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

Class description:
FastSpeech duration predictor module.

Method signatures and docstrings:
- def __init__(self, config, **kwargs): Init variables.
- def call(self, inputs, training=False): Call logic.

Implement the Python class `TFFastSpeechDurationPredictor` described below.

Class description:
FastSpeech duration predictor module.

Method signatures and docstrings:
- def __init__(self, config, **kwargs): Init variables.
- def call(self, inputs, training=False): Call logic.

<|skeleton|>
class TFFastSpeechDurationPredictor:
    """FastSpeech duration predictor module."""

    def __init__(self, config, **kwargs):
        """Init variables."""
        <|body_0|>

    def call(self, inputs, training=False):
        """Call logic."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(**kwargs)
        self.conv_layers = []
        for i in range(config.num_duration_conv_layers):
            self.conv_layers.append(tf.keras.layers.Conv1D(config.duration_predictor_filters, config.duration_predictor_kernel_sizes, padding='same', name='conv_._{}'.format(i)))
            self.conv_layers.append(tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm_._{}'.format(i)))
            self.conv_layers.append(tf.keras.layers.Activation(tf.nn.relu6))
            self.conv_layers.append(tf.keras.layers.Dropout(config.duration_predictor_dropout_probs))
        self.conv_layers_sequence = tf.keras.Sequential(self.conv_layers)
        self.output_layer = tf.keras.layers.Dense(1)
<|end_body_0|>

<|body_start_1|>
        encoder_hidden_states, attention_mask = inputs
        attention_mask = tf.cast(tf.expand_dims(attention_mask, 2), encoder_hidden_states.dtype)
        masked_encoder_hidden_states = encoder_hidden_states * attention_mask
        outputs = self.conv_layers_sequence(masked_encoder_hidden_states)
        outputs = self.output_layer(outputs)
        masked_outputs = outputs * attention_mask
        return tf.squeeze(tf.nn.relu6(masked_outputs), -1)
<|end_body_1|>

Solution:
class TFFastSpeechDurationPredictor:
    """FastSpeech duration predictor module."""

    def __init__(self, config, **kwargs):
        """Init variables."""
        super().__init__(**kwargs)
        self.conv_layers = []
        for i in range(config.num_duration_conv_layers):
            self.conv_layers.append(tf.keras.layers.Conv1D(config.duration_predictor_filters, config.duration_predictor_kernel_sizes, padding='same', name='conv_._{}'.format(i)))
            self.conv_layers.append(tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm_._{}'.format(i)))
            self.conv_layers.append(tf.keras.layers.Activation(tf.nn.relu6))
            self.conv_layers.append(tf.keras.layers.Dropout(config.duration_predictor_dropout_probs))
        self.conv_layers_sequence = tf.keras.Sequential(self.conv_layers)
        self.output_layer = tf.keras.layers.Dense(1)

    def call(self, inputs, training=False):
        """Call logic."""
        encoder_hidden_states, attention_mask = inputs
        attention_mask = tf.cast(tf.expand_dims(attention_mask, 2), encoder_hidden_states.dtype)
        masked_encoder_hidden_states = encoder_hidden_states * attention_mask
        outputs = self.conv_layers_sequence(masked_encoder_hidden_states)
        outputs = self.output_layer(outputs)
        masked_outputs = outputs * attention_mask
        return tf.squeeze(tf.nn.relu6(masked_outputs), -1)


serialize

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

Class description:
Implement the TeleconferenceDeviceQuality class.

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

Implement the Python class `TeleconferenceDeviceQuality` described below.

Class description:
Implement the TeleconferenceDeviceQuality class.

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

<|skeleton|>
class TeleconferenceDeviceQuality:

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

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

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

<|end_skeleton|>

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

<|body_start_1|>
        from .teleconference_device_media_quality import TeleconferenceDeviceMediaQuality
        from .teleconference_device_media_quality import TeleconferenceDeviceMediaQuality
        fields: Dict[str, Callable[[Any], None]] = {'callChainId': lambda n: setattr(self, 'call_chain_id', n.get_uuid_value()), 'cloudServiceDeploymentEnvironment': lambda n: setattr(self, 'cloud_service_deployment_environment', n.get_str_value()), 'cloudServiceDeploymentId': lambda n: setattr(self, 'cloud_service_deployment_id', n.get_str_value()), 'cloudServiceInstanceName': lambda n: setattr(self, 'cloud_service_instance_name', n.get_str_value()), 'cloudServiceName': lambda n: setattr(self, 'cloud_service_name', n.get_str_value()), 'deviceDescription': lambda n: setattr(self, 'device_description', n.get_str_value()), 'deviceName': lambda n: setattr(self, 'device_name', n.get_str_value()), 'mediaLegId': lambda n: setattr(self, 'media_leg_id', n.get_uuid_value()), 'mediaQualityList': lambda n: setattr(self, 'media_quality_list', n.get_collection_of_object_values(TeleconferenceDeviceMediaQuality)), '@odata.type': lambda n: setattr(self, 'odata_type', n.get_str_value()), 'participantId': lambda n: setattr(self, 'participant_id', n.get_uuid_value())}
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        writer.write_uuid_value('callChainId', self.call_chain_id)
        writer.write_str_value('cloudServiceDeploymentEnvironment', self.cloud_service_deployment_environment)
        writer.write_str_value('cloudServiceDeploymentId', self.cloud_service_deployment_id)
        writer.write_str_value('cloudServiceInstanceName', self.cloud_service_instance_name)
        writer.write_str_value('cloudServiceName', self.cloud_service_name)
        writer.write_str_value('deviceDescription', self.device_description)
        writer.write_str_value('deviceName', self.device_name)
        writer.write_uuid_value('mediaLegId', self.media_leg_id)
        writer.write_collection_of_object_values('mediaQualityList', self.media_quality_list)
        writer.write_str_value('@odata.type', self.odata_type)
        writer.write_uuid_value('participantId', self.participant_id)
        writer.write_additional_data_value(self.additional_data)
<|end_body_2|>

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

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .teleconference_device_media_quality import TeleconferenceDeviceMediaQuality
        from .teleconference_device_media_quality import TeleconferenceDeviceMediaQuality
        fields: Dict[str, Callable[[Any], None]] = {'callChainId': lambda n: setattr(self, 'call_chain_id', n.get_uuid_value()), 'cloudServiceDeploymentEnvironment': lambda n: setattr(self, 'cloud_service_deployment_environment', n.get_str_value()), 'cloudServiceDeploymentId': lambda n: setattr(self, 'cloud_service_deployment_id', n.get_str_value()), 'cloudServiceInstanceName': lambda n: setattr(self, 'cloud_service_instance_name', n.get_str_value()), 'cloudServiceName': lambda n: setattr(self, 'cloud_service_name', n.get_str_value()), 'deviceDescription': lambda n: setattr(self, 'device_description', n.get_str_value()), 'deviceName': lambda n: setattr(self, 'device_name', n.get_str_value()), 'mediaLegId': lambda n: setattr(self, 'media_leg_id', n.get_uuid_value()), 'mediaQualityList': lambda n: setattr(self, 'media_quality_list', n.get_collection_of_object_values(TeleconferenceDeviceMediaQuality)), '@odata.type': lambda n: setattr(self, 'odata_type', n.get_str_value()), 'participantId': lambda n: setattr(self, 'participant_id', n.get_uuid_value())}
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        writer.write_uuid_value('callChainId', self.call_chain_id)
        writer.write_str_value('cloudServiceDeploymentEnvironment', self.cloud_service_deployment_environment)
        writer.write_str_value('cloudServiceDeploymentId', self.cloud_service_deployment_id)
        writer.write_str_value('cloudServiceInstanceName', self.cloud_service_instance_name)
        writer.write_str_value('cloudServiceName', self.cloud_service_name)
        writer.write_str_value('deviceDescription', self.device_description)
        writer.write_str_value('deviceName', self.device_name)
        writer.write_uuid_value('mediaLegId', self.media_leg_id)
        writer.write_collection_of_object_values('mediaQualityList', self.media_quality_list)
        writer.write_str_value('@odata.type', self.odata_type)
        writer.write_uuid_value('participantId', self.participant_id)
        writer.write_additional_data_value(self.additional_data)


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

Class description:
Abstract Class for parsing Sio Eng Sio files

Method signatures and docstrings:
- def parse_chunks(self): Parse out any pending data chunks in the chunker. If it is a valid data piece, build a particle, update the position and timestamp. Go until the chunker has no more valid data. @retval a list of tuples with sample particles encountered in this parsing, plus the state. An empty list of nothing was parsed.
- def handle_non_data(self, non_data, non_end, start): Check for and handle any non-data that is found in the file

Implement the Python class `SioEngSioParser` described below.

Class description:
Abstract Class for parsing Sio Eng Sio files

Method signatures and docstrings:
- def parse_chunks(self): Parse out any pending data chunks in the chunker. If it is a valid data piece, build a particle, update the position and timestamp. Go until the chunker has no more valid data. @retval a list of tuples with sample particles encountered in this parsing, plus the state. An empty list of nothing was parsed.
- def handle_non_data(self, non_data, non_end, start): Check for and handle any non-data that is found in the file

<|skeleton|>
class SioEngSioParser:
    """Abstract Class for parsing Sio Eng Sio files"""

    def parse_chunks(self):
        """Parse out any pending data chunks in the chunker. If it is a valid data piece, build a particle, update the position and timestamp. Go until the chunker has no more valid data. @retval a list of tuples with sample particles encountered in this parsing, plus the state. An empty list of nothing was parsed."""
        <|body_0|>

    def handle_non_data(self, non_data, non_end, start):
        """Check for and handle any non-data that is found in the file"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        result_particles = []
        nd_timestamp, non_data, non_start, non_end = self._chunker.get_next_non_data_with_index(clean=False)
        timestamp, chunk, start, end = self._chunker.get_next_data_with_index(clean=True)
        self.handle_non_data(non_data, non_end, start)
        while chunk is not None:
            header_match = SIO_HEADER_MATCHER.match(chunk)
            if header_match.group(SIO_HEADER_GROUP_ID) == 'CS':
                data_match = ENG_MATCHER.match(chunk)
                if data_match:
                    posix_time = int(header_match.group(SIO_HEADER_GROUP_TIMESTAMP), 16)
                    log.debug('utc timestamp %s', datetime.utcfromtimestamp(posix_time))
                    timestamp = ntplib.system_to_ntp_time(float(posix_time))
                    sample = self._extract_sample(self._particle_class, None, data_match, internal_timestamp=timestamp)
                    if sample:
                        result_particles.append(sample)
                else:
                    log.warn('CS data does not match REGEX')
                    self._exception_callback(SampleException('CS data does not match REGEX'))
            elif header_match.group(SIO_HEADER_GROUP_ID) != 'PS':
                message = 'Unexpected Sio Header ID %s' % header_match.group(SIO_HEADER_GROUP_ID)
                log.warn(message)
                self._exception_callback(UnexpectedDataException(message))
            nd_timestamp, non_data, non_start, non_end = self._chunker.get_next_non_data_with_index(clean=False)
            timestamp, chunk, start, end = self._chunker.get_next_data_with_index(clean=True)
            self.handle_non_data(non_data, non_end, start)
        return result_particles
<|end_body_0|>

<|body_start_1|>
        if non_data is not None and non_end <= start:
            message = 'Found %d bytes of un-expected non-data' % len(non_data)
            log.warn(message)
            self._exception_callback(UnexpectedDataException(message))
<|end_body_1|>

Solution:
class SioEngSioParser:
    """Abstract Class for parsing Sio Eng Sio files"""

    def parse_chunks(self):
        """Parse out any pending data chunks in the chunker. If it is a valid data piece, build a particle, update the position and timestamp. Go until the chunker has no more valid data. @retval a list of tuples with sample particles encountered in this parsing, plus the state. An empty list of nothing was parsed."""
        result_particles = []
        nd_timestamp, non_data, non_start, non_end = self._chunker.get_next_non_data_with_index(clean=False)
        timestamp, chunk, start, end = self._chunker.get_next_data_with_index(clean=True)
        self.handle_non_data(non_data, non_end, start)
        while chunk is not None:
            header_match = SIO_HEADER_MATCHER.match(chunk)
            if header_match.group(SIO_HEADER_GROUP_ID) == 'CS':
                data_match = ENG_MATCHER.match(chunk)
                if data_match:
                    posix_time = int(header_match.group(SIO_HEADER_GROUP_TIMESTAMP), 16)
                    log.debug('utc timestamp %s', datetime.utcfromtimestamp(posix_time))
                    timestamp = ntplib.system_to_ntp_time(float(posix_time))
                    sample = self._extract_sample(self._particle_class, None, data_match, internal_timestamp=timestamp)
                    if sample:
                        result_particles.append(sample)
                else:
                    log.warn('CS data does not match REGEX')
                    self._exception_callback(SampleException('CS data does not match REGEX'))
            elif header_match.group(SIO_HEADER_GROUP_ID) != 'PS':
                message = 'Unexpected Sio Header ID %s' % header_match.group(SIO_HEADER_GROUP_ID)
                log.warn(message)
                self._exception_callback(UnexpectedDataException(message))
            nd_timestamp, non_data, non_start, non_end = self._chunker.get_next_non_data_with_index(clean=False)
            timestamp, chunk, start, end = self._chunker.get_next_data_with_index(clean=True)
            self.handle_non_data(non_data, non_end, start)
        return result_particles

    def handle_non_data(self, non_data, non_end, start):
        """Check for and handle any non-data that is found in the file"""
        if non_data is not None and non_end <= start:
            message = 'Found %d bytes of un-expected non-data' % len(non_data)
            log.warn(message)
            self._exception_callback(UnexpectedDataException(message))


解析评论页数据以及构造下一评论页请求

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

Class description:
解析数据和爬虫逻辑类

Method signatures and docstrings:
- def __init__(self, name=None, **kwargs): 完成解析前的初始化工作,主要是将用的到 xpath 配合完成 :param self: 类的对象自身 :param name: scrapy 会将 name 属性传递进来 :param kwargs: 字典形式的参数,用于更新 self.__dict__ :return None
- def parse(self, response): 构造帖子页请求
- def parse_post(self, response): 构造评论页请求
- def parse_comment(self, response): 解析评论页数据以及构造下一评论页请求

Implement the Python class `MySpider` described below.

Class description:
解析数据和爬虫逻辑类

Method signatures and docstrings:
- def __init__(self, name=None, **kwargs): 完成解析前的初始化工作,主要是将用的到 xpath 配合完成 :param self: 类的对象自身 :param name: scrapy 会将 name 属性传递进来 :param kwargs: 字典形式的参数,用于更新 self.__dict__ :return None
- def parse(self, response): 构造帖子页请求
- def parse_post(self, response): 构造评论页请求
- def parse_comment(self, response): 解析评论页数据以及构造下一评论页请求

<|skeleton|>
class MySpider:
    """解析数据和爬虫逻辑类"""

    def __init__(self, name=None, **kwargs):
        """完成解析前的初始化工作,主要是将用的到 xpath 配合完成 :param self: 类的对象自身 :param name: scrapy 会将 name 属性传递进来 :param kwargs: 字典形式的参数,用于更新 self.__dict__ :return None"""
        <|body_0|>

    def parse(self, response):
        """构造帖子页请求"""
        <|body_1|>

    def parse_post(self, response):
        """构造评论页请求"""
        <|body_2|>

    def parse_comment(self, response):
        """解析评论页数据以及构造下一评论页请求"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(name, **kwargs)
        self.website_id = 'gsmhosting'
        self.website_type = 'complaint'
        self.forum_list_xpath = '/html/body/div/div[1]/div/table[5]/tbody'
        self.forum_url_xpath = './tr/td/table/tr/td[3]/div/a/@href'
        self.post_list_xpath = '//*[@id="threadslist"]/tbody[2]/tr'
        self.post_url_xpath = './td[3]/div/a/@href'
        self.post_list_url_xpath = '//a[@rel="next"]/@href'
        self.post_comment_num = './td[5]/a/text()'
        self.post_view_num = './td[6]/text()'
        self.item_list_path = '//*[@id="posts"]/div'
        self.item_path = dict()
        self.item_path['user_name'] = './/*[@class="bigusername"]//text()'
        self.item_path['user_url'] = './/*[@class="bigusername"]//@href'
        self.item_path['user_group'] = './div/div/div/table/tr[2]/td[1]/div[2]/text()'
        self.item_path['user_name'] = './/*[@class="bigusername"]//text()'
        self.item_path['registration_date'] = './/*[contains(text(),"Join Date")]//text()'
        self.item_path['region'] = './/*[contains(text(),"Location")]//text()'
        self.item_path['age'] = './/*[contains(text(),"Age")]//text()'
        self.item_path['user_comment_num'] = './/*[contains(text(),"Posts")]//text()'
        self.item_path['points'] = './/*[contains(text(),"Thanks Meter")]//text()'
        self.item_path['title'] = './div/div/div/table/tr[2]/td[2]/div[1]/strong/text()'
        self.item_path['time'] = './div/div/div/table/tr[1]/td[1]/text()'
        self.item_path['main_body'] = './div/div/div/table/tr[2]/td[2]/div/text()'
        self.item_path['floor'] = './div/div/div/table/tr[1]/td[2]/a/strong/text()'
        self.item_url = '/html/body/div[3]/div[1]/div/table[1]/tr/td[2]/div/table/tr/td[5]/a/@href'
<|end_body_0|>

<|body_start_1|>
        forums = response.xpath(self.forum_list_xpath)
        for forum in forums:
            forum_url = forum.xpath(self.forum_url_xpath).extract_first()
            if forum_url:
                yield response.follow(forum_url, callback=self.parse_post)
<|end_body_1|>

<|body_start_2|>
        posts = response.xpath(self.post_list_xpath)
        for post in posts:
            post_url = post.xpath(self.post_url_xpath).extract_first()
            if post_url:
                comment_num = post.xpath(self.post_comment_num).extract_first()
                view_num = post.xpath(self.post_view_num).extract_first()
                yield response.follow(post_url, callback=self.parse_comment, meta={'comment_num': comment_num, 'view_num': view_num})
        post_list_url = response.xpath(self.post_list_url_xpath).extract_first()
        if post_list_url:
            yield response.follow(post_list_url, callback=self.parse_post)
<|end_body_2|>

<|body_start_3|>
        if response.meta.get('post_id'):
            post_id = response.meta['post_id']
        else:
            post_id = None
        comments = response.xpath(self.item_list_path)
        if comments:
            for comment_data in comments[:-1]:
                item = DataLoader(item=dict(), selector=comment_data)
                for field, xpath in self.item_path.items():
                    item.add_xpath(field, xpath)
                item.add_value('content_url', response.url)
                item = item.load_item()
                if item.get('floor') == '1':
                    item['content_comment_num'] = response.meta['comment_num']
                    item['view_num'] = response.meta['view_num']
                    yield self.padding_item(item, -1)
                    post_id = item['content_id']
                else:
                    yield self.padding_item(item, post_id)
        item_url = response.xpath(self.item_url).extract_first()
        if item_url:
            yield response.follow(item_url, callback=self.parse_comment, meta={'post_id': post_id})
<|end_body_3|>

Solution:
class MySpider:
    """解析数据和爬虫逻辑类"""

    def __init__(self, name=None, **kwargs):
        """完成解析前的初始化工作,主要是将用的到 xpath 配合完成 :param self: 类的对象自身 :param name: scrapy 会将 name 属性传递进来 :param kwargs: 字典形式的参数,用于更新 self.__dict__ :return None"""
        super().__init__(name, **kwargs)
        self.website_id = 'gsmhosting'
        self.website_type = 'complaint'
        self.forum_list_xpath = '/html/body/div/div[1]/div/table[5]/tbody'
        self.forum_url_xpath = './tr/td/table/tr/td[3]/div/a/@href'
        self.post_list_xpath = '//*[@id="threadslist"]/tbody[2]/tr'
        self.post_url_xpath = './td[3]/div/a/@href'
        self.post_list_url_xpath = '//a[@rel="next"]/@href'
        self.post_comment_num = './td[5]/a/text()'
        self.post_view_num = './td[6]/text()'
        self.item_list_path = '//*[@id="posts"]/div'
        self.item_path = dict()
        self.item_path['user_name'] = './/*[@class="bigusername"]//text()'
        self.item_path['user_url'] = './/*[@class="bigusername"]//@href'
        self.item_path['user_group'] = './div/div/div/table/tr[2]/td[1]/div[2]/text()'
        self.item_path['user_name'] = './/*[@class="bigusername"]//text()'
        self.item_path['registration_date'] = './/*[contains(text(),"Join Date")]//text()'
        self.item_path['region'] = './/*[contains(text(),"Location")]//text()'
        self.item_path['age'] = './/*[contains(text(),"Age")]//text()'
        self.item_path['user_comment_num'] = './/*[contains(text(),"Posts")]//text()'
        self.item_path['points'] = './/*[contains(text(),"Thanks Meter")]//text()'
        self.item_path['title'] = './div/div/div/table/tr[2]/td[2]/div[1]/strong/text()'
        self.item_path['time'] = './div/div/div/table/tr[1]/td[1]/text()'
        self.item_path['main_body'] = './div/div/div/table/tr[2]/td[2]/div/text()'
        self.item_path['floor'] = './div/div/div/table/tr[1]/td[2]/a/strong/text()'
        self.item_url = '/html/body/div[3]/div[1]/div/table[1]/tr/td[2]/div/table/tr/td[5]/a/@href'

    def parse(self, response):
        """构造帖子页请求"""
        forums = response.xpath(self.forum_list_xpath)
        for forum in forums:
            forum_url = forum.xpath(self.forum_url_xpath).extract_first()
            if forum_url:
                yield response.follow(forum_url, callback=self.parse_post)

    def parse_post(self, response):
        """构造评论页请求"""
        posts = response.xpath(self.post_list_xpath)
        for post in posts:
            post_url = post.xpath(self.post_url_xpath).extract_first()
            if post_url:
                comment_num = post.xpath(self.post_comment_num).extract_first()
                view_num = post.xpath(self.post_view_num).extract_first()
                yield response.follow(post_url, callback=self.parse_comment, meta={'comment_num': comment_num, 'view_num': view_num})
        post_list_url = response.xpath(self.post_list_url_xpath).extract_first()
        if post_list_url:
            yield response.follow(post_list_url, callback=self.parse_post)

    def parse_comment(self, response):
        """解析评论页数据以及构造下一评论页请求"""
        if response.meta.get('post_id'):
            post_id = response.meta['post_id']
        else:
            post_id = None
        comments = response.xpath(self.item_list_path)
        if comments:
            for comment_data in comments[:-1]:
                item = DataLoader(item=dict(), selector=comment_data)
                for field, xpath in self.item_path.items():
                    item.add_xpath(field, xpath)
                item.add_value('content_url', response.url)
                item = item.load_item()
                if item.get('floor') == '1':
                    item['content_comment_num'] = response.meta['comment_num']
                    item['view_num'] = response.meta['view_num']
                    yield self.padding_item(item, -1)
                    post_id = item['content_id']
                else:
                    yield self.padding_item(item, post_id)
        item_url = response.xpath(self.item_url).extract_first()
        if item_url:
            yield response.follow(item_url, callback=self.parse_comment, meta={'post_id': post_id})


Prompt:
Implement the Python class `SolutionFailed_2` described below.

Class description:
Status: Time Limit Exceeded Submitted: 0 minutes ago Last executed input: 8362 It's better than solution1, but not good enough.

Method signatures and docstrings:
- def count_prime_factor_5(self, n): Count prime factor 5 for positive intergers. 0 is not accepted.
- def trailingZeroes(self, n): :type n: int :rtype: int

Implement the Python class `SolutionFailed_2` described below.

Class description:
Status: Time Limit Exceeded Submitted: 0 minutes ago Last executed input: 8362 It's better than solution1, but not good enough.

Method signatures and docstrings:
- def count_prime_factor_5(self, n): Count prime factor 5 for positive intergers. 0 is not accepted.
- def trailingZeroes(self, n): :type n: int :rtype: int

<|skeleton|>
class SolutionFailed_2:
    """Status: Time Limit Exceeded Submitted: 0 minutes ago Last executed input: 8362 It's better than solution1, but not good enough."""

    def count_prime_factor_5(self, n):
        """Count prime factor 5 for positive intergers. 0 is not accepted."""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        prime_factor_5_num = 0
        while n / 5 == n / float(5):
            prime_factor_5_num += 1
            n /= 5
        return prime_factor_5_num
<|end_body_0|>

<|body_start_1|>
        trailing_zero_numbers = 0
        for i in range(1, n + 1):
            prime_factor_5_num = self.count_prime_factor_5(i)
            trailing_zero_numbers += prime_factor_5_num
        return trailing_zero_numbers
<|end_body_1|>

Solution:
class SolutionFailed_2:
    """Status: Time Limit Exceeded Submitted: 0 minutes ago Last executed input: 8362 It's better than solution1, but not good enough."""

    def count_prime_factor_5(self, n):
        """Count prime factor 5 for positive intergers. 0 is not accepted."""
        prime_factor_5_num = 0
        while n / 5 == n / float(5):
            prime_factor_5_num += 1
            n /= 5
        return prime_factor_5_num

    def trailingZeroes(self, n):
        """:type n: int :rtype: int"""
        trailing_zero_numbers = 0
        for i in range(1, n + 1):
            prime_factor_5_num = self.count_prime_factor_5(i)
            trailing_zero_numbers += prime_factor_5_num
        return trailing_zero_numbers


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

Class description:
Implement the AccountAPI class.

Method signatures and docstrings:
- def create(self, account): :type account: Account
- def update(self, account): :type account: Account
- def delete(self, account): :type account: Account|int
- def delete_all(self, *args): :type *args: list[Account]
- def list(self, **kwargs): :rtype: collections.Sequence[Account]
- def find(self, **kwargs): :rtype: Account

Implement the Python class `AccountAPI` described below.

Class description:
Implement the AccountAPI class.

Method signatures and docstrings:
- def create(self, account): :type account: Account
- def update(self, account): :type account: Account
- def delete(self, account): :type account: Account|int
- def delete_all(self, *args): :type *args: list[Account]
- def list(self, **kwargs): :rtype: collections.Sequence[Account]
- def find(self, **kwargs): :rtype: Account

<|skeleton|>
class AccountAPI:

    def create(self, account):
        """:type account: Account"""
        <|body_0|>

    def update(self, account):
        """:type account: Account"""
        <|body_1|>

    def delete(self, account):
        """:type account: Account|int"""
        <|body_2|>

    def delete_all(self, *args):
        """:type *args: list[Account]"""
        <|body_3|>

    def list(self, **kwargs):
        """:rtype: collections.Sequence[Account]"""
        <|body_4|>

    def find(self, **kwargs):
        """:rtype: Account"""
        <|body_5|>

<|end_skeleton|>

<|body_start_0|>
        cmd = new_object(createAccountCmd, account)
        created = self.api_client().createAccount(cmd, method='POST')
        return new_object(Account, created, set_all=True)
<|end_body_0|>

<|body_start_1|>
        cmd = new_object(updateAccountCmd, account)
        updated = self.api_client().updateAccount(cmd, method='POST')
        return new_object(Account, updated, set_all=True)
<|end_body_1|>

<|body_start_2|>
        if isinstance(account, int):
            account_id = account
        else:
            account_id = account.id
        cmd = deleteAccountCmd()
        cmd.id = account_id
        result = self.api_client().deleteAccount(cmd, method='POST')
        if not result.success:
            raise CloudstackAPIFailureException('deletion failed for id %s' % account_id)
<|end_body_2|>

<|body_start_3|>
        cmd = listAccountsCmd()
        cmd.listall = True
        results = self.api_client().listAccounts(cmd)
        for account in args:
            for result in results:
                if account.name == result.name:
                    self.delete(result)
<|end_body_3|>

<|body_start_4|>
        cmd = listAccountsCmd()
        copy_to_object(cmd, kwargs)
        results = self.api_client().listAccounts(cmd)
        if results is None:
            return []
        return list([new_object(Account, a, set_all=True) for a in results])
<|end_body_4|>

<|body_start_5|>
        if not 'listall' in kwargs:
            kwargs = dict(kwargs)
            kwargs['listall'] = True
        results = self.list(**kwargs)
        matches = len(results)
        if matches == 0:
            raise CloudstackNoResultsException('No results found matching %s' % repr(kwargs))
        elif matches == 1:
            return results[0]
        else:
            raise CloudstackMultipleResultsException('%s results found matching %s' % (matches, repr(kwargs)))
<|end_body_5|>

Solution:
class AccountAPI:
    def create(self, account):
        """:type account: Account"""
        cmd = new_object(createAccountCmd, account)
        created = self.api_client().createAccount(cmd, method='POST')
        return new_object(Account, created, set_all=True)

    def update(self, account):
        """:type account: Account"""
        cmd = new_object(updateAccountCmd, account)
        updated = self.api_client().updateAccount(cmd, method='POST')
        return new_object(Account, updated, set_all=True)

    def delete(self, account):
        """:type account: Account|int"""
        if isinstance(account, int):
            account_id = account
        else:
            account_id = account.id
        cmd = deleteAccountCmd()
        cmd.id = account_id
        result = self.api_client().deleteAccount(cmd, method='POST')
        if not result.success:
            raise CloudstackAPIFailureException('deletion failed for id %s' % account_id)

    def delete_all(self, *args):
        """:type *args: list[Account]"""
        cmd = listAccountsCmd()
        cmd.listall = True
        results = self.api_client().listAccounts(cmd)
        for account in args:
            for result in results:
                if account.name == result.name:
                    self.delete(result)

    def list(self, **kwargs):
        """:rtype: collections.Sequence[Account]"""
        cmd = listAccountsCmd()
        copy_to_object(cmd, kwargs)
        results = self.api_client().listAccounts(cmd)
        if results is None:
            return []
        return list([new_object(Account, a, set_all=True) for a in results])

    def find(self, **kwargs):
        """:rtype: Account"""
        if not 'listall' in kwargs:
            kwargs = dict(kwargs)
            kwargs['listall'] = True
        results = self.list(**kwargs)
        matches = len(results)
        if matches == 0:
            raise CloudstackNoResultsException('No results found matching %s' % repr(kwargs))
        elif matches == 1:
            return results[0]
        else:
            raise CloudstackMultipleResultsException('%s results found matching %s' % (matches, repr(kwargs)))


numberOfBoomerangs

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numberOfBoomerangs(self, points): :type points: List[List[int]] :rtype: int
- def numberOfBoomerangs2(self, points): :type points: List[List[int]] :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numberOfBoomerangs(self, points): :type points: List[List[int]] :rtype: int
- def numberOfBoomerangs2(self, points): :type points: List[List[int]] :rtype: int

<|skeleton|>
class Solution:

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

    def numberOfBoomerangs2(self, points):
        """:type points: List[List[int]] :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        result = 0
        for i in range(len(points)):
            group = collections.defaultdict(int)
            for j in range(len(points)):
                if j == i:
                    continue
                dx, dy = (points[i][0] - points[j][0], points[i][1] - points[j][1])
                group[dx ** 2 + dy ** 2] += 1
            for _, v in group.items():
                if v > 1:
                    result += v * (v - 1)
        return result
<|end_body_0|>

<|body_start_1|>
        cnt = 0
        for a, i in enumerate(points):
            dis_list = []
            for b, k in enumerate(points[:a] + points[a + 1:]):
                dis_list.append((k[0] - i[0]) ** 2 + (k[1] - i[1]) ** 2)
            for z in list(collections.Counter(dis_list).values()):
                if z > 1:
                    cnt += z * (z - 1)
        return cnt
<|end_body_1|>

Solution:
class Solution:
    def numberOfBoomerangs(self, points):
        """:type points: List[List[int]] :rtype: int"""
        result = 0
        for i in range(len(points)):
            group = collections.defaultdict(int)
            for j in range(len(points)):
                if j == i:
                    continue
                dx, dy = (points[i][0] - points[j][0], points[i][1] - points[j][1])
                group[dx ** 2 + dy ** 2] += 1
            for _, v in group.items():
                if v > 1:
                    result += v * (v - 1)
        return result

    def numberOfBoomerangs2(self, points):
        """:type points: List[List[int]] :rtype: int"""
        cnt = 0
        for a, i in enumerate(points):
            dis_list = []
            for b, k in enumerate(points[:a] + points[a + 1:]):
                dis_list.append((k[0] - i[0]) ** 2 + (k[1] - i[1]) ** 2)
            for z in list(collections.Counter(dis_list).values()):
                if z > 1:
                    cnt += z * (z - 1)
        return cnt


coinChange

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int

<|skeleton|>
class Solution:

    def coinChange(self, coins, amount):
        """:type coins: List[int] :type amount: int :rtype: int"""
        <|body_0|>

    def coinChange(self, coins, amount):
        """:type coins: List[int] :type amount: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        cache = {0: (0, True)}
        for c in coins:
            cache[c] = (1, True)
        self.cacheHit = 0
        self.minChange = {}

        def cc(coins, amount):
            if amount in cache:
                self.cacheHit += 1
                return cache[amount]
            for c in coins:
                if amount >= c:
                    cache.get(amount - c)
                    num, Flag = cc(coins, amount - c)
                    if Flag:
                        minc, flag = cache.get(amount, (0, False))
                        if not flag or 1 + num < minc:
                            cache[amount] = (num + 1, True)
            if amount not in cache:
                cache[amount] = (0, False)
            return cache[amount]
        num, flag = cc(sorted(coins, reverse=True), amount)
        print('cacheHit, ', self.cacheHit)
        if flag:
            return num
        else:
            return -1
<|end_body_0|>

<|body_start_1|>
        dp = [amount + 1 for _ in range(amount + 1)]
        dp[0] = 0
        for i in range(1, amount + 1):
            for c in coins:
                if c <= i:
                    dp[i] = min(dp[i], dp[i - c] + 1)
        return -1 if dp[amount] > amount else dp[amount]
<|end_body_1|>

Solution:
class Solution:
    def coinChange(self, coins, amount):
        """:type coins: List[int] :type amount: int :rtype: int"""
        cache = {0: (0, True)}
        for c in coins:
            cache[c] = (1, True)
        self.cacheHit = 0
        self.minChange = {}

        def cc(coins, amount):
            if amount in cache:
                self.cacheHit += 1
                return cache[amount]
            for c in coins:
                if amount >= c:
                    cache.get(amount - c)
                    num, Flag = cc(coins, amount - c)
                    if Flag:
                        minc, flag = cache.get(amount, (0, False))
                        if not flag or 1 + num < minc:
                            cache[amount] = (num + 1, True)
            if amount not in cache:
                cache[amount] = (0, False)
            return cache[amount]
        num, flag = cc(sorted(coins, reverse=True), amount)
        print('cacheHit, ', self.cacheHit)
        if flag:
            return num
        else:
            return -1

    def coinChange(self, coins, amount):
        """:type coins: List[int] :type amount: int :rtype: int"""
        dp = [amount + 1 for _ in range(amount + 1)]
        dp[0] = 0
        for i in range(1, amount + 1):
            for c in coins:
                if c <= i:
                    dp[i] = min(dp[i], dp[i - c] + 1)
        return -1 if dp[amount] > amount else dp[amount]


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isMatch_MLE(self, s, p): dp, similar to 011 Regular Expression Matching. Backward dp but Memory Limit Exceeds :param s: tape, an input string :param p: pattern, a pattern string :return: boolean
- def isMatch_forward(self, s, p): "?" is not the problem "*" is the problem Forward dp: dp starting from -1 if pattern[j]!="*", dp[i][j] = dp[i-1][j-1] && tape[i] matches pattern[j] if pattern[j]=="*", dp[i][j] = any(dp[m][j-1]) w.r.t m Compact the 2-D dp to 1-D dp: iterate through j, since we only need know j-1 state, thus dropping the dimension for j in dp :param s: tape, an input string :param p: pattern, a pattern string :return: boolean

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def isMatch_MLE(self, s, p): dp, similar to 011 Regular Expression Matching. Backward dp but Memory Limit Exceeds :param s: tape, an input string :param p: pattern, a pattern string :return: boolean
- def isMatch_forward(self, s, p): "?" is not the problem "*" is the problem Forward dp: dp starting from -1 if pattern[j]!="*", dp[i][j] = dp[i-1][j-1] && tape[i] matches pattern[j] if pattern[j]=="*", dp[i][j] = any(dp[m][j-1]) w.r.t m Compact the 2-D dp to 1-D dp: iterate through j, since we only need know j-1 state, thus dropping the dimension for j in dp :param s: tape, an input string :param p: pattern, a pattern string :return: boolean

<|skeleton|>
class Solution:

    def isMatch_MLE(self, s, p):
        """dp, similar to 011 Regular Expression Matching. Backward dp but Memory Limit Exceeds :param s: tape, an input string :param p: pattern, a pattern string :return: boolean"""
        <|body_0|>

    def isMatch_forward(self, s, p):
        """"?" is not the problem "*" is the problem Forward dp: dp starting from -1 if pattern[j]!="*", dp[i][j] = dp[i-1][j-1] && tape[i] matches pattern[j] if pattern[j]=="*", dp[i][j] = any(dp[m][j-1]) w.r.t m Compact the 2-D dp to 1-D dp: iterate through j, since we only need know j-1 state, thus dropping the dimension for j in dp :param s: tape, an input string :param p: pattern, a pattern string :return: boolean"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        tape = s
        pattern = p
        m = len(tape)
        n = len(pattern)
        dp = [[False for _ in xrange(n + 1)] for _ in xrange(m + 1)]
        dp[m][n] = True
        for j in xrange(n - 1, -1, -1):
            if pattern[j] == '*':
                dp[m][j] = dp[m][j + 1]
        for i in xrange(m - 1, -1, -1):
            for j in xrange(n - 1, -1, -1):
                if tape[i] == pattern[j] or pattern[j] == '?':
                    dp[i][j] = dp[i + 1][j + 1]
                elif pattern[j] == '*':
                    dp[i][j] = dp[i][j + 1] or dp[i + 1][j]
                else:
                    dp[i][j] = False
        return dp[0][0]
<|end_body_0|>

<|body_start_1|>
        tape = s
        pattern = p
        m = len(tape)
        n = len(pattern)
        if n - list(pattern).count('*') > m:
            return False
        dp = [False for _ in xrange(m + 1)]
        dp[0] = True
        for j in xrange(1, n + 1):
            if pattern[j - 1] == '*':
                k = 0
                while k < m + 1 and dp[k] != True:
                    k += 1
                for i in xrange(k, m + 1):
                    dp[i] = True
            else:
                for i in xrange(m, 0, -1):
                    dp[i] = dp[i - 1] and (tape[i - 1] == pattern[j - 1] or pattern[j - 1] == '?')
            dp[0] = dp[0] and pattern[j - 1] == '*'
        return dp[m]
<|end_body_1|>

Solution:
class Solution:
    def isMatch_MLE(self, s, p):
        """dp, similar to 011 Regular Expression Matching. Backward dp but Memory Limit Exceeds :param s: tape, an input string :param p: pattern, a pattern string :return: boolean"""
        tape = s
        pattern = p
        m = len(tape)
        n = len(pattern)
        dp = [[False for _ in xrange(n + 1)] for _ in xrange(m + 1)]
        dp[m][n] = True
        for j in xrange(n - 1, -1, -1):
            if pattern[j] == '*':
                dp[m][j] = dp[m][j + 1]
        for i in xrange(m - 1, -1, -1):
            for j in xrange(n - 1, -1, -1):
                if tape[i] == pattern[j] or pattern[j] == '?':
                    dp[i][j] = dp[i + 1][j + 1]
                elif pattern[j] == '*':
                    dp[i][j] = dp[i][j + 1] or dp[i + 1][j]
                else:
                    dp[i][j] = False
        return dp[0][0]

    def isMatch_forward(self, s, p):
        """"?" is not the problem "*" is the problem Forward dp: dp starting from -1 if pattern[j]!="*", dp[i][j] = dp[i-1][j-1] && tape[i] matches pattern[j] if pattern[j]=="*", dp[i][j] = any(dp[m][j-1]) w.r.t m Compact the 2-D dp to 1-D dp: iterate through j, since we only need know j-1 state, thus dropping the dimension for j in dp :param s: tape, an input string :param p: pattern, a pattern string :return: boolean"""
        tape = s
        pattern = p
        m = len(tape)
        n = len(pattern)
        if n - list(pattern).count('*') > m:
            return False
        dp = [False for _ in xrange(m + 1)]
        dp[0] = True
        for j in xrange(1, n + 1):
            if pattern[j - 1] == '*':
                k = 0
                while k < m + 1 and dp[k] != True:
                    k += 1
                for i in xrange(k, m + 1):
                    dp[i] = True
            else:
                for i in xrange(m, 0, -1):
                    dp[i] = dp[i - 1] and (tape[i - 1] == pattern[j - 1] or pattern[j - 1] == '?')
            dp[0] = dp[0] and pattern[j - 1] == '*'
        return dp[m]


constrain

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

Class description:
Implement the BoxEmb class.

Method signatures and docstrings:
- def __init__(self, vocab_size, embedding_dim): param emb: embedding class param embedding_dim: embedding dimension param rnn_hidden_size: rnn_hidden_size dimension param is_bidirectional: is the rnn bidirectional
- def embed(self, set_lkup): Embed using average string embedding :param set_lkup: :return: constrained emb # [batch_size, max_len_token, emb_dim/2, 2]
- def constrain(self, emb): Ensure that element at index 0 is less than element at index 1 :param emb: # [batch_size, max_len_token, emb_dim/2, 2] :return:

Implement the Python class `BoxEmb` described below.

Class description:
Implement the BoxEmb class.

Method signatures and docstrings:
- def __init__(self, vocab_size, embedding_dim): param emb: embedding class param embedding_dim: embedding dimension param rnn_hidden_size: rnn_hidden_size dimension param is_bidirectional: is the rnn bidirectional
- def embed(self, set_lkup): Embed using average string embedding :param set_lkup: :return: constrained emb # [batch_size, max_len_token, emb_dim/2, 2]
- def constrain(self, emb): Ensure that element at index 0 is less than element at index 1 :param emb: # [batch_size, max_len_token, emb_dim/2, 2] :return:

<|skeleton|>
class BoxEmb:

    def __init__(self, vocab_size, embedding_dim):
        """param emb: embedding class param embedding_dim: embedding dimension param rnn_hidden_size: rnn_hidden_size dimension param is_bidirectional: is the rnn bidirectional"""
        <|body_0|>

    def embed(self, set_lkup):
        """Embed using average string embedding :param set_lkup: :return: constrained emb # [batch_size, max_len_token, emb_dim/2, 2]"""
        <|body_1|>

    def constrain(self, emb):
        """Ensure that element at index 0 is less than element at index 1 :param emb: # [batch_size, max_len_token, emb_dim/2, 2] :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(BoxEmb, self).__init__()
        half_emb_dim = int(embedding_dim / 2)
        min = torch.empty(vocab_size * half_emb_dim).normal_(mean=0, std=0.2)
        delta = torch.log(torch.empty(vocab_size * half_emb_dim).normal_(mean=1, std=0.2))
        init_emb = torch.stack([min, delta], dim=1)
        init_emb = init_emb.reshape(vocab_size, embedding_dim)
        self.emb = EMB(vocab_size, embedding_dim, init_emb=init_emb)
<|end_body_0|>

<|body_start_1|>
        string_emb, mask = self.emb(torch.from_numpy(np.asarray(set_lkup)).to(device))
        string_constrain_emb = self.constrain(string_emb)
        return (string_constrain_emb, mask)
<|end_body_1|>

<|body_start_2|>
        batch_size, max_len_token, emb_dim = emb.shape
        half_emb_dim = int(emb_dim / 2)
        emb_reshape = emb.reshape(batch_size * max_len_token, emb_dim)
        emb_reshape = emb_reshape.reshape(batch_size, max_len_token, half_emb_dim, 2)
        min_val = emb_reshape[:, :, :, 0]
        delta_val = torch.exp(emb_reshape[:, :, :, 1])
        constrain_emb = torch.stack([min_val, delta_val], dim=3)
        return constrain_emb
<|end_body_2|>

Solution:
class BoxEmb:
    def __init__(self, vocab_size, embedding_dim):
        """param emb: embedding class param embedding_dim: embedding dimension param rnn_hidden_size: rnn_hidden_size dimension param is_bidirectional: is the rnn bidirectional"""
        super(BoxEmb, self).__init__()
        half_emb_dim = int(embedding_dim / 2)
        min = torch.empty(vocab_size * half_emb_dim).normal_(mean=0, std=0.2)
        delta = torch.log(torch.empty(vocab_size * half_emb_dim).normal_(mean=1, std=0.2))
        init_emb = torch.stack([min, delta], dim=1)
        init_emb = init_emb.reshape(vocab_size, embedding_dim)
        self.emb = EMB(vocab_size, embedding_dim, init_emb=init_emb)

    def embed(self, set_lkup):
        """Embed using average string embedding :param set_lkup: :return: constrained emb # [batch_size, max_len_token, emb_dim/2, 2]"""
        string_emb, mask = self.emb(torch.from_numpy(np.asarray(set_lkup)).to(device))
        string_constrain_emb = self.constrain(string_emb)
        return (string_constrain_emb, mask)

    def constrain(self, emb):
        """Ensure that element at index 0 is less than element at index 1 :param emb: # [batch_size, max_len_token, emb_dim/2, 2] :return:"""
        batch_size, max_len_token, emb_dim = emb.shape
        half_emb_dim = int(emb_dim / 2)
        emb_reshape = emb.reshape(batch_size * max_len_token, emb_dim)
        emb_reshape = emb_reshape.reshape(batch_size, max_len_token, half_emb_dim, 2)
        min_val = emb_reshape[:, :, :, 0]
        delta_val = torch.exp(emb_reshape[:, :, :, 1])
        constrain_emb = torch.stack([min_val, delta_val], dim=3)
        return constrain_emb


HandleRequest

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

Class description:
Servlet to get all of a user's projects in JSON format.

Method signatures and docstrings:
- def HandleRequest(self, mr): Retrieve list of a user's projects for the "My projects" menu. Args: mr: common information parsed from the HTTP request. Returns: Results dictionary in JSON format
- def _GatherProjects(self, mr): Return a dict of project names the current user is involved in.

Implement the Python class `ProjectsJsonFeed` described below.

Class description:
Servlet to get all of a user's projects in JSON format.

Method signatures and docstrings:
- def HandleRequest(self, mr): Retrieve list of a user's projects for the "My projects" menu. Args: mr: common information parsed from the HTTP request. Returns: Results dictionary in JSON format
- def _GatherProjects(self, mr): Return a dict of project names the current user is involved in.

<|skeleton|>
class ProjectsJsonFeed:
    """Servlet to get all of a user's projects in JSON format."""

    def HandleRequest(self, mr):
        """Retrieve list of a user's projects for the "My projects" menu. Args: mr: common information parsed from the HTTP request. Returns: Results dictionary in JSON format"""
        <|body_0|>

    def _GatherProjects(self, mr):
        """Return a dict of project names the current user is involved in."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not mr.auth.user_id:
            return {'error': 'User is not logged in.'}
        json_data = {}
        with self.profiler.Phase('page processing'):
            json_data.update(self._GatherProjects(mr))
        return json_data
<|end_body_0|>

<|body_start_1|>
        with self.profiler.Phase('GetUserProjects'):
            project_lists = sitewide_helpers.GetUserProjects(mr.cnxn, self.services, mr.auth.user_pb, mr.auth.effective_ids, mr.auth.effective_ids)
            visible_ownership, _visible_deleted, visible_membership, visible_contrib = project_lists
        with self.profiler.Phase('GetStarredProjects'):
            starred_projects = sitewide_helpers.GetViewableStarredProjects(mr.cnxn, self.services, mr.auth.user_id, mr.auth.effective_ids, mr.auth.user_pb)
        projects_dict = {'memberof': [p.project_name for p in visible_membership], 'ownerof': [p.project_name for p in visible_ownership], 'contributorto': [p.project_name for p in visible_contrib], 'starred_projects': [p.project_name for p in starred_projects]}
        return projects_dict
<|end_body_1|>

Solution:
class ProjectsJsonFeed:
    """Servlet to get all of a user's projects in JSON format."""

    def HandleRequest(self, mr):
        """Retrieve list of a user's projects for the "My projects" menu. Args: mr: common information parsed from the HTTP request. Returns: Results dictionary in JSON format"""
        if not mr.auth.user_id:
            return {'error': 'User is not logged in.'}
        json_data = {}
        with self.profiler.Phase('page processing'):
            json_data.update(self._GatherProjects(mr))
        return json_data

    def _GatherProjects(self, mr):
        """Return a dict of project names the current user is involved in."""
        with self.profiler.Phase('GetUserProjects'):
            project_lists = sitewide_helpers.GetUserProjects(mr.cnxn, self.services, mr.auth.user_pb, mr.auth.effective_ids, mr.auth.effective_ids)
            visible_ownership, _visible_deleted, visible_membership, visible_contrib = project_lists
        with self.profiler.Phase('GetStarredProjects'):
            starred_projects = sitewide_helpers.GetViewableStarredProjects(mr.cnxn, self.services, mr.auth.user_id, mr.auth.effective_ids, mr.auth.user_pb)
        projects_dict = {'memberof': [p.project_name for p in visible_membership], 'ownerof': [p.project_name for p in visible_ownership], 'contributorto': [p.project_name for p in visible_contrib], 'starred_projects': [p.project_name for p in starred_projects]}
        return projects_dict


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

Class description:
Implement the CreatePostRequestBody class.

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

Implement the Python class `CreatePostRequestBody` described below.

Class description:
Implement the CreatePostRequestBody class.

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

<|skeleton|>
class CreatePostRequestBody:

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

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

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

<|end_skeleton|>

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

<|body_start_1|>
        from ....models.print_certificate_signing_request import PrintCertificateSigningRequest
        from ....models.print_certificate_signing_request import PrintCertificateSigningRequest
        fields: Dict[str, Callable[[Any], None]] = {'certificateSigningRequest': lambda n: setattr(self, 'certificate_signing_request', n.get_object_value(PrintCertificateSigningRequest)), 'connectorId': lambda n: setattr(self, 'connector_id', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'hasPhysicalDevice': lambda n: setattr(self, 'has_physical_device', n.get_bool_value()), 'manufacturer': lambda n: setattr(self, 'manufacturer', n.get_str_value()), 'model': lambda n: setattr(self, 'model', n.get_str_value()), 'physicalDeviceId': lambda n: setattr(self, 'physical_device_id', n.get_str_value())}
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        writer.write_object_value('certificateSigningRequest', self.certificate_signing_request)
        writer.write_str_value('connectorId', self.connector_id)
        writer.write_str_value('displayName', self.display_name)
        writer.write_bool_value('hasPhysicalDevice', self.has_physical_device)
        writer.write_str_value('manufacturer', self.manufacturer)
        writer.write_str_value('model', self.model)
        writer.write_str_value('physicalDeviceId', self.physical_device_id)
        writer.write_additional_data_value(self.additional_data)
<|end_body_2|>

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

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from ....models.print_certificate_signing_request import PrintCertificateSigningRequest
        from ....models.print_certificate_signing_request import PrintCertificateSigningRequest
        fields: Dict[str, Callable[[Any], None]] = {'certificateSigningRequest': lambda n: setattr(self, 'certificate_signing_request', n.get_object_value(PrintCertificateSigningRequest)), 'connectorId': lambda n: setattr(self, 'connector_id', n.get_str_value()), 'displayName': lambda n: setattr(self, 'display_name', n.get_str_value()), 'hasPhysicalDevice': lambda n: setattr(self, 'has_physical_device', n.get_bool_value()), 'manufacturer': lambda n: setattr(self, 'manufacturer', n.get_str_value()), 'model': lambda n: setattr(self, 'model', n.get_str_value()), 'physicalDeviceId': lambda n: setattr(self, 'physical_device_id', n.get_str_value())}
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        writer.write_object_value('certificateSigningRequest', self.certificate_signing_request)
        writer.write_str_value('connectorId', self.connector_id)
        writer.write_str_value('displayName', self.display_name)
        writer.write_bool_value('hasPhysicalDevice', self.has_physical_device)
        writer.write_str_value('manufacturer', self.manufacturer)
        writer.write_str_value('model', self.model)
        writer.write_str_value('physicalDeviceId', self.physical_device_id)
        writer.write_additional_data_value(self.additional_data)


intensity

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

Class description:
Class describing a spot light source

Method signatures and docstrings:
- def __init__(self, position: Vec3, direction: Vec3, p: int, a=0.02, b=0.1, c=0) -> None: Constructor: param position: position of the light param direction: direction of the light param p: controls how much the spotlight is focussed params a, b, c: quadratic equation coefficients for ad^2 + bd + c
- def intensity(self, shadowray): Point intensity calculation: param shadowray: ray from light to hitrecord point

Implement the Python class `SpotLight` described below.

Class description:
Class describing a spot light source

Method signatures and docstrings:
- def __init__(self, position: Vec3, direction: Vec3, p: int, a=0.02, b=0.1, c=0) -> None: Constructor: param position: position of the light param direction: direction of the light param p: controls how much the spotlight is focussed params a, b, c: quadratic equation coefficients for ad^2 + bd + c
- def intensity(self, shadowray): Point intensity calculation: param shadowray: ray from light to hitrecord point

<|skeleton|>
class SpotLight:
    """Class describing a spot light source"""

    def __init__(self, position: Vec3, direction: Vec3, p: int, a=0.02, b=0.1, c=0) -> None:
        """Constructor: param position: position of the light param direction: direction of the light param p: controls how much the spotlight is focussed params a, b, c: quadratic equation coefficients for ad^2 + bd + c"""
        <|body_0|>

    def intensity(self, shadowray):
        """Point intensity calculation: param shadowray: ray from light to hitrecord point"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        Light.__init__(self, 'SpotLight')
        self.position = position
        self.direction = direction
        self.p = p
        self.dir_ray = Ray(position, self.direction - self.position)
        self.a = a
        self.b = b
        self.c = c
<|end_body_0|>

<|body_start_1|>
        d = shadowray.direction.length()
        f_att = np.clip(1 / (self.c + self.b * d + self.a * d * d), 0, 1)
        cos_angle = self.direction.dot(-shadowray.direction) / (self.direction.length() * shadowray.direction.length())
        return f_att * cos_angle ** self.p
<|end_body_1|>

Solution:
class SpotLight:
    """Class describing a spot light source"""

    def __init__(self, position: Vec3, direction: Vec3, p: int, a=0.02, b=0.1, c=0) -> None:
        """Constructor: param position: position of the light param direction: direction of the light param p: controls how much the spotlight is focussed params a, b, c: quadratic equation coefficients for ad^2 + bd + c"""
        Light.__init__(self, 'SpotLight')
        self.position = position
        self.direction = direction
        self.p = p
        self.dir_ray = Ray(position, self.direction - self.position)
        self.a = a
        self.b = b
        self.c = c

    def intensity(self, shadowray):
        """Point intensity calculation: param shadowray: ray from light to hitrecord point"""
        d = shadowray.direction.length()
        f_att = np.clip(1 / (self.c + self.b * d + self.a * d * d), 0, 1)
        cos_angle = self.direction.dot(-shadowray.direction) / (self.direction.length() * shadowray.direction.length())
        return f_att * cos_angle ** self.p


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

Class description:
Object to store span information.

Method signatures and docstrings:
- def __init__(self, name, index, start, end, export_names, is_header): Set attributes.
- def set_node(self, parent_node=None): Create an XML node under parent_node.
- def __repr__(self): Stringify the most interesting span info (for debugging mostly).
- def __lt__(self, other_span): Return True if other_span comes after this span. Sort spans according to their position and hierarchy. Sort by: 1. start position (smaller indices first) 2. end position (larger indices first) 3. the calculated element hierarchy

Implement the Python class `Span` described below.

Class description:
Object to store span information.

Method signatures and docstrings:
- def __init__(self, name, index, start, end, export_names, is_header): Set attributes.
- def set_node(self, parent_node=None): Create an XML node under parent_node.
- def __repr__(self): Stringify the most interesting span info (for debugging mostly).
- def __lt__(self, other_span): Return True if other_span comes after this span. Sort spans according to their position and hierarchy. Sort by: 1. start position (smaller indices first) 2. end position (larger indices first) 3. the calculated element hierarchy

<|skeleton|>
class Span:
    """Object to store span information."""

    def __init__(self, name, index, start, end, export_names, is_header):
        """Set attributes."""
        <|body_0|>

    def set_node(self, parent_node=None):
        """Create an XML node under parent_node."""
        <|body_1|>

    def __repr__(self):
        """Stringify the most interesting span info (for debugging mostly)."""
        <|body_2|>

    def __lt__(self, other_span):
        """Return True if other_span comes after this span. Sort spans according to their position and hierarchy. Sort by: 1. start position (smaller indices first) 2. end position (larger indices first) 3. the calculated element hierarchy"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.name = name
        self.index = index
        self.start = start[0]
        self.end = end[0]
        self.start_sub = start[1] if len(start) > 1 else False
        self.end_sub = end[1] if len(end) > 1 else False
        self.export = export_names.get(self.name, self.name)
        self.is_header = is_header
        self.node = None
        self.overlap_id = None
<|end_body_0|>

<|body_start_1|>
        if parent_node is not None:
            self.node = etree.SubElement(parent_node, self.export)
        else:
            self.node = etree.Element(self.export)
<|end_body_1|>

<|body_start_2|>
        if self.export != self.name:
            return '<%s/%s %s %s-%s>' % (self.name, self.export, self.index, self.start, self.end)
        return '<%s %s %s-%s>' % (self.name, self.index, self.start, self.end)
<|end_body_2|>

<|body_start_3|>
        def get_sort_key(span, sub_positions=False, empty_span=False):
            """Return a sort key for span which makes span comparison possible."""
            hierarchy_index = elem_hierarchy.index(span.name) if span.name in elem_hierarchy else -1
            if empty_span:
                if sub_positions:
                    return ((span.start, span.start_sub), hierarchy_index, (span.end, span.end_sub))
                else:
                    return (span.start, hierarchy_index, span.end)
            elif sub_positions:
                return ((span.start, span.start_sub), (-span.end, -span.end_sub), hierarchy_index)
            else:
                return (span.start, -span.end, hierarchy_index)
        if (self.start, self.start_sub) == (self.end, self.end_sub) or (other_span.start, other_span.start_sub) == (other_span.end, other_span.end_sub):
            sort_key1 = get_sort_key(self, empty_span=True)
            sort_key2 = get_sort_key(other_span, empty_span=True)
        elif self.start_sub and other_span.start_sub:
            sort_key1 = get_sort_key(self, sub_positions=True)
            sort_key2 = get_sort_key(other_span, sub_positions=True)
        else:
            sort_key1 = get_sort_key(self)
            sort_key2 = get_sort_key(other_span)
        return sort_key1 < sort_key2
<|end_body_3|>

Solution:
class Span:
    """Object to store span information."""

    def __init__(self, name, index, start, end, export_names, is_header):
        """Set attributes."""
        self.name = name
        self.index = index
        self.start = start[0]
        self.end = end[0]
        self.start_sub = start[1] if len(start) > 1 else False
        self.end_sub = end[1] if len(end) > 1 else False
        self.export = export_names.get(self.name, self.name)
        self.is_header = is_header
        self.node = None
        self.overlap_id = None

    def set_node(self, parent_node=None):
        """Create an XML node under parent_node."""
        if parent_node is not None:
            self.node = etree.SubElement(parent_node, self.export)
        else:
            self.node = etree.Element(self.export)

    def __repr__(self):
        """Stringify the most interesting span info (for debugging mostly)."""
        if self.export != self.name:
            return '<%s/%s %s %s-%s>' % (self.name, self.export, self.index, self.start, self.end)
        return '<%s %s %s-%s>' % (self.name, self.index, self.start, self.end)

    def __lt__(self, other_span):
        """Return True if other_span comes after this span. Sort spans according to their position and hierarchy. Sort by: 1. start position (smaller indices first) 2. end position (larger indices first) 3. the calculated element hierarchy"""
        def get_sort_key(span, sub_positions=False, empty_span=False):
            """Return a sort key for span which makes span comparison possible."""
            hierarchy_index = elem_hierarchy.index(span.name) if span.name in elem_hierarchy else -1
            if empty_span:
                if sub_positions:
                    return ((span.start, span.start_sub), hierarchy_index, (span.end, span.end_sub))
                else:
                    return (span.start, hierarchy_index, span.end)
            elif sub_positions:
                return ((span.start, span.start_sub), (-span.end, -span.end_sub), hierarchy_index)
            else:
                return (span.start, -span.end, hierarchy_index)
        if (self.start, self.start_sub) == (self.end, self.end_sub) or (other_span.start, other_span.start_sub) == (other_span.end, other_span.end_sub):
            sort_key1 = get_sort_key(self, empty_span=True)
            sort_key2 = get_sort_key(other_span, empty_span=True)
        elif self.start_sub and other_span.start_sub:
            sort_key1 = get_sort_key(self, sub_positions=True)
            sort_key2 = get_sort_key(other_span, sub_positions=True)
        else:
            sort_key1 = get_sort_key(self)
            sort_key2 = get_sort_key(other_span)
        return sort_key1 < sort_key2


save

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

Class description:
[An AudioAdapter implementation that use FFMPEG binary through subprocess in order to perform I/O operation for audio processing. When created, FFMPEG binary path will be checked and expended, raising exception if not found. Such path could be infered using FFMPEG_PATH environment variable.]

Method signatures and docstrings:
- def load(self, path, offset=None, duration=None, sample_rate=None, dtype=np.float32): [Loads the audio file denoted by the given path and returns it data as a waveform.] Arguments: path {[type]} -- [Path of the audio file to load data from.] Keyword Arguments: offset {[type]} -- [(Optional) Start offset to load from in seconds.] (default: {None}) duration {[type]} -- [(Optional) Duration to load in seconds.] (default: {None}) sample_rate {[type]} -- [(Optional) Sample rate to load audio with.] (default: {None}) dtype {[type]} -- [(Optional) Numpy data type to use, default to float32.] (default: {np.float32})
- def save(self, path, data, sample_rate, codec=None, bitrate=None): [Write waveform data to the file denoted by the given path using FFMPEG process.] Arguments: path {[type]} -- [Path of the audio file to save data in.] data {[type]} -- [Waveform data to write.] sample_rate {[type]} -- [Sample rate to write file in.] Keyword Arguments: codec {[type]} -- [(Optional) Writing codec to use.] (default: {None}) bitrate {[type]} -- [(Optional) Bitrate of the written audio file.] (default: {None})

Implement the Python class `FFMPEGProcessAudioAdapter` described below.

Class description:
[An AudioAdapter implementation that use FFMPEG binary through subprocess in order to perform I/O operation for audio processing. When created, FFMPEG binary path will be checked and expended, raising exception if not found. Such path could be infered using FFMPEG_PATH environment variable.]

Method signatures and docstrings:
- def load(self, path, offset=None, duration=None, sample_rate=None, dtype=np.float32): [Loads the audio file denoted by the given path and returns it data as a waveform.] Arguments: path {[type]} -- [Path of the audio file to load data from.] Keyword Arguments: offset {[type]} -- [(Optional) Start offset to load from in seconds.] (default: {None}) duration {[type]} -- [(Optional) Duration to load in seconds.] (default: {None}) sample_rate {[type]} -- [(Optional) Sample rate to load audio with.] (default: {None}) dtype {[type]} -- [(Optional) Numpy data type to use, default to float32.] (default: {np.float32})
- def save(self, path, data, sample_rate, codec=None, bitrate=None): [Write waveform data to the file denoted by the given path using FFMPEG process.] Arguments: path {[type]} -- [Path of the audio file to save data in.] data {[type]} -- [Waveform data to write.] sample_rate {[type]} -- [Sample rate to write file in.] Keyword Arguments: codec {[type]} -- [(Optional) Writing codec to use.] (default: {None}) bitrate {[type]} -- [(Optional) Bitrate of the written audio file.] (default: {None})

<|skeleton|>
class FFMPEGProcessAudioAdapter:
    """[An AudioAdapter implementation that use FFMPEG binary through subprocess in order to perform I/O operation for audio processing. When created, FFMPEG binary path will be checked and expended, raising exception if not found. Such path could be infered using FFMPEG_PATH environment variable.]"""

    def load(self, path, offset=None, duration=None, sample_rate=None, dtype=np.float32):
        """[Loads the audio file denoted by the given path and returns it data as a waveform.] Arguments: path {[type]} -- [Path of the audio file to load data from.] Keyword Arguments: offset {[type]} -- [(Optional) Start offset to load from in seconds.] (default: {None}) duration {[type]} -- [(Optional) Duration to load in seconds.] (default: {None}) sample_rate {[type]} -- [(Optional) Sample rate to load audio with.] (default: {None}) dtype {[type]} -- [(Optional) Numpy data type to use, default to float32.] (default: {np.float32})"""
        <|body_0|>

    def save(self, path, data, sample_rate, codec=None, bitrate=None):
        """[Write waveform data to the file denoted by the given path using FFMPEG process.] Arguments: path {[type]} -- [Path of the audio file to save data in.] data {[type]} -- [Waveform data to write.] sample_rate {[type]} -- [Sample rate to write file in.] Keyword Arguments: codec {[type]} -- [(Optional) Writing codec to use.] (default: {None}) bitrate {[type]} -- [(Optional) Bitrate of the written audio file.] (default: {None})"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not isinstance(path, str):
            path = path.decode()
        try:
            probe = ffmpeg.probe(path)
        except ffmpeg._run.Error as e:
            raise Exception('An error occurs with ffprobe (see ffprobe output below)\n\n{}'.format(e.stderr.decode()))
        if 'streams' not in probe or len(probe['streams']) == 0:
            raise Exception('No stream was found with ffprobe')
        metadata = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'))
        n_channels = metadata['channels']
        if sample_rate is None:
            sample_rate = metadata['sample_rate']
        output_kwargs = {'format': 'f32le', 'ar': sample_rate}
        if duration is not None:
            output_kwargs['t'] = _to_ffmpeg_time(duration)
        if offset is not None:
            output_kwargs['ss'] = _to_ffmpeg_time(offset)
        process = ffmpeg.input(path).output('pipe:', **output_kwargs).run_async(pipe_stdout=True, pipe_stderr=True)
        buffer, _ = process.communicate()
        waveform = np.frombuffer(buffer, dtype='<f4').reshape(-1, n_channels)
        if not waveform.dtype == np.dtype(dtype):
            waveform = waveform.astype(dtype)
        return (waveform, sample_rate)
<|end_body_0|>

<|body_start_1|>
        directory = os.path.dirname(path)
        if not os.path.exists(directory):
            raise IOError(f'output directory does not exists: {directory}')
        input_kwargs = {'ar': sample_rate, 'ac': data.shape[1]}
        output_kwargs = {'ar': sample_rate, 'strict': '-2'}
        if bitrate:
            output_kwargs['audio_bitrate'] = bitrate
        if codec is not None and codec != 'wav':
            output_kwargs['codec'] = _to_ffmpeg_codec(codec)
        process = ffmpeg.input('pipe:', format='f32le', **input_kwargs).output(path, **output_kwargs).overwrite_output().run_async(pipe_stdin=True, pipe_stderr=True, quiet=True)
        try:
            process.stdin.write(data.astype('<f4').tobytes())
            process.stdin.close()
            process.wait()
        except IOError:
            raise IOError(f'FFMPEG error: {process.stderr.read()}')
<|end_body_1|>

Solution:
class FFMPEGProcessAudioAdapter:
    """[An AudioAdapter implementation that use FFMPEG binary through subprocess in order to perform I/O operation for audio processing. When created, FFMPEG binary path will be checked and expended, raising exception if not found. Such path could be infered using FFMPEG_PATH environment variable.]"""

    def load(self, path, offset=None, duration=None, sample_rate=None, dtype=np.float32):
        """[Loads the audio file denoted by the given path and returns it data as a waveform.] Arguments: path {[type]} -- [Path of the audio file to load data from.] Keyword Arguments: offset {[type]} -- [(Optional) Start offset to load from in seconds.] (default: {None}) duration {[type]} -- [(Optional) Duration to load in seconds.] (default: {None}) sample_rate {[type]} -- [(Optional) Sample rate to load audio with.] (default: {None}) dtype {[type]} -- [(Optional) Numpy data type to use, default to float32.] (default: {np.float32})"""
        if not isinstance(path, str):
            path = path.decode()
        try:
            probe = ffmpeg.probe(path)
        except ffmpeg._run.Error as e:
            raise Exception('An error occurs with ffprobe (see ffprobe output below)\n\n{}'.format(e.stderr.decode()))
        if 'streams' not in probe or len(probe['streams']) == 0:
            raise Exception('No stream was found with ffprobe')
        metadata = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'))
        n_channels = metadata['channels']
        if sample_rate is None:
            sample_rate = metadata['sample_rate']
        output_kwargs = {'format': 'f32le', 'ar': sample_rate}
        if duration is not None:
            output_kwargs['t'] = _to_ffmpeg_time(duration)
        if offset is not None:
            output_kwargs['ss'] = _to_ffmpeg_time(offset)
        process = ffmpeg.input(path).output('pipe:', **output_kwargs).run_async(pipe_stdout=True, pipe_stderr=True)
        buffer, _ = process.communicate()
        waveform = np.frombuffer(buffer, dtype='<f4').reshape(-1, n_channels)
        if not waveform.dtype == np.dtype(dtype):
            waveform = waveform.astype(dtype)
        return (waveform, sample_rate)

    def save(self, path, data, sample_rate, codec=None, bitrate=None):
        """[Write waveform data to the file denoted by the given path using FFMPEG process.] Arguments: path {[type]} -- [Path of the audio file to save data in.] data {[type]} -- [Waveform data to write.] sample_rate {[type]} -- [Sample rate to write file in.] Keyword Arguments: codec {[type]} -- [(Optional) Writing codec to use.] (default: {None}) bitrate {[type]} -- [(Optional) Bitrate of the written audio file.] (default: {None})"""
        directory = os.path.dirname(path)
        if not os.path.exists(directory):
            raise IOError(f'output directory does not exists: {directory}')
        input_kwargs = {'ar': sample_rate, 'ac': data.shape[1]}
        output_kwargs = {'ar': sample_rate, 'strict': '-2'}
        if bitrate:
            output_kwargs['audio_bitrate'] = bitrate
        if codec is not None and codec != 'wav':
            output_kwargs['codec'] = _to_ffmpeg_codec(codec)
        process = ffmpeg.input('pipe:', format='f32le', **input_kwargs).output(path, **output_kwargs).overwrite_output().run_async(pipe_stdin=True, pipe_stderr=True, quiet=True)
        try:
            process.stdin.write(data.astype('<f4').tobytes())
            process.stdin.close()
            process.wait()
        except IOError:
            raise IOError(f'FFMPEG error: {process.stderr.read()}')


get

查询所有可用的角色（组）

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

Class description:
Implement the GroupEndpoint class.

Method signatures and docstrings:
- def get(self, request): 查询所有可用的角色（组）
- def post(self, request): 新增一个角色
- def delete(self, request): 删除一个角色

Implement the Python class `GroupEndpoint` described below.

Class description:
Implement the GroupEndpoint class.

Method signatures and docstrings:
- def get(self, request): 查询所有可用的角色（组）
- def post(self, request): 新增一个角色
- def delete(self, request): 删除一个角色

<|skeleton|>
class GroupEndpoint:

    def get(self, request):
        """查询所有可用的角色（组）"""
        <|body_0|>

    def post(self, request):
        """新增一个角色"""
        <|body_1|>

    def delete(self, request):
        """删除一个角色"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if not request.user.has_perm('auth.view_group'):
            raise PermissionDenied()
        role_name = request.GET.get('name', None)
        data_source = Group.objects.prefetch_related('permissions').prefetch_related('user_set__groups')
        if role_name:
            self.queryset = data_source.filter(name=role_name)
        else:
            self.queryset = data_source.all()
        data = []
        for i in self.queryset:
            data.append({'id': i.id, 'name': i.name, 'default': i.default_group.default, 'permissions': build_cn_permission_list([p.codename for p in i.permissions.all()], 'short'), 'users': i.user_set.all().values('username', 'first_name')})
        return Response({'code': 0, 'msg': 'success', 'data': data})
<|end_body_0|>

<|body_start_1|>
        fields = [('*name', str, (verify_max_length, 20))]
        data = validate_post_data(request.body, fields)
        self.queryset, _ = Group.objects.update_or_create(name=data['name'])
        return Response({'code': 0, 'msg': 'success'}, status=HTTP_201_CREATED)
<|end_body_1|>

<|body_start_2|>
        group_id = request.GET.get('id', None)
        try:
            self.queryset = Group.objects.get(pk=int(group_id))
        except TypeError:
            raise ParseError('id is not a number')
        except Group.DoesNotExist:
            raise NotFound('not found this group')
        self.queryset.delete()
        return Response({'code': 0, 'msg': 'success'})
<|end_body_2|>

Solution:
class GroupEndpoint:
    def get(self, request):
        """查询所有可用的角色（组）"""
        if not request.user.has_perm('auth.view_group'):
            raise PermissionDenied()
        role_name = request.GET.get('name', None)
        data_source = Group.objects.prefetch_related('permissions').prefetch_related('user_set__groups')
        if role_name:
            self.queryset = data_source.filter(name=role_name)
        else:
            self.queryset = data_source.all()
        data = []
        for i in self.queryset:
            data.append({'id': i.id, 'name': i.name, 'default': i.default_group.default, 'permissions': build_cn_permission_list([p.codename for p in i.permissions.all()], 'short'), 'users': i.user_set.all().values('username', 'first_name')})
        return Response({'code': 0, 'msg': 'success', 'data': data})

    def post(self, request):
        """新增一个角色"""
        fields = [('*name', str, (verify_max_length, 20))]
        data = validate_post_data(request.body, fields)
        self.queryset, _ = Group.objects.update_or_create(name=data['name'])
        return Response({'code': 0, 'msg': 'success'}, status=HTTP_201_CREATED)

    def delete(self, request):
        """删除一个角色"""
        group_id = request.GET.get('id', None)
        try:
            self.queryset = Group.objects.get(pk=int(group_id))
        except TypeError:
            raise ParseError('id is not a number')
        except Group.DoesNotExist:
            raise NotFound('not found this group')
        self.queryset.delete()
        return Response({'code': 0, 'msg': 'success'})


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

Class description:
Shows a list of all projects, and lets you to add new projects.

Method signatures and docstrings:
- def get(self): List all projects
- def post(self): Create new project

Implement the Python class `ProjectList` described below.

Class description:
Shows a list of all projects, and lets you to add new projects.

Method signatures and docstrings:
- def get(self): List all projects
- def post(self): Create new project

<|skeleton|>
class ProjectList:
    """Shows a list of all projects, and lets you to add new projects."""

    def get(self):
        """List all projects"""
        <|body_0|>

    def post(self):
        """Create new project"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        data = parser.parse_args()
        project_type = data.get('type')
        if project_type == 'annotation':
            return project_dao.all(project_type=TASK_ANNOTATION)
        elif project_type == 'fusion':
            return project_dao.all(project_type=TASK_FUSION)
        return project_dao.all()
<|end_body_0|>

<|body_start_1|>
        req_dict = api.payload
        name = req_dict.get('name')
        if not name:
            return abort(400, 'Invalid parameters.')
        return project_dao.create(api.payload)
<|end_body_1|>

Solution:
class ProjectList:
    """Shows a list of all projects, and lets you to add new projects."""

    def get(self):
        """List all projects"""
        data = parser.parse_args()
        project_type = data.get('type')
        if project_type == 'annotation':
            return project_dao.all(project_type=TASK_ANNOTATION)
        elif project_type == 'fusion':
            return project_dao.all(project_type=TASK_FUSION)
        return project_dao.all()

    def post(self):
        """Create new project"""
        req_dict = api.payload
        name = req_dict.get('name')
        if not name:
            return abort(400, 'Invalid parameters.')
        return project_dao.create(api.payload)


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

Class description:
Implement the VirtualizationReport class.

Method signatures and docstrings:
- def test_cluster_exists(self): Cluster exists for site.
- def test_vms_exist(self): Correct number of VMs (account for special tag or not)

Implement the Python class `VirtualizationReport` described below.

Class description:
Implement the VirtualizationReport class.

Method signatures and docstrings:
- def test_cluster_exists(self): Cluster exists for site.
- def test_vms_exist(self): Correct number of VMs (account for special tag or not)

<|skeleton|>
class VirtualizationReport:

    def test_cluster_exists(self):
        """Cluster exists for site."""
        <|body_0|>

    def test_vms_exist(self):
        """Correct number of VMs (account for special tag or not)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        sites = Site.objects.filter(status=SiteStatusChoices.STATUS_ACTIVE)
        missing_clusters = Site.objects.filter(clusters__isnull=True).exclude(tags__name__in=['no-cluster'])
        for site in sites:
            if site in missing_clusters:
                self.log_failure(site, 'Missing VM cluster')
            else:
                self.log_success(site)
<|end_body_0|>

<|body_start_1|>
        sites = Site.objects.filter(status__in=[1, 2]).prefetch_related('tags').annotate(vm_count=Count('clusters__virtual_machines')).order_by('name')
        for site in sites:
            tags = site.tags.names()
            desired_count = 2
            special_tag = ''
            if 'special_tag' in [tag for tag in tags]:
                desired_count = 3
                special_tag = ' special_tag'
            if not site.vm_count:
                self.log_failure(site, 'No VMs ({}/{})'.format(site.vm_count, desired_count))
            elif site.vm_count == desired_count:
                self.log_success(site)
            elif site.vm_count > desired_count:
                self.log_warning(site, 'Too many VMs ({}/{})'.format(site.vm_count, desired_count))
            elif site.vm_count < desired_count:
                self.log_warning(site, 'Too few VMs ({}/{}){}'.format(site.vm_count, desired_count, special_tag))
            else:
                self.log_info(site, 'Unknown status')
<|end_body_1|>

Solution:
class VirtualizationReport:
    def test_cluster_exists(self):
        """Cluster exists for site."""
        sites = Site.objects.filter(status=SiteStatusChoices.STATUS_ACTIVE)
        missing_clusters = Site.objects.filter(clusters__isnull=True).exclude(tags__name__in=['no-cluster'])
        for site in sites:
            if site in missing_clusters:
                self.log_failure(site, 'Missing VM cluster')
            else:
                self.log_success(site)

    def test_vms_exist(self):
        """Correct number of VMs (account for special tag or not)"""
        sites = Site.objects.filter(status__in=[1, 2]).prefetch_related('tags').annotate(vm_count=Count('clusters__virtual_machines')).order_by('name')
        for site in sites:
            tags = site.tags.names()
            desired_count = 2
            special_tag = ''
            if 'special_tag' in [tag for tag in tags]:
                desired_count = 3
                special_tag = ' special_tag'
            if not site.vm_count:
                self.log_failure(site, 'No VMs ({}/{})'.format(site.vm_count, desired_count))
            elif site.vm_count == desired_count:
                self.log_success(site)
            elif site.vm_count > desired_count:
                self.log_warning(site, 'Too many VMs ({}/{})'.format(site.vm_count, desired_count))
            elif site.vm_count < desired_count:
                self.log_warning(site, 'Too few VMs ({}/{}){}'.format(site.vm_count, desired_count, special_tag))
            else:
                self.log_info(site, 'Unknown status')


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def summaryRanges_group(self, nums): :type nums: List[int] :rtype: List[str]
- def summaryRanges_onepass(self, nums): :type nums: List[int] :rtype: List[str]

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def summaryRanges_group(self, nums): :type nums: List[int] :rtype: List[str]
- def summaryRanges_onepass(self, nums): :type nums: List[int] :rtype: List[str]

<|skeleton|>
class Solution:

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

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

<|end_skeleton|>

<|body_start_0|>
        ans = []
        start = 0
        for p, group in itertools.groupby([n - v for n, v in enumerate(nums)]):
            interval = len(list(group))
            end = start + interval
            if interval == 1:
                ans.append(str(nums[start]))
            else:
                ans.append('{}->{}'.format(nums[start], nums[end - 1]))
            start = end
        return ans
<|end_body_0|>

<|body_start_1|>
        start = 0
        ans = []
        nums.append(float('INF'))
        for i in range(1, len(nums)):
            if nums[i] > nums[i - 1] + 1:
                if i - 1 == start:
                    ans.append(str(nums[start]))
                else:
                    ans.append(str(nums[start]) + '->' + str(nums[i - 1]))
                start = i
        return ans
<|end_body_1|>

Solution:
class Solution:
    def summaryRanges_group(self, nums):
        """:type nums: List[int] :rtype: List[str]"""
        ans = []
        start = 0
        for p, group in itertools.groupby([n - v for n, v in enumerate(nums)]):
            interval = len(list(group))
            end = start + interval
            if interval == 1:
                ans.append(str(nums[start]))
            else:
                ans.append('{}->{}'.format(nums[start], nums[end - 1]))
            start = end
        return ans

    def summaryRanges_onepass(self, nums):
        """:type nums: List[int] :rtype: List[str]"""
        start = 0
        ans = []
        nums.append(float('INF'))
        for i in range(1, len(nums)):
            if nums[i] > nums[i - 1] + 1:
                if i - 1 == start:
                    ans.append(str(nums[start]))
                else:
                    ans.append(str(nums[start]) + '->' + str(nums[i - 1]))
                start = i
        return ans