes, self.num_heads, -1)
        edges = adj_matrix.nonzero(as_tuple=False)
        edge_feats_flat = edge_feats.view(batch_size * num_nodes * num_nodes, self.num_heads, -1)
        edge_indices_row = edges[:, 0] * num_nodes + edges[:, 1]
        edge_indices_col = edges[:, 0] * num_nodes + edges[:, 2]
        if node_feats != None:
            a_input = torch.cat([torch.index_select(input=node_feats_flat, index=edge_indices_row, dim=0), torch.index_select(input=node_feats_flat, index=edge_indices_col, dim=0), edge_feats_flat], dim=-1)
        else:
            a_input = edge_feats_flat
        attn_logits = torch.einsum('bhc,hc->bh', a_input, self.a)
        attn_logits = self.leakyrelu(attn_logits)
        attn_matrix = attn_logits.new_zeros(adj_matrix.shape + (self.num_heads,)).fill_(-9000000000000000.0)
        attn_matrix[adj_matrix[..., None].repeat(1, 1, 1, self.num_heads) == 1] = attn_logits.reshape(-1)
        attn_probs = F.softmax(attn_matrix, dim=2)
        if print_attn_probs:
            print('Attention probs\n', attn_probs.permute(0, 3, 1, 2))
        values = node_feats + edge_feats if node_feats != None else edge_feats
        node_feats = torch.einsum('bijh,bijhc->bihc', attn_probs, values)
        if self.concat_heads:
            node_feats = node_feats.reshape(batch_size, num_nodes, -1)
        else:
            node_feats = node_feats.mean(dim=2)
        return node_feats
<|end_body_1|>

Solution:
class GATLayer:
    def __init__(self, c_in, c_out, edge_dim, num_heads, concat_heads=True, alpha=0.2):
        """Inputs: c_in - Dimensionality of input features c_out - Dimensionality of output features num_heads - Number of heads, i.e. attention mechanisms to apply in parallel. The output features are equally split up over the heads if concat_heads=True. concat_heads - If True, the output of the different heads is concatenated instead of averaged. alpha - Negative slope of the LeakyReLU activation."""
        super().__init__()
        self.num_heads = num_heads
        self.concat_heads = concat_heads
        if self.concat_heads:
            assert c_out % num_heads == 0, 'Number of output features must be a multiple of the count of heads.'
            c_out = c_out // num_heads
        if c_in != 0:
            self.node_projection = nn.Linear(c_in, c_out * num_heads)
            nn.init.xavier_uniform_(self.node_projection.weight.data, gain=1.414)
        self.edge_projection = nn.Linear(edge_dim, c_out * num_heads)
        if c_in != 0:
            self.a = nn.Parameter(torch.Tensor(num_heads, 3 * c_out))
        else:
            self.a = nn.Parameter(torch.Tensor(num_heads, c_out))
        self.leakyrelu = nn.LeakyReLU(alpha)
        nn.init.xavier_uniform_(self.edge_projection.weight.data, gain=1.414)
        nn.init.xavier_uniform_(self.a.data, gain=1.414)

    def forward(self, node_feats, edge_feats, adj_matrix, print_attn_probs=False):
        """Inputs: node_feats - Input features of the node. Shape: [batch_size, c_in] adj_matrix - Adjacency matrix including self-connections. Shape: [batch_size, num_nodes, num_nodes] print_attn_probs - If True, the attention weights are printed during the forward pass (for debugging purposes)"""
        batch_size, num_nodes = (edge_feats.size(0), adj_matrix.size(-1))
        if node_feats != None:
            node_feats = self.node_projection(node_feats)
            node_feats = node_feats.view(batch_size, num_nodes, self.num_heads, -1)
            node_feats_flat = node_feats.view(batch_size * num_nodes, self.num_heads, -1)
        edge_feats = self.edge_projection(edge_feats)
        edge_feats = edge_feats.view(batch_size, num_nodes, num_nodes, self.num_heads, -1)
        edges = adj_matrix.nonzero(as_tuple=False)
        edge_feats_flat = edge_feats.view(batch_size * num_nodes * num_nodes, self.num_heads, -1)
        edge_indices_row = edges[:, 0] * num_nodes + edges[:, 1]
        edge_indices_col = edges[:, 0] * num_nodes + edges[:, 2]
        if node_feats != None:
            a_input = torch.cat([torch.index_select(input=node_feats_flat, index=edge_indices_row, dim=0), torch.index_select(input=node_feats_flat, index=edge_indices_col, dim=0), edge_feats_flat], dim=-1)
        else:
            a_input = edge_feats_flat
        attn_logits = torch.einsum('bhc,hc->bh', a_input, self.a)
        attn_logits = self.leakyrelu(attn_logits)
        attn_matrix = attn_logits.new_zeros(adj_matrix.shape + (self.num_heads,)).fill_(-9000000000000000.0)
        attn_matrix[adj_matrix[..., None].repeat(1, 1, 1, self.num_heads) == 1] = attn_logits.reshape(-1)
        attn_probs = F.softmax(attn_matrix, dim=2)
        if print_attn_probs:
            print('Attention probs\n', attn_probs.permute(0, 3, 1, 2))
        values = node_feats + edge_feats if node_feats != None else edge_feats
        node_feats = torch.einsum('bijh,bijhc->bihc', attn_probs, values)
        if self.concat_heads:
            node_feats = node_feats.reshape(batch_size, num_nodes, -1)
        else:
            node_feats = node_feats.mean(dim=2)
        return node_feats


GenerateConfigData

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

Class description:
Generates configuration for a Python application.

Method signatures and docstrings:
- def __init__(self, path, params, runtime): Constructor. Args: path: (str) Root path of the source tree. params: (ext_runtime.Params) Parameters passed through to the fingerprinters. runtime: (str) The runtime name.
- def GenerateAppYaml(self, notify): Generate app.yaml. Args: notify: depending on whether we're in deploy, write messages to the user or to log. Returns: (bool) True if file was written Note: this is not a recommended use-case, python-compat users likely have an existing app.yaml. But users can still get here with the --runtime flag.
- def GenerateDockerfileData(self): Generates dockerfiles. Returns: list(ext_runtime.GeneratedFile) the list of generated dockerfiles
- def GenerateConfigs(self): Generate all config files for the module.
- def GenerateConfigData(self): Generate all config files for the module. Returns: list(ext_runtime.GeneratedFile) A list of the config files that were generated

Implement the Python class `PythonConfigurator` described below.

Class description:
Generates configuration for a Python application.

Method signatures and docstrings:
- def __init__(self, path, params, runtime): Constructor. Args: path: (str) Root path of the source tree. params: (ext_runtime.Params) Parameters passed through to the fingerprinters. runtime: (str) The runtime name.
- def GenerateAppYaml(self, notify): Generate app.yaml. Args: notify: depending on whether we're in deploy, write messages to the user or to log. Returns: (bool) True if file was written Note: this is not a recommended use-case, python-compat users likely have an existing app.yaml. But users can still get here with the --runtime flag.
- def GenerateDockerfileData(self): Generates dockerfiles. Returns: list(ext_runtime.GeneratedFile) the list of generated dockerfiles
- def GenerateConfigs(self): Generate all config files for the module.
- def GenerateConfigData(self): Generate all config files for the module. Returns: list(ext_runtime.GeneratedFile) A list of the config files that were generated

<|skeleton|>
class PythonConfigurator:
    """Generates configuration for a Python application."""

    def __init__(self, path, params, runtime):
        """Constructor. Args: path: (str) Root path of the source tree. params: (ext_runtime.Params) Parameters passed through to the fingerprinters. runtime: (str) The runtime name."""
        <|body_0|>

    def GenerateAppYaml(self, notify):
        """Generate app.yaml. Args: notify: depending on whether we're in deploy, write messages to the user or to log. Returns: (bool) True if file was written Note: this is not a recommended use-case, python-compat users likely have an existing app.yaml. But users can still get here with the --runtime flag."""
        <|body_1|>

    def GenerateDockerfileData(self):
        """Generates dockerfiles. Returns: list(ext_runtime.GeneratedFile) the list of generated dockerfiles"""
        <|body_2|>

    def GenerateConfigs(self):
        """Generate all config files for the module."""
        <|body_3|>

    def GenerateConfigData(self):
        """Generate all config files for the module. Returns: list(ext_runtime.GeneratedFile) A list of the config files that were generated"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.root = path
        self.params = params
        self.runtime = runtime
<|end_body_0|>

<|body_start_1|>
        if not self.params.appinfo:
            app_yaml = os.path.join(self.root, 'app.yaml')
            if not os.path.exists(app_yaml):
                notify('Writing [app.yaml] to [%s].' % self.root)
                runtime = 'custom' if self.params.custom else self.runtime
                with open(app_yaml, 'w') as f:
                    f.write(PYTHON_APP_YAML.format(runtime=runtime))
                log.warn(APP_YAML_WARNING)
                return True
        return False
<|end_body_1|>

<|body_start_2|>
        if self.runtime == 'python-compat':
            dockerfile_preamble = COMPAT_DOCKERFILE_PREAMBLE
        else:
            dockerfile_preamble = PYTHON27_DOCKERFILE_PREAMBLE
        all_config_files = []
        dockerfile_name = config.DOCKERFILE
        dockerfile_components = [dockerfile_preamble, DOCKERFILE_INSTALL_APP]
        if self.runtime == 'python-compat':
            dockerfile_components.append(DOCKERFILE_INSTALL_REQUIREMENTS_TXT)
        dockerfile_contents = ''.join((c for c in dockerfile_components))
        dockerfile = ext_runtime.GeneratedFile(dockerfile_name, dockerfile_contents)
        all_config_files.append(dockerfile)
        dockerignore = ext_runtime.GeneratedFile('.dockerignore', DOCKERIGNORE)
        all_config_files.append(dockerignore)
        return all_config_files
<|end_body_2|>

<|body_start_3|>
        notify = log.info if self.params.deploy else log.status.Print
        self.GenerateAppYaml(notify)
        created = False
        if self.params.custom or self.params.deploy:
            dockerfiles = self.GenerateDockerfileData()
            for dockerfile in dockerfiles:
                if dockerfile.WriteTo(self.root, notify):
                    created = True
            if not created:
                notify('All config files already exist, not generating anything.')
        return created
<|end_body_3|>

<|body_start_4|>
        notify = log.info if self.params.deploy else log.status.Print
        self.GenerateAppYaml(notify)
        if not (self.params.custom or self.params.deploy):
            return []
        all_config_files = self.GenerateDockerfileData()
        return [f for f in all_config_files if not os.path.exists(os.path.join(self.root, f.filename))]
<|end_body_4|>

Solution:
class PythonConfigurator:
    """Generates configuration for a Python application."""

    def __init__(self, path, params, runtime):
        """Constructor. Args: path: (str) Root path of the source tree. params: (ext_runtime.Params) Parameters passed through to the fingerprinters. runtime: (str) The runtime name."""
        self.root = path
        self.params = params
        self.runtime = runtime

    def GenerateAppYaml(self, notify):
        """Generate app.yaml. Args: notify: depending on whether we're in deploy, write messages to the user or to log. Returns: (bool) True if file was written Note: this is not a recommended use-case, python-compat users likely have an existing app.yaml. But users can still get here with the --runtime flag."""
        if not self.params.appinfo:
            app_yaml = os.path.join(self.root, 'app.yaml')
            if not os.path.exists(app_yaml):
                notify('Writing [app.yaml] to [%s].' % self.root)
                runtime = 'custom' if self.params.custom else self.runtime
                with open(app_yaml, 'w') as f:
                    f.write(PYTHON_APP_YAML.format(runtime=runtime))
                log.warn(APP_YAML_WARNING)
                return True
        return False

    def GenerateDockerfileData(self):
        """Generates dockerfiles. Returns: list(ext_runtime.GeneratedFile) the list of generated dockerfiles"""
        if self.runtime == 'python-compat':
            dockerfile_preamble = COMPAT_DOCKERFILE_PREAMBLE
        else:
            dockerfile_preamble = PYTHON27_DOCKERFILE_PREAMBLE
        all_config_files = []
        dockerfile_name = config.DOCKERFILE
        dockerfile_components = [dockerfile_preamble, DOCKERFILE_INSTALL_APP]
        if self.runtime == 'python-compat':
            dockerfile_components.append(DOCKERFILE_INSTALL_REQUIREMENTS_TXT)
        dockerfile_contents = ''.join((c for c in dockerfile_components))
        dockerfile = ext_runtime.GeneratedFile(dockerfile_name, dockerfile_contents)
        all_config_files.append(dockerfile)
        dockerignore = ext_runtime.GeneratedFile('.dockerignore', DOCKERIGNORE)
        all_config_files.append(dockerignore)
        return all_config_files

    def GenerateConfigs(self):
        """Generate all config files for the module."""
        notify = log.info if self.params.deploy else log.status.Print
        self.GenerateAppYaml(notify)
        created = False
        if self.params.custom or self.params.deploy:
            dockerfiles = self.GenerateDockerfileData()
            for dockerfile in dockerfiles:
                if dockerfile.WriteTo(self.root, notify):
                    created = True
            if not created:
                notify('All config files already exist, not generating anything.')
        return created

    def GenerateConfigData(self):
        """Generate all config files for the module. Returns: list(ext_runtime.GeneratedFile) A list of the config files that were generated"""
        notify = log.info if self.params.deploy else log.status.Print
        self.GenerateAppYaml(notify)
        if not (self.params.custom or self.params.deploy):
            return []
        all_config_files = self.GenerateDockerfileData()
        return [f for f in all_config_files if not os.path.exists(os.path.join(self.root, f.filename))]


charge

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

Class description:
Electrical Storage System, holds charges of backup power

Method signatures and docstrings:
- def __init__(self) -> None: Constructor for electrical storage system
- def discharge(self, w) -> int: Discharge w Watts from the battery :param w: Watts to remove from the battery :type w: float :return: 1 if the battery was successfully discharged, 0 otherwise
- def charge(self, w) -> int: Add w Watts to the battery :param w: Watts to add to the battery :type w: float :return: 1 if the battery was successfully charged, 0 otherwise
- def current_charge(self, step=None) -> int: Returns the charge of the battery, either at the current step or at a specified step :param step: Step to evaluate. Default None :type step: int :return: Charge of electrical system at step

Implement the Python class `ElectricalStorage` described below.

Class description:
Electrical Storage System, holds charges of backup power

Method signatures and docstrings:
- def __init__(self) -> None: Constructor for electrical storage system
- def discharge(self, w) -> int: Discharge w Watts from the battery :param w: Watts to remove from the battery :type w: float :return: 1 if the battery was successfully discharged, 0 otherwise
- def charge(self, w) -> int: Add w Watts to the battery :param w: Watts to add to the battery :type w: float :return: 1 if the battery was successfully charged, 0 otherwise
- def current_charge(self, step=None) -> int: Returns the charge of the battery, either at the current step or at a specified step :param step: Step to evaluate. Default None :type step: int :return: Charge of electrical system at step

<|skeleton|>
class ElectricalStorage:
    """Electrical Storage System, holds charges of backup power"""

    def __init__(self) -> None:
        """Constructor for electrical storage system"""
        <|body_0|>

    def discharge(self, w) -> int:
        """Discharge w Watts from the battery :param w: Watts to remove from the battery :type w: float :return: 1 if the battery was successfully discharged, 0 otherwise"""
        <|body_1|>

    def charge(self, w) -> int:
        """Add w Watts to the battery :param w: Watts to add to the battery :type w: float :return: 1 if the battery was successfully charged, 0 otherwise"""
        <|body_2|>

    def current_charge(self, step=None) -> int:
        """Returns the charge of the battery, either at the current step or at a specified step :param step: Step to evaluate. Default None :type step: int :return: Charge of electrical system at step"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self._amps = 415
        self._voltage = 12
        self._max_capacity = self._amps * self._voltage * 3600
        self._current_capacity = float(0)
        self._charge_history = list()
<|end_body_0|>

<|body_start_1|>
        if self._current_capacity - w < 0:
            self._charge_history.append(self._current_capacity)
            return 0
        else:
            self._current_capacity -= w
            self._charge_history.append(self._current_capacity)
            return 1
<|end_body_1|>

<|body_start_2|>
        if self._current_capacity + w >= self._max_capacity:
            self._charge_history.append(self._current_capacity)
            return 0
        else:
            self._current_capacity += w
            self._charge_history.append(self._current_capacity)
            return 1
<|end_body_2|>

<|body_start_3|>
        if step is not None:
            return self._charge_history[int(step)]
        return self._current_capacity
<|end_body_3|>

Solution:
class ElectricalStorage:
    """Electrical Storage System, holds charges of backup power"""

    def __init__(self) -> None:
        """Constructor for electrical storage system"""
        self._amps = 415
        self._voltage = 12
        self._max_capacity = self._amps * self._voltage * 3600
        self._current_capacity = float(0)
        self._charge_history = list()

    def discharge(self, w) -> int:
        """Discharge w Watts from the battery :param w: Watts to remove from the battery :type w: float :return: 1 if the battery was successfully discharged, 0 otherwise"""
        if self._current_capacity - w < 0:
            self._charge_history.append(self._current_capacity)
            return 0
        else:
            self._current_capacity -= w
            self._charge_history.append(self._current_capacity)
            return 1

    def charge(self, w) -> int:
        """Add w Watts to the battery :param w: Watts to add to the battery :type w: float :return: 1 if the battery was successfully charged, 0 otherwise"""
        if self._current_capacity + w >= self._max_capacity:
            self._charge_history.append(self._current_capacity)
            return 0
        else:
            self._current_capacity += w
            self._charge_history.append(self._current_capacity)
            return 1

    def current_charge(self, step=None) -> int:
        """Returns the charge of the battery, either at the current step or at a specified step :param step: Step to evaluate. Default None :type step: int :return: Charge of electrical system at step"""
        if step is not None:
            return self._charge_history[int(step)]
        return self._current_capacity


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

Class description:
Implement the add_env class.

Method signatures and docstrings:
- def get(self, requests): 获取添加环境页面
- def post(self, requests): 添加环境

Implement the Python class `add_env` described below.

Class description:
Implement the add_env class.

Method signatures and docstrings:
- def get(self, requests): 获取添加环境页面
- def post(self, requests): 添加环境

<|skeleton|>
class add_env:

    def get(self, requests):
        """获取添加环境页面"""
        <|body_0|>

    def post(self, requests):
        """添加环境"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        pro_lists = Project.objects.all()
        content = {'pro_lists': pro_lists}
        return render(requests, 'project_env/env_add.html', content)
<|end_body_0|>

<|body_start_1|>
        logger.info('环境添加的参数:%s' % requests.POST)
        env_url = requests.POST.get('env_url')
        env = Environment.objects.filter(url=env_url)
        env_name = requests.POST.get('env_name')
        description = requests.POST.get('description')
        prj_id = requests.POST.get('prj_id')
        pro_lists = Project.objects.filter(prj_id=prj_id)
        env_status = requests.POST.get('env_status')
        if env:
            messages.error(requests, '环境地址host重复了')
        else:
            envs = Environment(env_name=env_name, url=env_url, description=description, status=env_status, project=pro_lists[0])
            envs.save()
            logger.info('新增环境完成！！！')
            return redirect(reverse('project_env:get_env', kwargs={'page_num': 1}))
        return render(requests, 'project_env/env_add.html', {'env_name': env_name, 'description': description, 'pro_lists': pro_lists, 'env_url': env_url, 'env_status': env_status, 'message': '该环境地址库中已经存在'})
<|end_body_1|>

Solution:
class add_env:
    def get(self, requests):
        """获取添加环境页面"""
        pro_lists = Project.objects.all()
        content = {'pro_lists': pro_lists}
        return render(requests, 'project_env/env_add.html', content)

    def post(self, requests):
        """添加环境"""
        logger.info('环境添加的参数:%s' % requests.POST)
        env_url = requests.POST.get('env_url')
        env = Environment.objects.filter(url=env_url)
        env_name = requests.POST.get('env_name')
        description = requests.POST.get('description')
        prj_id = requests.POST.get('prj_id')
        pro_lists = Project.objects.filter(prj_id=prj_id)
        env_status = requests.POST.get('env_status')
        if env:
            messages.error(requests, '环境地址host重复了')
        else:
            envs = Environment(env_name=env_name, url=env_url, description=description, status=env_status, project=pro_lists[0])
            envs.save()
            logger.info('新增环境完成！！！')
            return redirect(reverse('project_env:get_env', kwargs={'page_num': 1}))
        return render(requests, 'project_env/env_add.html', {'env_name': env_name, 'description': description, 'pro_lists': pro_lists, 'env_url': env_url, 'env_status': env_status, 'message': '该环境地址库中已经存在'})


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

Class description:
Class for backend conversations

Method signatures and docstrings:
- def create_windows(self): Create windows for this conversation
- def get_name(self): Get the name of the conversation, depending on type
- def get_key(self): Get a key for sorting this conversation
- def send_msg(self, msg): Send message coming from the UI/input window

Implement the Python class `BackendConversation` described below.

Class description:
Class for backend conversations

Method signatures and docstrings:
- def create_windows(self): Create windows for this conversation
- def get_name(self): Get the name of the conversation, depending on type
- def get_key(self): Get a key for sorting this conversation
- def send_msg(self, msg): Send message coming from the UI/input window

<|skeleton|>
class BackendConversation:
    """Class for backend conversations"""

    def create_windows(self):
        """Create windows for this conversation"""
        <|body_0|>

    def get_name(self):
        """Get the name of the conversation, depending on type"""
        <|body_1|>

    def get_key(self):
        """Get a key for sorting this conversation"""
        <|body_2|>

    def send_msg(self, msg):
        """Send message coming from the UI/input window"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        log_title = 'Command log of {0}'.format(self.name)
        input_title = 'Command to {0}'.format(self.name)
        log_config = nuqql.config.get('log_win')
        self.wins.log_win = nuqql.win.LogWin(log_config, self, log_title)
        self.wins.log_win.list = self.history.log
        input_config = nuqql.config.get('input_win')
        self.wins.input_win = nuqql.win.InputWin(input_config, self, input_title)
<|end_body_0|>

<|body_start_1|>
        if self.notification > 0:
            notify = '# '
        else:
            notify = ''
        return '{0}{{backend}} {1}'.format(notify, self.name)
<|end_body_1|>

<|body_start_2|>
        sort_notify = 0 - self.notification
        sort_type = 0
        sort_status = 0
        sort_name = self.name
        sort_type = 1
        return (sort_notify, sort_type, sort_status, sort_name)
<|end_body_2|>

<|body_start_3|>
        tstamp = datetime.datetime.now()
        log_msg = nuqql.history.LogMessage(tstamp, 'you', msg, own=True)
        self.wins.log_win.add(log_msg)
        if self.backend is not None:
            self.backend.client.send_command(msg)
<|end_body_3|>

Solution:
class BackendConversation:
    """Class for backend conversations"""

    def create_windows(self):
        """Create windows for this conversation"""
        log_title = 'Command log of {0}'.format(self.name)
        input_title = 'Command to {0}'.format(self.name)
        log_config = nuqql.config.get('log_win')
        self.wins.log_win = nuqql.win.LogWin(log_config, self, log_title)
        self.wins.log_win.list = self.history.log
        input_config = nuqql.config.get('input_win')
        self.wins.input_win = nuqql.win.InputWin(input_config, self, input_title)

    def get_name(self):
        """Get the name of the conversation, depending on type"""
        if self.notification > 0:
            notify = '# '
        else:
            notify = ''
        return '{0}{{backend}} {1}'.format(notify, self.name)

    def get_key(self):
        """Get a key for sorting this conversation"""
        sort_notify = 0 - self.notification
        sort_type = 0
        sort_status = 0
        sort_name = self.name
        sort_type = 1
        return (sort_notify, sort_type, sort_status, sort_name)

    def send_msg(self, msg):
        """Send message coming from the UI/input window"""
        tstamp = datetime.datetime.now()
        log_msg = nuqql.history.LogMessage(tstamp, 'you', msg, own=True)
        self.wins.log_win.add(log_msg)
        if self.backend is not None:
            self.backend.client.send_command(msg)


accuracy

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

Class description:
Implement the ClassifierModel class.

Method signatures and docstrings:
- def classify(self, input_datum_or_data): Classifies a datum or each datum in a list of data. Args: input_datum_or_data: a 1-dimensional np.array of a single datum or a 2-dimensional np.array of data where each row is a datum. Returns: An integer (representing a label) if a single datum is passed in, or a list of integers (representing the labels) if multiple data is passed in.
- def accuracy(self, input_data, target_data): Computes the accuracy of the model classification predictions. Args: input_data: a 2-dimensional np.array of input data where each row is a datum. target_data: a 2-dimensional np.array of correct labels where each row is a probability distribution over the labels (or alternatively, a one-hot vector representation of the label). Returns: A float, the accuracy of the model for the given data.

Implement the Python class `ClassifierModel` described below.

Class description:
Implement the ClassifierModel class.

Method signatures and docstrings:
- def classify(self, input_datum_or_data): Classifies a datum or each datum in a list of data. Args: input_datum_or_data: a 1-dimensional np.array of a single datum or a 2-dimensional np.array of data where each row is a datum. Returns: An integer (representing a label) if a single datum is passed in, or a list of integers (representing the labels) if multiple data is passed in.
- def accuracy(self, input_data, target_data): Computes the accuracy of the model classification predictions. Args: input_data: a 2-dimensional np.array of input data where each row is a datum. target_data: a 2-dimensional np.array of correct labels where each row is a probability distribution over the labels (or alternatively, a one-hot vector representation of the label). Returns: A float, the accuracy of the model for the given data.

<|skeleton|>
class ClassifierModel:

    def classify(self, input_datum_or_data):
        """Classifies a datum or each datum in a list of data. Args: input_datum_or_data: a 1-dimensional np.array of a single datum or a 2-dimensional np.array of data where each row is a datum. Returns: An integer (representing a label) if a single datum is passed in, or a list of integers (representing the labels) if multiple data is passed in."""
        <|body_0|>

    def accuracy(self, input_data, target_data):
        """Computes the accuracy of the model classification predictions. Args: input_data: a 2-dimensional np.array of input data where each row is a datum. target_data: a 2-dimensional np.array of correct labels where each row is a probability distribution over the labels (or alternatively, a one-hot vector representation of the label). Returns: A float, the accuracy of the model for the given data."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        prediction = self.predict(input_datum_or_data)
        category = np.argmax(prediction, axis=-1)
        return category
<|end_body_0|>

<|body_start_1|>
        category_labels = np.argmax(target_data, axis=-1)
        correct_prediction = self.classify(input_data) == category_labels
        accuracy = correct_prediction.mean()
        return accuracy
<|end_body_1|>

Solution:
class ClassifierModel:
    def classify(self, input_datum_or_data):
        """Classifies a datum or each datum in a list of data. Args: input_datum_or_data: a 1-dimensional np.array of a single datum or a 2-dimensional np.array of data where each row is a datum. Returns: An integer (representing a label) if a single datum is passed in, or a list of integers (representing the labels) if multiple data is passed in."""
        prediction = self.predict(input_datum_or_data)
        category = np.argmax(prediction, axis=-1)
        return category

    def accuracy(self, input_data, target_data):
        """Computes the accuracy of the model classification predictions. Args: input_data: a 2-dimensional np.array of input data where each row is a datum. target_data: a 2-dimensional np.array of correct labels where each row is a probability distribution over the labels (or alternatively, a one-hot vector representation of the label). Returns: A float, the accuracy of the model for the given data."""
        category_labels = np.argmax(target_data, axis=-1)
        correct_prediction = self.classify(input_data) == category_labels
        accuracy = correct_prediction.mean()
        return accuracy


traceCourbe

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

Class description:
Canevas spécialisé pour dessiner des courbes élongation/temps

Method signatures and docstrings:
- def __init__(self, boss=None, larg=200, haut=150): Constructeur du graphique : Axes et échelle horiz.
- def traceCourbe(self, freq=1, phase=0, ampl=10, coul='red'): Tracé d'un graphique élongation/temps sur 1 seconde

Implement the Python class `OscilloGraphe` described below.

Class description:
Canevas spécialisé pour dessiner des courbes élongation/temps

Method signatures and docstrings:
- def __init__(self, boss=None, larg=200, haut=150): Constructeur du graphique : Axes et échelle horiz.
- def traceCourbe(self, freq=1, phase=0, ampl=10, coul='red'): Tracé d'un graphique élongation/temps sur 1 seconde

<|skeleton|>
class OscilloGraphe:
    """Canevas spécialisé pour dessiner des courbes élongation/temps"""

    def __init__(self, boss=None, larg=200, haut=150):
        """Constructeur du graphique : Axes et échelle horiz."""
        <|body_0|>

    def traceCourbe(self, freq=1, phase=0, ampl=10, coul='red'):
        """Tracé d'un graphique élongation/temps sur 1 seconde"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Canvas.__init__(self)
        self.configure(width=larg, height=haut)
        self.larg, self.haut = (larg, haut)
        self.create_line(10, haut / 2, larg, haut / 2, arrow=LAST)
        self.create_line(10, haut - 5, 10, 5, arrow=LAST)
        pas = (larg - 25) / 8.0
        for t in range(1, 9):
            stx = 10 + t * pas
            self.create_line(stx, haut / 2 - 4, stx, haut / 2 + 4)
<|end_body_0|>

<|body_start_1|>
        curve = []
        pas = (self.larg - 25) / 1000.0
        for t in range(0, 1001, 5):
            e = ampl * sin(2 * pi * freq * t / 1000 - phase)
            x = 10 + t * pas
            y = self.haut / 2 - e * self.haut / 25
            curve.append((x, y))
        n = self.create_line(curve, fill=coul, smooth=1)
<|end_body_1|>

Solution:
class OscilloGraphe:
    """Canevas spécialisé pour dessiner des courbes élongation/temps"""

    def __init__(self, boss=None, larg=200, haut=150):
        """Constructeur du graphique : Axes et échelle horiz."""
        Canvas.__init__(self)
        self.configure(width=larg, height=haut)
        self.larg, self.haut = (larg, haut)
        self.create_line(10, haut / 2, larg, haut / 2, arrow=LAST)
        self.create_line(10, haut - 5, 10, 5, arrow=LAST)
        pas = (larg - 25) / 8.0
        for t in range(1, 9):
            stx = 10 + t * pas
            self.create_line(stx, haut / 2 - 4, stx, haut / 2 + 4)

    def traceCourbe(self, freq=1, phase=0, ampl=10, coul='red'):
        """Tracé d'un graphique élongation/temps sur 1 seconde"""
        curve = []
        pas = (self.larg - 25) / 1000.0
        for t in range(0, 1001, 5):
            e = ampl * sin(2 * pi * freq * t / 1000 - phase)
            x = 10 + t * pas
            y = self.haut / 2 - e * self.haut / 25
            curve.append((x, y))
        n = self.create_line(curve, fill=coul, smooth=1)


url

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

Class description:
SearchResultProcessor for LMS Search

Method signatures and docstrings:
- def get_course_key(self): fetch course key object from string representation - retain result for subsequent uses
- def get_usage_key(self): fetch usage key for component from string representation - retain result for subsequent uses
- def get_module_store(self): module store accessor - retain result for subsequent uses
- def get_course_blocks(self, user): fetch cached blocks for course - retain for subsequent use
- def url(self): Property to display the url for the given location, useful for allowing navigation
- def should_remove(self, user): Test to see if this result should be removed due to access restriction

Implement the Python class `LmsSearchResultProcessor` described below.

Class description:
SearchResultProcessor for LMS Search

Method signatures and docstrings:
- def get_course_key(self): fetch course key object from string representation - retain result for subsequent uses
- def get_usage_key(self): fetch usage key for component from string representation - retain result for subsequent uses
- def get_module_store(self): module store accessor - retain result for subsequent uses
- def get_course_blocks(self, user): fetch cached blocks for course - retain for subsequent use
- def url(self): Property to display the url for the given location, useful for allowing navigation
- def should_remove(self, user): Test to see if this result should be removed due to access restriction

<|skeleton|>
class LmsSearchResultProcessor:
    """SearchResultProcessor for LMS Search"""

    def get_course_key(self):
        """fetch course key object from string representation - retain result for subsequent uses"""
        <|body_0|>

    def get_usage_key(self):
        """fetch usage key for component from string representation - retain result for subsequent uses"""
        <|body_1|>

    def get_module_store(self):
        """module store accessor - retain result for subsequent uses"""
        <|body_2|>

    def get_course_blocks(self, user):
        """fetch cached blocks for course - retain for subsequent use"""
        <|body_3|>

    def url(self):
        """Property to display the url for the given location, useful for allowing navigation"""
        <|body_4|>

    def should_remove(self, user):
        """Test to see if this result should be removed due to access restriction"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        if self._course_key is None:
            self._course_key = CourseKey.from_string(self._results_fields['course'])
        return self._course_key
<|end_body_0|>

<|body_start_1|>
        if self._usage_key is None:
            usage_key = UsageKey.from_string(self._results_fields['id'])
            self._usage_key = usage_key.map_into_course(self.get_course_key())
        return self._usage_key
<|end_body_1|>

<|body_start_2|>
        if self._module_store is None:
            self._module_store = modulestore()
        return self._module_store
<|end_body_2|>

<|body_start_3|>
        course_key = self.get_course_key()
        if course_key not in self._course_blocks:
            root_block_usage_key = self.get_module_store().make_course_usage_key(course_key)
            self._course_blocks[course_key] = get_course_blocks(user, root_block_usage_key)
        return self._course_blocks[course_key]
<|end_body_3|>

<|body_start_4|>
        if 'course' not in self._results_fields or 'id' not in self._results_fields:
            raise ValueError('Must have course and id in order to build url')
        return reverse('jump_to', kwargs={'course_id': self._results_fields['course'], 'location': self._results_fields['id']})
<|end_body_4|>

<|body_start_5|>
        if has_access(user, 'staff', self.get_course_key()):
            return False
        return self.get_usage_key() not in self.get_course_blocks(user).get_block_keys()
<|end_body_5|>

Solution:
class LmsSearchResultProcessor:
    """SearchResultProcessor for LMS Search"""

    def get_course_key(self):
        """fetch course key object from string representation - retain result for subsequent uses"""
        if self._course_key is None:
            self._course_key = CourseKey.from_string(self._results_fields['course'])
        return self._course_key

    def get_usage_key(self):
        """fetch usage key for component from string representation - retain result for subsequent uses"""
        if self._usage_key is None:
            usage_key = UsageKey.from_string(self._results_fields['id'])
            self._usage_key = usage_key.map_into_course(self.get_course_key())
        return self._usage_key

    def get_module_store(self):
        """module store accessor - retain result for subsequent uses"""
        if self._module_store is None:
            self._module_store = modulestore()
        return self._module_store

    def get_course_blocks(self, user):
        """fetch cached blocks for course - retain for subsequent use"""
        course_key = self.get_course_key()
        if course_key not in self._course_blocks:
            root_block_usage_key = self.get_module_store().make_course_usage_key(course_key)
            self._course_blocks[course_key] = get_course_blocks(user, root_block_usage_key)
        return self._course_blocks[course_key]

    def url(self):
        """Property to display the url for the given location, useful for allowing navigation"""
        if 'course' not in self._results_fields or 'id' not in self._results_fields:
            raise ValueError('Must have course and id in order to build url')
        return reverse('jump_to', kwargs={'course_id': self._results_fields['course'], 'location': self._results_fields['id']})

    def should_remove(self, user):
        """Test to see if this result should be removed due to access restriction"""
        if has_access(user, 'staff', self.get_course_key()):
            return False
        return self.get_usage_key() not in self.get_course_blocks(user).get_block_keys()


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

Class description:
The UI for the music module

Method signatures and docstrings:
- def __init__(self, parent): Create a new UI for the module Args: parent: A tk or ttk object
- def update_keys(self): Updates the Google API key with the text value

Implement the Python class `ModuleUIFrame` described below.

Class description:
The UI for the music module

Method signatures and docstrings:
- def __init__(self, parent): Create a new UI for the module Args: parent: A tk or ttk object
- def update_keys(self): Updates the Google API key with the text value

<|skeleton|>
class ModuleUIFrame:
    """The UI for the music module"""

    def __init__(self, parent):
        """Create a new UI for the module Args: parent: A tk or ttk object"""
        <|body_0|>

    def update_keys(self):
        """Updates the Google API key with the text value"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(ModuleUIFrame, self).__init__(parent)
        self.columnconfigure(0, weight=1)
        self.rowconfigure(1, weight=1)
        from ....datatools import get_data
        data = get_data()
        api_frame = ttk.LabelFrame(self, padding=8, text='Google API')
        api_frame.grid(row=0, column=0, sticky='W E N S')
        api_frame.columnconfigure(0, weight=1)
        self.google_api_key = tk.StringVar()
        ttk.Label(api_frame, text='Google API Key').grid(column=0, row=0, sticky='W E N S')
        ttk.Entry(api_frame, textvariable=self.google_api_key).grid(column=0, row=1, padx=0, pady=4, sticky='W E N S')
        self.soundcloud_client_id = tk.StringVar()
        ttk.Label(api_frame, text='SoundCloud Client ID').grid(column=0, row=2, sticky='W E N S')
        ttk.Entry(api_frame, textvariable=self.soundcloud_client_id).grid(column=0, row=3, padx=0, pady=4, sticky='W E N S')
        ttk.Button(api_frame, command=lambda: self.update_keys(), text='Update API Data').grid(column=0, row=4, padx=0, pady=4, sticky='W E N S')
        if 'google_api_key' in data['discord']['keys']:
            self.google_api_key.set(data['discord']['keys']['google_api_key'])
        if 'soundcloud_client_id' in data['discord']['keys']:
            self.soundcloud_client_id.set(data['discord']['keys']['soundcloud_client_id'])
<|end_body_0|>

<|body_start_1|>
        from ...main import add_api_key
        add_api_key('google_api_key', self.google_api_key.get())
        add_api_key('soundcloud_client_id', self.soundcloud_client_id.get())
<|end_body_1|>

Solution:
class ModuleUIFrame:
    """The UI for the music module"""

    def __init__(self, parent):
        """Create a new UI for the module Args: parent: A tk or ttk object"""
        super(ModuleUIFrame, self).__init__(parent)
        self.columnconfigure(0, weight=1)
        self.rowconfigure(1, weight=1)
        from ....datatools import get_data
        data = get_data()
        api_frame = ttk.LabelFrame(self, padding=8, text='Google API')
        api_frame.grid(row=0, column=0, sticky='W E N S')
        api_frame.columnconfigure(0, weight=1)
        self.google_api_key = tk.StringVar()
        ttk.Label(api_frame, text='Google API Key').grid(column=0, row=0, sticky='W E N S')
        ttk.Entry(api_frame, textvariable=self.google_api_key).grid(column=0, row=1, padx=0, pady=4, sticky='W E N S')
        self.soundcloud_client_id = tk.StringVar()
        ttk.Label(api_frame, text='SoundCloud Client ID').grid(column=0, row=2, sticky='W E N S')
        ttk.Entry(api_frame, textvariable=self.soundcloud_client_id).grid(column=0, row=3, padx=0, pady=4, sticky='W E N S')
        ttk.Button(api_frame, command=lambda: self.update_keys(), text='Update API Data').grid(column=0, row=4, padx=0, pady=4, sticky='W E N S')
        if 'google_api_key' in data['discord']['keys']:
            self.google_api_key.set(data['discord']['keys']['google_api_key'])
        if 'soundcloud_client_id' in data['discord']['keys']:
            self.soundcloud_client_id.set(data['discord']['keys']['soundcloud_client_id'])

    def update_keys(self):
        """Updates the Google API key with the text value"""
        from ...main import add_api_key
        add_api_key('google_api_key', self.google_api_key.get())
        add_api_key('soundcloud_client_id', self.soundcloud_client_id.get())


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

Class description:
CustomerListner

Method signatures and docstrings:
- def on_get(self, req, res, _id): handle GET requests
- def on_delete(self, req, res, _id): handle DELETE requests
- def on_put(self, req, res, _id): handle PUT requests

Implement the Python class `CustomerListener` described below.

Class description:
CustomerListner

Method signatures and docstrings:
- def on_get(self, req, res, _id): handle GET requests
- def on_delete(self, req, res, _id): handle DELETE requests
- def on_put(self, req, res, _id): handle PUT requests

<|skeleton|>
class CustomerListener:
    """CustomerListner"""

    def on_get(self, req, res, _id):
        """handle GET requests"""
        <|body_0|>

    def on_delete(self, req, res, _id):
        """handle DELETE requests"""
        <|body_1|>

    def on_put(self, req, res, _id):
        """handle PUT requests"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        customer = Customer()
        customer.id = _id
        customer = CustomerHelper.get_detail(customer)
        req.context['result'] = {'data': customer.to_dict(), 'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200
<|end_body_0|>

<|body_start_1|>
        customer = Customer()
        customer.id = _id
        CustomerHelper.delete(customer)
        req.context['result'] = {'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200
<|end_body_1|>

<|body_start_2|>
        customer = CustomerHelper.parse_from_query_string_request(req)
        customer.id = _id
        CustomerHelper.update(customer)
        req.context['result'] = {'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200
<|end_body_2|>

Solution:
class CustomerListener:
    """CustomerListner"""

    def on_get(self, req, res, _id):
        """handle GET requests"""
        customer = Customer()
        customer.id = _id
        customer = CustomerHelper.get_detail(customer)
        req.context['result'] = {'data': customer.to_dict(), 'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200

    def on_delete(self, req, res, _id):
        """handle DELETE requests"""
        customer = Customer()
        customer.id = _id
        CustomerHelper.delete(customer)
        req.context['result'] = {'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200

    def on_put(self, req, res, _id):
        """handle PUT requests"""
        customer = CustomerHelper.parse_from_query_string_request(req)
        customer.id = _id
        CustomerHelper.update(customer)
        req.context['result'] = {'status': {'code': 200, 'message': 'success'}}
        res.status = falcon.HTTP_200


maxNumber

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxNumber(self, nums1, nums2, k): :type nums1: List[int] :type nums2: List[int] :type k: int :rtype: List[int]
- def get_max_subarray(self, nums, k): A method to get the max subarray while preserve the related position in nums

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxNumber(self, nums1, nums2, k): :type nums1: List[int] :type nums2: List[int] :type k: int :rtype: List[int]
- def get_max_subarray(self, nums, k): A method to get the max subarray while preserve the related position in nums

<|skeleton|>
class Solution:

    def maxNumber(self, nums1, nums2, k):
        """:type nums1: List[int] :type nums2: List[int] :type k: int :rtype: List[int]"""
        <|body_0|>

    def get_max_subarray(self, nums, k):
        """A method to get the max subarray while preserve the related position in nums"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        len1, len2 = (len(nums1), len(nums2))
        res = [0] * k
        for i in xrange(max(0, k - len2), min(k, len1) + 1):
            subarray1 = self.get_max_subarray(nums1, i)
            subarray2 = self.get_max_subarray(nums2, k - i)
            res = max(res, [max(subarray1, subarray2).pop(0) for _ in xrange(k)])
        return res
<|end_body_0|>

<|body_start_1|>
        res = [0] * k
        cur = 0
        for i in xrange(len(nums)):
            while cur > 0 and cur + len(nums) - i > k and (nums[i] > res[cur - 1]):
                cur -= 1
            if cur < k:
                res[cur] = nums[i]
                cur += 1
        return res
<|end_body_1|>

Solution:
class Solution:
    def maxNumber(self, nums1, nums2, k):
        """:type nums1: List[int] :type nums2: List[int] :type k: int :rtype: List[int]"""
        len1, len2 = (len(nums1), len(nums2))
        res = [0] * k
        for i in xrange(max(0, k - len2), min(k, len1) + 1):
            subarray1 = self.get_max_subarray(nums1, i)
            subarray2 = self.get_max_subarray(nums2, k - i)
            res = max(res, [max(subarray1, subarray2).pop(0) for _ in xrange(k)])
        return res

    def get_max_subarray(self, nums, k):
        """A method to get the max subarray while preserve the related position in nums"""
        res = [0] * k
        cur = 0
        for i in xrange(len(nums)):
            while cur > 0 and cur + len(nums) - i > k and (nums[i] > res[cur - 1]):
                cur -= 1
            if cur < k:
                res[cur] = nums[i]
                cur += 1
        return res


deserialize

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

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.

Implement the Python class `Codec` described below.

Class description:
Implement the Codec class.

Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.

<|skeleton|>
class Codec:

    def serialize(self, root: TreeNode) -> str:
        """Encodes a tree to a single string."""
        <|body_0|>

    def deserialize(self, data: str) -> TreeNode:
        """Decodes your encoded data to tree."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not root:
            return '# '
        ans = str(root.val) + ' '
        ans += self.serialize(root.left)
        ans += self.serialize(root.right)
        return ans
<|end_body_0|>

<|body_start_1|>
        def helper(data):
            if not data:
                return None
            cur = data.pop(0)
            if cur == '#':
                return None
            root = TreeNode(int(cur))
            root.left = helper(data)
            root.right = helper(data)
            return root
        return helper(data.split(' '))
<|end_body_1|>

Solution:
class Codec:
    def serialize(self, root: TreeNode) -> str:
        """Encodes a tree to a single string."""
        if not root:
            return '# '
        ans = str(root.val) + ' '
        ans += self.serialize(root.left)
        ans += self.serialize(root.right)
        return ans

    def deserialize(self, data: str) -> TreeNode:
        """Decodes your encoded data to tree."""
        def helper(data):
            if not data:
                return None
            cur = data.pop(0)
            if cur == '#':
                return None
            root = TreeNode(int(cur))
            root.left = helper(data)
            root.right = helper(data)
            return root
        return helper(data.split(' '))


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

Class description:
Implement the TestReverse class.

Method signatures and docstrings:
- def test_reverse_empty_word(self): An empty word must return an empty response
- def test_reverse_invalid_word(self): An invalid word must return False
- def test_reverse_valid_word(self): For a valid string, it must return the ivnersed string
- def test_reverse_valid_sentence(self): It must reverse only the words, not the sentence

Implement the Python class `TestReverse` described below.

Class description:
Implement the TestReverse class.

Method signatures and docstrings:
- def test_reverse_empty_word(self): An empty word must return an empty response
- def test_reverse_invalid_word(self): An invalid word must return False
- def test_reverse_valid_word(self): For a valid string, it must return the ivnersed string
- def test_reverse_valid_sentence(self): It must reverse only the words, not the sentence

<|skeleton|>
class TestReverse:

    def test_reverse_empty_word(self):
        """An empty word must return an empty response"""
        <|body_0|>

    def test_reverse_invalid_word(self):
        """An invalid word must return False"""
        <|body_1|>

    def test_reverse_valid_word(self):
        """For a valid string, it must return the ivnersed string"""
        <|body_2|>

    def test_reverse_valid_sentence(self):
        """It must reverse only the words, not the sentence"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        word = ''
        inverse = inverser(word)
        self.assertEqual(inverse, word)
        self.assertIsInstance(inverse, str)
<|end_body_0|>

<|body_start_1|>
        word = int(152)
        inverse = inverser(word)
        self.assertFalse(inverse)
<|end_body_1|>

<|body_start_2|>
        word = 'abcde'
        inverse = inverser(word)
        reverse = inverser(inverse)
        self.assertEqual(reverse, word)
        self.assertFalse(word == inverse)
<|end_body_2|>

<|body_start_3|>
        sentence = 'abcde jklm nopq'
        inverse_sentence = inverser(sentence)
        inverse_word = inverse_string(sentence)
        reverse_sentence = inverser(inverse_sentence)
        self.assertEqual(sentence, reverse_sentence)
        self.assertFalse(sentence == inverse_word)
<|end_body_3|>

Solution:
class TestReverse:
    def test_reverse_empty_word(self):
        """An empty word must return an empty response"""
        word = ''
        inverse = inverser(word)
        self.assertEqual(inverse, word)
        self.assertIsInstance(inverse, str)

    def test_reverse_invalid_word(self):
        """An invalid word must return False"""
        word = int(152)
        inverse = inverser(word)
        self.assertFalse(inverse)

    def test_reverse_valid_word(self):
        """For a valid string, it must return the ivnersed string"""
        word = 'abcde'
        inverse = inverser(word)
        reverse = inverser(inverse)
        self.assertEqual(reverse, word)
        self.assertFalse(word == inverse)

    def test_reverse_valid_sentence(self):
        """It must reverse only the words, not the sentence"""
        sentence = 'abcde jklm nopq'
        inverse_sentence = inverser(sentence)
        inverse_word = inverse_string(sentence)
        reverse_sentence = inverser(inverse_sentence)
        self.assertEqual(sentence, reverse_sentence)
        self.assertFalse(sentence == inverse_word)


pmf

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

Class description:
The class to call method of poisson distribution

Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Initialize method
- def pmf(self, k): Calculates the value of the PMF for a given number of successes
- def cdf(self, k): Calculates the value of the CDF

Implement the Python class `Poisson` described below.

Class description:
The class to call method of poisson distribution

Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Initialize method
- def pmf(self, k): Calculates the value of the PMF for a given number of successes
- def cdf(self, k): Calculates the value of the CDF

<|skeleton|>
class Poisson:
    """The class to call method of poisson distribution"""

    def __init__(self, data=None, lambtha=1.0):
        """Initialize method"""
        <|body_0|>

    def pmf(self, k):
        """Calculates the value of the PMF for a given number of successes"""
        <|body_1|>

    def cdf(self, k):
        """Calculates the value of the CDF"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if data is None:
            if lambtha > 0:
                self.lambtha = float(lambtha)
            else:
                raise ValueError('lambtha must be a positive value')
        else:
            if type(data) != list:
                raise TypeError('data must be a list')
            if len(data) < 2:
                raise ValueError('data must contain multiple values')
            self.lambtha = sum(data) / len(data)
<|end_body_0|>

<|body_start_1|>
        e = 2.7182818285
        if type(k) != int:
            k = int(k)
        if k < 0:
            return 0
        factorial = 1
        for i in range(1, k + 1):
            factorial = factorial * i
        pmf = pow(e, -self.lambtha) * pow(self.lambtha, k) / factorial
        return pmf
<|end_body_1|>

<|body_start_2|>
        if type(k) != int:
            k = int(k)
        if k < 0:
            return 0
        factorial = 1
        prob = []
        for i in range(0, k + 1):
            prob.append(self.pmf(i))
        return sum(prob)
<|end_body_2|>

Solution:
class Poisson:
    """The class to call method of poisson distribution"""

    def __init__(self, data=None, lambtha=1.0):
        """Initialize method"""
        if data is None:
            if lambtha > 0:
                self.lambtha = float(lambtha)
            else:
                raise ValueError('lambtha must be a positive value')
        else:
            if type(data) != list:
                raise TypeError('data must be a list')
            if len(data) < 2:
                raise ValueError('data must contain multiple values')
            self.lambtha = sum(data) / len(data)

    def pmf(self, k):
        """Calculates the value of the PMF for a given number of successes"""
        e = 2.7182818285
        if type(k) != int:
            k = int(k)
        if k < 0:
            return 0
        factorial = 1
        for i in range(1, k + 1):
            factorial = factorial * i
        pmf = pow(e, -self.lambtha) * pow(self.lambtha, k) / factorial
        return pmf

    def cdf(self, k):
        """Calculates the value of the CDF"""
        if type(k) != int:
            k = int(k)
        if k < 0:
            return 0
        factorial = 1
        prob = []
        for i in range(0, k + 1):
            prob.append(self.pmf(i))
        return sum(prob)


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

Class description:
Implement the PayFlowProTransaction class.

Method signatures and docstrings:
- def _issue_request(self, xml): This is a common method that can be used to submit a request in XML form to the payflowpro server @param xml XML data structure expected by paypal's server @type xml string
- def _handle_result_code(self, result_code): Interpret a result code and act accordingly @param result_code result code received from payflowpro server @type result_code int

Implement the Python class `PayFlowProTransaction` described below.

Class description:
Implement the PayFlowProTransaction class.

Method signatures and docstrings:
- def _issue_request(self, xml): This is a common method that can be used to submit a request in XML form to the payflowpro server @param xml XML data structure expected by paypal's server @type xml string
- def _handle_result_code(self, result_code): Interpret a result code and act accordingly @param result_code result code received from payflowpro server @type result_code int

<|skeleton|>
class PayFlowProTransaction:

    def _issue_request(self, xml):
        """This is a common method that can be used to submit a request in XML form to the payflowpro server @param xml XML data structure expected by paypal's server @type xml string"""
        <|body_0|>

    def _handle_result_code(self, result_code):
        """Interpret a result code and act accordingly @param result_code result code received from payflowpro server @type result_code int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        rr = raw_response()
        try:
            response = urllib.urlopen(self.settings.URL, xml)
        except IOError:
            raise connection_error_exception
        rr.text = response.read()
        rr.enter()
        return self._parse_response(rr.text)
<|end_body_0|>

<|body_start_1|>
        result_code = int(result_code)
        if result_code == 0:
            pass
        elif result_code == 1:
            raise transaction_failure_exception('authentication failed to the merchant services gateway')
        elif result_code == 4:
            raise transaction_failure_exception('invalid currency amount format.  Please indicate currency in cents')
        elif result_code == 12:
            raise transaction_denied_exception
        else:
            raise transaction_failure_exception('transaction failed')
<|end_body_1|>

Solution:
class PayFlowProTransaction:
    def _issue_request(self, xml):
        """This is a common method that can be used to submit a request in XML form to the payflowpro server @param xml XML data structure expected by paypal's server @type xml string"""
        rr = raw_response()
        try:
            response = urllib.urlopen(self.settings.URL, xml)
        except IOError:
            raise connection_error_exception
        rr.text = response.read()
        rr.enter()
        return self._parse_response(rr.text)

    def _handle_result_code(self, result_code):
        """Interpret a result code and act accordingly @param result_code result code received from payflowpro server @type result_code int"""
        result_code = int(result_code)
        if result_code == 0:
            pass
        elif result_code == 1:
            raise transaction_failure_exception('authentication failed to the merchant services gateway')
        elif result_code == 4:
            raise transaction_failure_exception('invalid currency amount format.  Please indicate currency in cents')
        elif result_code == 12:
            raise transaction_denied_exception
        else:
            raise transaction_failure_exception('transaction failed')


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

Class description:
Implement the AntibioticDetector class.

Method signatures and docstrings:
- def __init__(self, antibiotic_threshold=0.9, antibiotic_key='antibiotic'): Death detector for antibiotics Checks whether the cell can survive the current internal antibiotic concentrations. Arguments: antibiotic_threshold (float): The maximum internal antibiotic concentration the cell can survive. antibiotic_key (str): The name of the variable storing the cell's internal antibiotic concentration.
- def check_can_survive(self, states): Checks if the current antibiotic concentration is survivable The internal antibiotic concentration MUST be stored in a variable of a port named ``internal``. Returns: bool: False if the antibiotic concentration is strictly greater than the the threshold. True otherwise.

Implement the Python class `AntibioticDetector` described below.

Class description:
Implement the AntibioticDetector class.

Method signatures and docstrings:
- def __init__(self, antibiotic_threshold=0.9, antibiotic_key='antibiotic'): Death detector for antibiotics Checks whether the cell can survive the current internal antibiotic concentrations. Arguments: antibiotic_threshold (float): The maximum internal antibiotic concentration the cell can survive. antibiotic_key (str): The name of the variable storing the cell's internal antibiotic concentration.
- def check_can_survive(self, states): Checks if the current antibiotic concentration is survivable The internal antibiotic concentration MUST be stored in a variable of a port named ``internal``. Returns: bool: False if the antibiotic concentration is strictly greater than the the threshold. True otherwise.

<|skeleton|>
class AntibioticDetector:

    def __init__(self, antibiotic_threshold=0.9, antibiotic_key='antibiotic'):
        """Death detector for antibiotics Checks whether the cell can survive the current internal antibiotic concentrations. Arguments: antibiotic_threshold (float): The maximum internal antibiotic concentration the cell can survive. antibiotic_key (str): The name of the variable storing the cell's internal antibiotic concentration."""
        <|body_0|>

    def check_can_survive(self, states):
        """Checks if the current antibiotic concentration is survivable The internal antibiotic concentration MUST be stored in a variable of a port named ``internal``. Returns: bool: False if the antibiotic concentration is strictly greater than the the threshold. True otherwise."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(AntibioticDetector, self).__init__()
        self.threshold = antibiotic_threshold
        self.key = antibiotic_key
        self.needed_state_keys.setdefault('internal', set()).add(antibiotic_key)
<|end_body_0|>

<|body_start_1|>
        concentration = states['internal'][self.key]
        if concentration > self.threshold:
            return False
        return True
<|end_body_1|>

Solution:
class AntibioticDetector:
    def __init__(self, antibiotic_threshold=0.9, antibiotic_key='antibiotic'):
        """Death detector for antibiotics Checks whether the cell can survive the current internal antibiotic concentrations. Arguments: antibiotic_threshold (float): The maximum internal antibiotic concentration the cell can survive. antibiotic_key (str): The name of the variable storing the cell's internal antibiotic concentration."""
        super(AntibioticDetector, self).__init__()
        self.threshold = antibiotic_threshold
        self.key = antibiotic_key
        self.needed_state_keys.setdefault('internal', set()).add(antibiotic_key)

    def check_can_survive(self, states):
        """Checks if the current antibiotic concentration is survivable The internal antibiotic concentration MUST be stored in a variable of a port named ``internal``. Returns: bool: False if the antibiotic concentration is strictly greater than the the threshold. True otherwise."""
        concentration = states['internal'][self.key]
        if concentration > self.threshold:
            return False
        return True