describe

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

Class description:
This class represents a user with normal vision. It can serve as a base class for other vision-related components.

Method signatures and docstrings:
- def score_perceive(self, web_access, el, edge_metrics): Evaluate the user's ability to perceive el, based on contrast & size Args: web_access: Web interface to interact with the browser el: A particular element on this interface. edge_metrics: EdgeMetrics object that stores data for a user/edge. Returns: contrast_multiplier: perceive_score float in [0, 1]
- def _get_contrast_multiplier(web_access, el, edge_metrics, reg_text_cutoff=4.5, large_text_cutoff=3.0): Returns the contrast_multiplier for a given element on a page. Uses AA WCAG 2 standards: https://webaim.org/articles/contrast/ Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to reg_text_cutoff: min contrast ratio for regular-sized text large_text_cutoff min contrast ratio for large text Returns: contrast_multiplier: 0.0 or 1.0, representing complaince with WCAG contrast_ratio: contrast ratio used by WCAG
- def _get_size_multiplier(web_access, el, edge_metrics, min_pixels=44): Returns the size_multiplier for a given element on a page. Uses AAA WCAG 2: https://www.w3.org/WAI/WCAG21/quickref/#target-size Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to min_pixels: Number of pixels to an element must be in each dimension Returns: size_multiplier: 0.0 or 1.0, representing complaince with WCAG size_xy: tuple (x, y), representing the width, height of the el
- def describe(self, web_access, el): Describes an element based on its innerText, value, type, id, and any labels if they are perceivable. Args: web_access: Web access to the user interface for retrieving actionable elements on a web page. el: WebAccess::Element representing the element to find dist to Returns: A list containing the innerText, value, type, id, and any perceivable labels of an element.

Implement the Python class `VisionAbility` described below.

Class description:
This class represents a user with normal vision. It can serve as a base class for other vision-related components.

Method signatures and docstrings:
- def score_perceive(self, web_access, el, edge_metrics): Evaluate the user's ability to perceive el, based on contrast & size Args: web_access: Web interface to interact with the browser el: A particular element on this interface. edge_metrics: EdgeMetrics object that stores data for a user/edge. Returns: contrast_multiplier: perceive_score float in [0, 1]
- def _get_contrast_multiplier(web_access, el, edge_metrics, reg_text_cutoff=4.5, large_text_cutoff=3.0): Returns the contrast_multiplier for a given element on a page. Uses AA WCAG 2 standards: https://webaim.org/articles/contrast/ Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to reg_text_cutoff: min contrast ratio for regular-sized text large_text_cutoff min contrast ratio for large text Returns: contrast_multiplier: 0.0 or 1.0, representing complaince with WCAG contrast_ratio: contrast ratio used by WCAG
- def _get_size_multiplier(web_access, el, edge_metrics, min_pixels=44): Returns the size_multiplier for a given element on a page. Uses AAA WCAG 2: https://www.w3.org/WAI/WCAG21/quickref/#target-size Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to min_pixels: Number of pixels to an element must be in each dimension Returns: size_multiplier: 0.0 or 1.0, representing complaince with WCAG size_xy: tuple (x, y), representing the width, height of the el
- def describe(self, web_access, el): Describes an element based on its innerText, value, type, id, and any labels if they are perceivable. Args: web_access: Web access to the user interface for retrieving actionable elements on a web page. el: WebAccess::Element representing the element to find dist to Returns: A list containing the innerText, value, type, id, and any perceivable labels of an element.

<|skeleton|>
class VisionAbility:
    """This class represents a user with normal vision. It can serve as a base class for other vision-related components."""

    def score_perceive(self, web_access, el, edge_metrics):
        """Evaluate the user's ability to perceive el, based on contrast & size Args: web_access: Web interface to interact with the browser el: A particular element on this interface. edge_metrics: EdgeMetrics object that stores data for a user/edge. Returns: contrast_multiplier: perceive_score float in [0, 1]"""
        <|body_0|>

    def _get_contrast_multiplier(web_access, el, edge_metrics, reg_text_cutoff=4.5, large_text_cutoff=3.0):
        """Returns the contrast_multiplier for a given element on a page. Uses AA WCAG 2 standards: https://webaim.org/articles/contrast/ Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to reg_text_cutoff: min contrast ratio for regular-sized text large_text_cutoff min contrast ratio for large text Returns: contrast_multiplier: 0.0 or 1.0, representing complaince with WCAG contrast_ratio: contrast ratio used by WCAG"""
        <|body_1|>

    def _get_size_multiplier(web_access, el, edge_metrics, min_pixels=44):
        """Returns the size_multiplier for a given element on a page. Uses AAA WCAG 2: https://www.w3.org/WAI/WCAG21/quickref/#target-size Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to min_pixels: Number of pixels to an element must be in each dimension Returns: size_multiplier: 0.0 or 1.0, representing complaince with WCAG size_xy: tuple (x, y), representing the width, height of the el"""
        <|body_2|>

    def describe(self, web_access, el):
        """Describes an element based on its innerText, value, type, id, and any labels if they are perceivable. Args: web_access: Web access to the user interface for retrieving actionable elements on a web page. el: WebAccess::Element representing the element to find dist to Returns: A list containing the innerText, value, type, id, and any perceivable labels of an element."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        c_multiplier, c_ratio = self._get_contrast_multiplier(web_access, el, edge_metrics)
        edge_metrics.contrast_ratio = c_ratio
        if c_multiplier == 0:
            return 0.0
        size_multiplier, size = self._get_size_multiplier(web_access, el, edge_metrics)
        edge_metrics.size = size
        return size_multiplier * c_multiplier
<|end_body_0|>

<|body_start_1|>
        contrast_ratio = edge_metrics.build_data.contrast_ratio(web_access, None, el)
        font_size = edge_metrics.build_data.font_size(web_access, None, el)
        if font_size < 18:
            if contrast_ratio >= reg_text_cutoff:
                return (1.0, contrast_ratio)
            else:
                return (0.0, contrast_ratio)
        elif contrast_ratio >= large_text_cutoff:
            return (1.0, contrast_ratio)
        else:
            return (0.0, contrast_ratio)
<|end_body_1|>

<|body_start_2|>
        exception_tags = ['li', 'ul']
        height = edge_metrics.build_data.height(web_access, None, el)
        width = edge_metrics.build_data.width(web_access, None, el)
        tag_name = edge_metrics.build_data.tag_name(web_access, None, el)
        if tag_name in exception_tags or (height >= min_pixels and width >= min_pixels):
            return (1.0, (width, height))
        else:
            return (0.0, (width, height))
<|end_body_2|>

<|body_start_3|>
        tags = set()
        tags.add(web_access.get_selenium_element(el).get_attribute('innerText'))
        tags.add(web_access.get_selenium_element(el).get_attribute('value'))
        tags.add(web_access.get_selenium_element(el).get_attribute('type'))
        field_id = web_access.get_selenium_element(el).get_attribute('id')
        if field_id is not None:
            labels = web_access.query_xpath('//label[@for="%s"]' % field_id)
            if labels is not None:
                for label in labels:
                    if self.score_perceive(web_access, label, web_access._create_edge_metrics()) > 0.0:
                        t = web_access.get_selenium_element(label).get_attribute('innerText')
                        if t is not None:
                            tags.add(t)
        return tags
<|end_body_3|>

Solution:
class VisionAbility:
    """This class represents a user with normal vision. It can serve as a base class for other vision-related components."""

    def score_perceive(self, web_access, el, edge_metrics):
        """Evaluate the user's ability to perceive el, based on contrast & size Args: web_access: Web interface to interact with the browser el: A particular element on this interface. edge_metrics: EdgeMetrics object that stores data for a user/edge. Returns: contrast_multiplier: perceive_score float in [0, 1]"""
        c_multiplier, c_ratio = self._get_contrast_multiplier(web_access, el, edge_metrics)
        edge_metrics.contrast_ratio = c_ratio
        if c_multiplier == 0:
            return 0.0
        size_multiplier, size = self._get_size_multiplier(web_access, el, edge_metrics)
        edge_metrics.size = size
        return size_multiplier * c_multiplier

    def _get_contrast_multiplier(web_access, el, edge_metrics, reg_text_cutoff=4.5, large_text_cutoff=3.0):
        """Returns the contrast_multiplier for a given element on a page. Uses AA WCAG 2 standards: https://webaim.org/articles/contrast/ Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to reg_text_cutoff: min contrast ratio for regular-sized text large_text_cutoff min contrast ratio for large text Returns: contrast_multiplier: 0.0 or 1.0, representing complaince with WCAG contrast_ratio: contrast ratio used by WCAG"""
        contrast_ratio = edge_metrics.build_data.contrast_ratio(web_access, None, el)
        font_size = edge_metrics.build_data.font_size(web_access, None, el)
        if font_size < 18:
            if contrast_ratio >= reg_text_cutoff:
                return (1.0, contrast_ratio)
            else:
                return (0.0, contrast_ratio)
        elif contrast_ratio >= large_text_cutoff:
            return (1.0, contrast_ratio)
        else:
            return (0.0, contrast_ratio)

    def _get_size_multiplier(web_access, el, edge_metrics, min_pixels=44):
        """Returns the size_multiplier for a given element on a page. Uses AAA WCAG 2: https://www.w3.org/WAI/WCAG21/quickref/#target-size Args: web_access: Web interface to interact with the browser el: WebAccess::Element representing the element to find dist to min_pixels: Number of pixels to an element must be in each dimension Returns: size_multiplier: 0.0 or 1.0, representing complaince with WCAG size_xy: tuple (x, y), representing the width, height of the el"""
        exception_tags = ['li', 'ul']
        height = edge_metrics.build_data.height(web_access, None, el)
        width = edge_metrics.build_data.width(web_access, None, el)
        tag_name = edge_metrics.build_data.tag_name(web_access, None, el)
        if tag_name in exception_tags or (height >= min_pixels and width >= min_pixels):
            return (1.0, (width, height))
        else:
            return (0.0, (width, height))

    def describe(self, web_access, el):
        """Describes an element based on its innerText, value, type, id, and any labels if they are perceivable. Args: web_access: Web access to the user interface for retrieving actionable elements on a web page. el: WebAccess::Element representing the element to find dist to Returns: A list containing the innerText, value, type, id, and any perceivable labels of an element."""
        tags = set()
        tags.add(web_access.get_selenium_element(el).get_attribute('innerText'))
        tags.add(web_access.get_selenium_element(el).get_attribute('value'))
        tags.add(web_access.get_selenium_element(el).get_attribute('type'))
        field_id = web_access.get_selenium_element(el).get_attribute('id')
        if field_id is not None:
            labels = web_access.query_xpath('//label[@for="%s"]' % field_id)
            if labels is not None:
                for label in labels:
                    if self.score_perceive(web_access, label, web_access._create_edge_metrics()) > 0.0:
                        t = web_access.get_selenium_element(label).get_attribute('innerText')
                        if t is not None:
                            tags.add(t)
        return tags


create

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

Class description:
Manage Attachments in the Power Reg Forum system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, name, post, optional_attributes=None): Create a new Attachment @param name name of the Attachment @type name string @param post post FK @type post int @return a reference to the newly created Attachment

Implement the Python class `ForumPostAttachmentManager` described below.

Class description:
Manage Attachments in the Power Reg Forum system

Method signatures and docstrings:
- def __init__(self): constructor
- def create(self, auth_token, name, post, optional_attributes=None): Create a new Attachment @param name name of the Attachment @type name string @param post post FK @type post int @return a reference to the newly created Attachment

<|skeleton|>
class ForumPostAttachmentManager:
    """Manage Attachments in the Power Reg Forum system"""

    def __init__(self):
        """constructor"""
        <|body_0|>

    def create(self, auth_token, name, post, optional_attributes=None):
        """Create a new Attachment @param name name of the Attachment @type name string @param post post FK @type post int @return a reference to the newly created Attachment"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        ObjectManager.__init__(self)
        self.getters.update({'description': 'get_general', 'name': 'get_general', 'post': 'get_foreign_key'})
        self.setters.update({'description': 'set_general', 'name': 'set_general', 'post': 'set_foreign_key'})
        self.my_django_model = facade.models.ForumPostAttachment
        self.setter = facade.subsystems.Setter
<|end_body_0|>

<|body_start_1|>
        if optional_attributes is None:
            optional_attributes = dict()
        new_attachment = self.my_django_model(name=name)
        new_attachment.blame = facade.managers.BlameManager().create(auth_token)
        new_attachment.post = self._find_by_id(post, facade.models.ForumPost)
        new_attachment.save()
        self.authorizer.check_create_permissions(auth_token, new_attachment)
        return new_attachment
<|end_body_1|>

Solution:
class ForumPostAttachmentManager:
    """Manage Attachments in the Power Reg Forum system"""

    def __init__(self):
        """constructor"""
        ObjectManager.__init__(self)
        self.getters.update({'description': 'get_general', 'name': 'get_general', 'post': 'get_foreign_key'})
        self.setters.update({'description': 'set_general', 'name': 'set_general', 'post': 'set_foreign_key'})
        self.my_django_model = facade.models.ForumPostAttachment
        self.setter = facade.subsystems.Setter

    def create(self, auth_token, name, post, optional_attributes=None):
        """Create a new Attachment @param name name of the Attachment @type name string @param post post FK @type post int @return a reference to the newly created Attachment"""
        if optional_attributes is None:
            optional_attributes = dict()
        new_attachment = self.my_django_model(name=name)
        new_attachment.blame = facade.managers.BlameManager().create(auth_token)
        new_attachment.post = self._find_by_id(post, facade.models.ForumPost)
        new_attachment.save()
        self.authorizer.check_create_permissions(auth_token, new_attachment)
        return new_attachment


process

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

Class description:
Process given input into Sequences.

Method signatures and docstrings:
- def __init__(self, project): Set up local variables for the SequenceProcessor.
- def remove_stops(self, words): Remove every sort of stop from the sentences. :param list words: A list of TaggedWord objects. :return list: The list without stops.
- def process(self, sentence, sequence_dict=None): Iterate and record every four word long sequence. The method records using the ReaderWriter a list of sequences present in the given sentence. :param Sentence sentence: The sentence to process, :return list: A list of Sequence objects, representing the results of processing. These sequences are also sent to the ReaderWriter.
- def get_sequence(self, sentence, i, j): Handle the main processing part in the process() loop. :param Sentence sentence: A sentence object to create sequences from. :param int i: The index to start the sequence from, inclusive. :param int j: The index to stop the sequence from, exclusive. :return list: A list of dicts representing sequences.

Implement the Python class `SequenceProcessor` described below.

Class description:
Process given input into Sequences.

Method signatures and docstrings:
- def __init__(self, project): Set up local variables for the SequenceProcessor.
- def remove_stops(self, words): Remove every sort of stop from the sentences. :param list words: A list of TaggedWord objects. :return list: The list without stops.
- def process(self, sentence, sequence_dict=None): Iterate and record every four word long sequence. The method records using the ReaderWriter a list of sequences present in the given sentence. :param Sentence sentence: The sentence to process, :return list: A list of Sequence objects, representing the results of processing. These sequences are also sent to the ReaderWriter.
- def get_sequence(self, sentence, i, j): Handle the main processing part in the process() loop. :param Sentence sentence: A sentence object to create sequences from. :param int i: The index to start the sequence from, inclusive. :param int j: The index to stop the sequence from, exclusive. :return list: A list of dicts representing sequences.

<|skeleton|>
class SequenceProcessor:
    """Process given input into Sequences."""

    def __init__(self, project):
        """Set up local variables for the SequenceProcessor."""
        <|body_0|>

    def remove_stops(self, words):
        """Remove every sort of stop from the sentences. :param list words: A list of TaggedWord objects. :return list: The list without stops."""
        <|body_1|>

    def process(self, sentence, sequence_dict=None):
        """Iterate and record every four word long sequence. The method records using the ReaderWriter a list of sequences present in the given sentence. :param Sentence sentence: The sentence to process, :return list: A list of Sequence objects, representing the results of processing. These sequences are also sent to the ReaderWriter."""
        <|body_2|>

    def get_sequence(self, sentence, i, j):
        """Handle the main processing part in the process() loop. :param Sentence sentence: A sentence object to create sequences from. :param int i: The index to start the sequence from, inclusive. :param int j: The index to stop the sequence from, exclusive. :return list: A list of dicts representing sequences."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.project = project
        self.previously_indexed = []
        self.logger = logging.getLogger(__name__)
        self.project_logger = ProjectLogger(self.logger, project)
<|end_body_0|>

<|body_start_1|>
        without_stops = []
        for word in words:
            if word.word.lower() not in app.config['STOPWORDS']:
                without_stops.append(word)
        return without_stops
<|end_body_1|>

<|body_start_2|>
        sequences = []
        for i in range(0, len(sentence.words)):
            self.previously_indexed = []
            for j in range(i + 1, len(sentence.words) + 1):
                if j - i < 5:
                    sequences.extend(self.get_sequence(sentence, i, j))
        if isinstance(sequence_dict, dict):
            for sequence in sequences:
                sequence_text = sequence['sequence']
                lemmatized = sequence['is_lemmatized']
                has_function_words = sequence['has_function_words']
                all_function_words = sequence['all_function_words']
                length = len(sequence['words'])
                position = sequence['start_position']
                key = sequence_text
                if key in sequence_dict.keys():
                    sequence = sequence_dict[key]
                else:
                    try:
                        sequence = Sequence.query.filter_by(sequence=sequence_text).one()
                    except MultipleResultsFound:
                        self.project_logger.error('Duplicate records found for: %s', str(key))
                    except NoResultFound:
                        sequence = Sequence(sequence=sequence_text, lemmatized=lemmatized, has_function_words=has_function_words, all_function_words=all_function_words, length=length)
                    sequence_dict[key] = sequence
                sentence.add_sequence(sequence=sequence, position=position, project=self.project, force=False)
        return sequences
<|end_body_2|>

<|body_start_3|>
        sequences = []
        wordlist = sentence.words[i:j]
        lemmatized_phrase = join_tws(wordlist, ' ', 'lemma')
        surface_phrase = join_tws(wordlist, ' ', 'word')
        if surface_phrase in self.previously_indexed:
            return sequences
        wordlist_nostops = self.remove_stops(wordlist)
        lemmatized_phrase_nostops = join_tws(wordlist_nostops, ' ', LEMMA)
        surface_phrase_nostops = join_tws(wordlist_nostops, ' ', WORD)
        has_stops = len(wordlist_nostops) < len(wordlist)
        lemmatized_has_stops = len(lemmatized_phrase_nostops) < len(lemmatized_phrase)
        all_stop_words = len(wordlist_nostops) == 0
        lemmatized_all_stop_words = len(lemmatized_phrase_nostops) == 0
        sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': surface_phrase, 'is_lemmatized': False, 'has_function_words': has_stops, 'all_function_words': all_stop_words, 'words': wordlist})
        self.previously_indexed.append(surface_phrase)
        if has_stops and (not all_stop_words) and (wordlist_nostops[0] == wordlist[0]) and (not surface_phrase_nostops in self.previously_indexed):
            sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': surface_phrase_nostops, 'is_lemmatized': False, 'has_function_words': False, 'all_function_words': False, 'words': wordlist_nostops})
            self.previously_indexed.append(surface_phrase_nostops)
        sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': lemmatized_phrase, 'is_lemmatized': True, 'has_function_words': lemmatized_has_stops, 'all_function_words': lemmatized_all_stop_words, 'words': wordlist})
        self.previously_indexed.append(lemmatized_phrase)
        if lemmatized_has_stops and (not lemmatized_all_stop_words) and (wordlist_nostops[0] == wordlist[0]) and (not lemmatized_phrase_nostops in self.previously_indexed):
            sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': lemmatized_phrase_nostops, 'is_lemmatized': True, 'has_function_words': False, 'all_function_words': False, 'words': wordlist_nostops})
        return sequences
<|end_body_3|>

Solution:
class SequenceProcessor:
    """Process given input into Sequences."""

    def __init__(self, project):
        """Set up local variables for the SequenceProcessor."""
        self.project = project
        self.previously_indexed = []
        self.logger = logging.getLogger(__name__)
        self.project_logger = ProjectLogger(self.logger, project)

    def remove_stops(self, words):
        """Remove every sort of stop from the sentences. :param list words: A list of TaggedWord objects. :return list: The list without stops."""
        without_stops = []
        for word in words:
            if word.word.lower() not in app.config['STOPWORDS']:
                without_stops.append(word)
        return without_stops

    def process(self, sentence, sequence_dict=None):
        """Iterate and record every four word long sequence. The method records using the ReaderWriter a list of sequences present in the given sentence. :param Sentence sentence: The sentence to process, :return list: A list of Sequence objects, representing the results of processing. These sequences are also sent to the ReaderWriter."""
        sequences = []
        for i in range(0, len(sentence.words)):
            self.previously_indexed = []
            for j in range(i + 1, len(sentence.words) + 1):
                if j - i < 5:
                    sequences.extend(self.get_sequence(sentence, i, j))
        if isinstance(sequence_dict, dict):
            for sequence in sequences:
                sequence_text = sequence['sequence']
                lemmatized = sequence['is_lemmatized']
                has_function_words = sequence['has_function_words']
                all_function_words = sequence['all_function_words']
                length = len(sequence['words'])
                position = sequence['start_position']
                key = sequence_text
                if key in sequence_dict.keys():
                    sequence = sequence_dict[key]
                else:
                    try:
                        sequence = Sequence.query.filter_by(sequence=sequence_text).one()
                    except MultipleResultsFound:
                        self.project_logger.error('Duplicate records found for: %s', str(key))
                    except NoResultFound:
                        sequence = Sequence(sequence=sequence_text, lemmatized=lemmatized, has_function_words=has_function_words, all_function_words=all_function_words, length=length)
                    sequence_dict[key] = sequence
                sentence.add_sequence(sequence=sequence, position=position, project=self.project, force=False)
        return sequences

    def get_sequence(self, sentence, i, j):
        """Handle the main processing part in the process() loop. :param Sentence sentence: A sentence object to create sequences from. :param int i: The index to start the sequence from, inclusive. :param int j: The index to stop the sequence from, exclusive. :return list: A list of dicts representing sequences."""
        sequences = []
        wordlist = sentence.words[i:j]
        lemmatized_phrase = join_tws(wordlist, ' ', 'lemma')
        surface_phrase = join_tws(wordlist, ' ', 'word')
        if surface_phrase in self.previously_indexed:
            return sequences
        wordlist_nostops = self.remove_stops(wordlist)
        lemmatized_phrase_nostops = join_tws(wordlist_nostops, ' ', LEMMA)
        surface_phrase_nostops = join_tws(wordlist_nostops, ' ', WORD)
        has_stops = len(wordlist_nostops) < len(wordlist)
        lemmatized_has_stops = len(lemmatized_phrase_nostops) < len(lemmatized_phrase)
        all_stop_words = len(wordlist_nostops) == 0
        lemmatized_all_stop_words = len(lemmatized_phrase_nostops) == 0
        sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': surface_phrase, 'is_lemmatized': False, 'has_function_words': has_stops, 'all_function_words': all_stop_words, 'words': wordlist})
        self.previously_indexed.append(surface_phrase)
        if has_stops and (not all_stop_words) and (wordlist_nostops[0] == wordlist[0]) and (not surface_phrase_nostops in self.previously_indexed):
            sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': surface_phrase_nostops, 'is_lemmatized': False, 'has_function_words': False, 'all_function_words': False, 'words': wordlist_nostops})
            self.previously_indexed.append(surface_phrase_nostops)
        sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': lemmatized_phrase, 'is_lemmatized': True, 'has_function_words': lemmatized_has_stops, 'all_function_words': lemmatized_all_stop_words, 'words': wordlist})
        self.previously_indexed.append(lemmatized_phrase)
        if lemmatized_has_stops and (not lemmatized_all_stop_words) and (wordlist_nostops[0] == wordlist[0]) and (not lemmatized_phrase_nostops in self.previously_indexed):
            sequences.append({'start_position': i, 'sentence_id': sentence.id, 'document_id': sentence.document_id, 'sequence': lemmatized_phrase_nostops, 'is_lemmatized': True, 'has_function_words': False, 'all_function_words': False, 'words': wordlist_nostops})
        return sequences


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

Class description:
This class implements the featurization to implement Weave convolutions. Weave convolutions were introduced in [1]_. Unlike Duvenaud graph convolutions, weave convolutions require a quadratic matrix of interaction descriptors for each pair of atoms. These extra descriptors may provide for additional descriptive power but at the cost of a larger featurized dataset. Examples -------- >>> import deepchem as dc >>> mols = ["CCC"] >>> featurizer = dc.feat.WeaveFeaturizer() >>> features = featurizer.featurize(mols) >>> type(features[0]) <class 'deepchem.feat.mol_graphs.WeaveMol'> >>> features[0].get_num_atoms() # 3 atoms in compound 3 >>> features[0].get_num_features() # feature size 75 >>> type(f

Method signatures and docstrings:
- def __init__(self, graph_distance: bool=True, explicit_H: bool=False, use_chirality: bool=False, max_pair_distance: Optional[int]=None): Initialize this featurizer with set parameters. Parameters ---------- graph_distance: bool, (default True) If True, use graph distance for distance features. Otherwise, use Euclidean distance. Note that this means that molecules that this featurizer is invoked on must have valid conformer information if this option is set. explicit_H: bool, (default False) If true, model hydrogens in the molecule. use_chirality: bool, (default False) If true, use chiral information in the featurization max_pair_distance: Optional[int], (default None) This value can be a positive integer or None. This parameter determines the maximum graph distance at which pair features are computed. For example, if `max_pai
- def _featurize(self, mol): Encodes mol as a WeaveMol object.

Implement the Python class `WeaveFeaturizer` described below.

Class description:
This class implements the featurization to implement Weave convolutions. Weave convolutions were introduced in [1]_. Unlike Duvenaud graph convolutions, weave convolutions require a quadratic matrix of interaction descriptors for each pair of atoms. These extra descriptors may provide for additional descriptive power but at the cost of a larger featurized dataset. Examples -------- >>> import deepchem as dc >>> mols = ["CCC"] >>> featurizer = dc.feat.WeaveFeaturizer() >>> features = featurizer.featurize(mols) >>> type(features[0]) <class 'deepchem.feat.mol_graphs.WeaveMol'> >>> features[0].get_num_atoms() # 3 atoms in compound 3 >>> features[0].get_num_features() # feature size 75 >>> type(f

Method signatures and docstrings:
- def __init__(self, graph_distance: bool=True, explicit_H: bool=False, use_chirality: bool=False, max_pair_distance: Optional[int]=None): Initialize this featurizer with set parameters. Parameters ---------- graph_distance: bool, (default True) If True, use graph distance for distance features. Otherwise, use Euclidean distance. Note that this means that molecules that this featurizer is invoked on must have valid conformer information if this option is set. explicit_H: bool, (default False) If true, model hydrogens in the molecule. use_chirality: bool, (default False) If true, use chiral information in the featurization max_pair_distance: Optional[int], (default None) This value can be a positive integer or None. This parameter determines the maximum graph distance at which pair features are computed. For example, if `max_pai
- def _featurize(self, mol): Encodes mol as a WeaveMol object.

<|skeleton|>
class WeaveFeaturizer:
    """This class implements the featurization to implement Weave convolutions. Weave convolutions were introduced in [1]_. Unlike Duvenaud graph convolutions, weave convolutions require a quadratic matrix of interaction descriptors for each pair of atoms. These extra descriptors may provide for additional descriptive power but at the cost of a larger featurized dataset. Examples -------- >>> import deepchem as dc >>> mols = ["CCC"] >>> featurizer = dc.feat.WeaveFeaturizer() >>> features = featurizer.featurize(mols) >>> type(features[0]) <class 'deepchem.feat.mol_graphs.WeaveMol'> >>> features[0].get_num_atoms() # 3 atoms in compound 3 >>> features[0].get_num_features() # feature size 75 >>> type(f"""

    def __init__(self, graph_distance: bool=True, explicit_H: bool=False, use_chirality: bool=False, max_pair_distance: Optional[int]=None):
        """Initialize this featurizer with set parameters. Parameters ---------- graph_distance: bool, (default True) If True, use graph distance for distance features. Otherwise, use Euclidean distance. Note that this means that molecules that this featurizer is invoked on must have valid conformer information if this option is set. explicit_H: bool, (default False) If true, model hydrogens in the molecule. use_chirality: bool, (default False) If true, use chiral information in the featurization max_pair_distance: Optional[int], (default None) This value can be a positive integer or None. This parameter determines the maximum graph distance at which pair features are computed. For example, if `max_pai"""
        <|body_0|>

    def _featurize(self, mol):
        """Encodes mol as a WeaveMol object."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.graph_distance = graph_distance
        self.dtype = object
        self.explicit_H = explicit_H
        self.use_chirality = use_chirality
        if isinstance(max_pair_distance, int) and max_pair_distance <= 0:
            raise ValueError('max_pair_distance must either be a positive integer or None')
        self.max_pair_distance = max_pair_distance
        if self.use_chirality:
            self.bt_len = int(GraphConvConstants.bond_fdim_base) + len(GraphConvConstants.possible_bond_stereo)
        else:
            self.bt_len = int(GraphConvConstants.bond_fdim_base)
<|end_body_0|>

<|body_start_1|>
        idx_nodes = [(a.GetIdx(), atom_features(a, explicit_H=self.explicit_H, use_chirality=self.use_chirality)) for a in mol.GetAtoms()]
        idx_nodes.sort()
        idx, nodes = list(zip(*idx_nodes))
        nodes = np.vstack(nodes)
        bond_features_map = {}
        for b in mol.GetBonds():
            bond_features_map[tuple(sorted([b.GetBeginAtomIdx(), b.GetEndAtomIdx()]))] = bond_features(b, use_chirality=self.use_chirality)
        bond_adj_list = [[] for mol_id in range(len(nodes))]
        for bond in bond_features_map.keys():
            bond_adj_list[bond[0]].append(bond[1])
            bond_adj_list[bond[1]].append(bond[0])
        pairs, pair_edges = pair_features(mol, bond_features_map, bond_adj_list, bt_len=self.bt_len, graph_distance=self.graph_distance, max_pair_distance=self.max_pair_distance)
        return WeaveMol(nodes, pairs, pair_edges)
<|end_body_1|>

Solution:
class WeaveFeaturizer:
    """This class implements the featurization to implement Weave convolutions. Weave convolutions were introduced in [1]_. Unlike Duvenaud graph convolutions, weave convolutions require a quadratic matrix of interaction descriptors for each pair of atoms. These extra descriptors may provide for additional descriptive power but at the cost of a larger featurized dataset. Examples -------- >>> import deepchem as dc >>> mols = ["CCC"] >>> featurizer = dc.feat.WeaveFeaturizer() >>> features = featurizer.featurize(mols) >>> type(features[0]) <class 'deepchem.feat.mol_graphs.WeaveMol'> >>> features[0].get_num_atoms() # 3 atoms in compound 3 >>> features[0].get_num_features() # feature size 75 >>> type(f"""

    def __init__(self, graph_distance: bool=True, explicit_H: bool=False, use_chirality: bool=False, max_pair_distance: Optional[int]=None):
        """Initialize this featurizer with set parameters. Parameters ---------- graph_distance: bool, (default True) If True, use graph distance for distance features. Otherwise, use Euclidean distance. Note that this means that molecules that this featurizer is invoked on must have valid conformer information if this option is set. explicit_H: bool, (default False) If true, model hydrogens in the molecule. use_chirality: bool, (default False) If true, use chiral information in the featurization max_pair_distance: Optional[int], (default None) This value can be a positive integer or None. This parameter determines the maximum graph distance at which pair features are computed. For example, if `max_pai"""
        self.graph_distance = graph_distance
        self.dtype = object
        self.explicit_H = explicit_H
        self.use_chirality = use_chirality
        if isinstance(max_pair_distance, int) and max_pair_distance <= 0:
            raise ValueError('max_pair_distance must either be a positive integer or None')
        self.max_pair_distance = max_pair_distance
        if self.use_chirality:
            self.bt_len = int(GraphConvConstants.bond_fdim_base) + len(GraphConvConstants.possible_bond_stereo)
        else:
            self.bt_len = int(GraphConvConstants.bond_fdim_base)

    def _featurize(self, mol):
        """Encodes mol as a WeaveMol object."""
        idx_nodes = [(a.GetIdx(), atom_features(a, explicit_H=self.explicit_H, use_chirality=self.use_chirality)) for a in mol.GetAtoms()]
        idx_nodes.sort()
        idx, nodes = list(zip(*idx_nodes))
        nodes = np.vstack(nodes)
        bond_features_map = {}
        for b in mol.GetBonds():
            bond_features_map[tuple(sorted([b.GetBeginAtomIdx(), b.GetEndAtomIdx()]))] = bond_features(b, use_chirality=self.use_chirality)
        bond_adj_list = [[] for mol_id in range(len(nodes))]
        for bond in bond_features_map.keys():
            bond_adj_list[bond[0]].append(bond[1])
            bond_adj_list[bond[1]].append(bond[0])
        pairs, pair_edges = pair_features(mol, bond_features_map, bond_adj_list, bt_len=self.bt_len, graph_distance=self.graph_distance, max_pair_distance=self.max_pair_distance)
        return WeaveMol(nodes, pairs, pair_edges)


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

Class description:
Implement the RecordMissedCallsTest class.

Method signatures and docstrings:
- def test_record_a_new_missed_call(self): Record a new missed call. One new record should be inserted into DB.
- def test_record_multiple_new_missed_call(self): Record multiple new missed calls. Multiple new record should be inserted into DB.
- def test_delete_records(self): When user has fetched missed calls, they should delete them in DB. Test whether fetched records has been deleted.

Implement the Python class `RecordMissedCallsTest` described below.

Class description:
Implement the RecordMissedCallsTest class.

Method signatures and docstrings:
- def test_record_a_new_missed_call(self): Record a new missed call. One new record should be inserted into DB.
- def test_record_multiple_new_missed_call(self): Record multiple new missed calls. Multiple new record should be inserted into DB.
- def test_delete_records(self): When user has fetched missed calls, they should delete them in DB. Test whether fetched records has been deleted.

<|skeleton|>
class RecordMissedCallsTest:

    def test_record_a_new_missed_call(self):
        """Record a new missed call. One new record should be inserted into DB."""
        <|body_0|>

    def test_record_multiple_new_missed_call(self):
        """Record multiple new missed calls. Multiple new record should be inserted into DB."""
        <|body_1|>

    def test_delete_records(self):
        """When user has fetched missed calls, they should delete them in DB. Test whether fetched records has been deleted."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        response = self.client.post(url, data)
        self.assertEqual(response.status_code, 201)
        url = '/dialrecords/missedcalls/' + called_number + '/'
        response = self.client.get(url)
        record_list = json.loads(response.content)
        self.assertEqual(called_number, record_list[-1]['called_number'])
        self.assertEqual(calling_number, record_list[-1]['calling_number'])
        self.assertEqual(calling_date, record_list[-1]['calling_date'])
<|end_body_0|>

<|body_start_1|>
        times = 9
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        for i in range(0, times):
            self.client.post(url, data)
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, str(times))
        url = '/dialrecords/missedcalls/' + called_number + '/'
        response = self.client.get(url)
        record_list = json.loads(response.content)
        self.assertEqual(len(record_list), times)
<|end_body_1|>

<|body_start_2|>
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        response = self.client.post(url, data)
        self.assertEqual(response.status_code, 201)
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, '1')
        url = '/dialrecords/missedcalls/' + called_number + '/'
        self.client.delete(url)
        response = self.client.get(url)
        self.assertEqual(response.content, '[]')
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, '0')
<|end_body_2|>

Solution:
class RecordMissedCallsTest:
    def test_record_a_new_missed_call(self):
        """Record a new missed call. One new record should be inserted into DB."""
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        response = self.client.post(url, data)
        self.assertEqual(response.status_code, 201)
        url = '/dialrecords/missedcalls/' + called_number + '/'
        response = self.client.get(url)
        record_list = json.loads(response.content)
        self.assertEqual(called_number, record_list[-1]['called_number'])
        self.assertEqual(calling_number, record_list[-1]['calling_number'])
        self.assertEqual(calling_date, record_list[-1]['calling_date'])

    def test_record_multiple_new_missed_call(self):
        """Record multiple new missed calls. Multiple new record should be inserted into DB."""
        times = 9
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        for i in range(0, times):
            self.client.post(url, data)
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, str(times))
        url = '/dialrecords/missedcalls/' + called_number + '/'
        response = self.client.get(url)
        record_list = json.loads(response.content)
        self.assertEqual(len(record_list), times)

    def test_delete_records(self):
        """When user has fetched missed calls, they should delete them in DB. Test whether fetched records has been deleted."""
        url = '/dialrecords/missedcalls/'
        called_number = '008618664565400'
        calling_number = '008618664565402'
        calling_date = '2013-10-25T06:30:59Z'
        data = {'called_number': called_number, 'calling_number': calling_number, 'calling_date': calling_date}
        response = self.client.post(url, data)
        self.assertEqual(response.status_code, 201)
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, '1')
        url = '/dialrecords/missedcalls/' + called_number + '/'
        self.client.delete(url)
        response = self.client.get(url)
        self.assertEqual(response.content, '[]')
        url = '/dialrecords/missedcalls/' + called_number + '/badge/'
        response = self.client.get(url)
        self.assertEqual(response.content, '0')


load

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

Class description:
Implement the Jaspar class.

Method signatures and docstrings:
- def load(self, list_file, matrix_dir): Load all of the matrices in list_file from matrix_dir. Return a dictionary of PWM objects.
- def load_matrix(self, matrix_file): Load an individual matrix into a motility-compatible format.

Implement the Python class `Jaspar` described below.

Class description:
Implement the Jaspar class.

Method signatures and docstrings:
- def load(self, list_file, matrix_dir): Load all of the matrices in list_file from matrix_dir. Return a dictionary of PWM objects.
- def load_matrix(self, matrix_file): Load an individual matrix into a motility-compatible format.

<|skeleton|>
class Jaspar:

    def load(self, list_file, matrix_dir):
        """Load all of the matrices in list_file from matrix_dir. Return a dictionary of PWM objects."""
        <|body_0|>

    def load_matrix(self, matrix_file):
        """Load an individual matrix into a motility-compatible format."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        l = open(list_file).readlines()
        l = [i.split() for i in l]
        l = [(i[0], i[2]) for i in l]
        d = {}
        for matrix_file, name in l:
            filename = '%s.pfm' % (matrix_file,)
            matrix_file = os.path.join(matrix_dir, filename)
            d[name] = PWM(self.load_matrix(matrix_file))
        return d
<|end_body_0|>

<|body_start_1|>
        f = open(matrix_file)
        l = f.readlines()
        l = [i.split() for i in l if len(i.strip())]
        l = [map(float, i) for i in l]
        assert len(l) == 4
        lens = map(len, l)
        assert min(lens) == max(lens)
        site_len = min(lens)
        mat = []
        for i in range(0, site_len):
            A = l[0][i]
            C = l[1][i]
            G = l[2][i]
            T = l[3][i]
            N = 0
            mat.append((A, C, G, T, N))
        return mat
<|end_body_1|>

Solution:
class Jaspar:
    def load(self, list_file, matrix_dir):
        """Load all of the matrices in list_file from matrix_dir. Return a dictionary of PWM objects."""
        l = open(list_file).readlines()
        l = [i.split() for i in l]
        l = [(i[0], i[2]) for i in l]
        d = {}
        for matrix_file, name in l:
            filename = '%s.pfm' % (matrix_file,)
            matrix_file = os.path.join(matrix_dir, filename)
            d[name] = PWM(self.load_matrix(matrix_file))
        return d

    def load_matrix(self, matrix_file):
        """Load an individual matrix into a motility-compatible format."""
        f = open(matrix_file)
        l = f.readlines()
        l = [i.split() for i in l if len(i.strip())]
        l = [map(float, i) for i in l]
        assert len(l) == 4
        lens = map(len, l)
        assert min(lens) == max(lens)
        site_len = min(lens)
        mat = []
        for i in range(0, site_len):
            A = l[0][i]
            C = l[1][i]
            G = l[2][i]
            T = l[3][i]
            N = 0
            mat.append((A, C, G, T, N))
        return mat


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

Class description:
Depot Admin Site Class.

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize admin site.
- def get_urls(self): Return admin urls with extra urls.

Implement the Python class `DepotAdminSite` described below.

Class description:
Depot Admin Site Class.

Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize admin site.
- def get_urls(self): Return admin urls with extra urls.

<|skeleton|>
class DepotAdminSite:
    """Depot Admin Site Class."""

    def __init__(self, *args, **kwargs):
        """Initialize admin site."""
        <|body_0|>

    def get_urls(self):
        """Return admin urls with extra urls."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(*args, **kwargs)
        self._registry.update(admin.site._registry)
        self._actions = {}
        self._global_actions = {}
<|end_body_0|>

<|body_start_1|>
        urls = admin.site.get_urls()
        extra_urls = [path('create-user/', depot_views.create_user, name='admin-user-creation'), path('create-permissions-contenttypes/', depot_views.create_permissions_content_types, name='temp-permissions-contenttypes'), path('favicon.ico', RedirectView.as_view(url='/static/images/favicon.ico'))]
        return urls + extra_urls
<|end_body_1|>

Solution:
class DepotAdminSite:
    """Depot Admin Site Class."""

    def __init__(self, *args, **kwargs):
        """Initialize admin site."""
        super().__init__(*args, **kwargs)
        self._registry.update(admin.site._registry)
        self._actions = {}
        self._global_actions = {}

    def get_urls(self):
        """Return admin urls with extra urls."""
        urls = admin.site.get_urls()
        extra_urls = [path('create-user/', depot_views.create_user, name='admin-user-creation'), path('create-permissions-contenttypes/', depot_views.create_permissions_content_types, name='temp-permissions-contenttypes'), path('favicon.ico', RedirectView.as_view(url='/static/images/favicon.ico'))]
        return urls + extra_urls


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

Class description:
Implement the Output class.

Method signatures and docstrings:
- def enter_output_list(self): 进入出库单列表
- def filter_order(self): 筛选待出库的订单
- def single_output(self): APP单个出库
- def batch_output(self): APP批量出库

Implement the Python class `Output` described below.

Class description:
Implement the Output class.

Method signatures and docstrings:
- def enter_output_list(self): 进入出库单列表
- def filter_order(self): 筛选待出库的订单
- def single_output(self): APP单个出库
- def batch_output(self): APP批量出库

<|skeleton|>
class Output:

    def enter_output_list(self):
        """进入出库单列表"""
        <|body_0|>

    def filter_order(self):
        """筛选待出库的订单"""
        <|body_1|>

    def single_output(self):
        """APP单个出库"""
        <|body_2|>

    def batch_output(self):
        """APP批量出库"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        logging.info('========== enter_output_list ==========')
        self.driver.find_element_by_xpath(self.delivery_list).click()
        sleep(1)
<|end_body_0|>

<|body_start_1|>
        logging.info('========== filter_order ==========')
        self.driver.find_element_by_id(self.status_filter).click()
        sleep(0.5)
        self.driver.find_elements_by_class_name(self.check_label)[1].click()
        sleep(0.5)
        if not self.driver.find_elements_by_class_name(self.order_list):
            i = 1
            while True:
                self.driver.find_element_by_id(self.date_filter).click()
                sleep(0.5)
                conditions = self.driver.find_elements_by_class_name(self.check_label)
                conditions[i].click()
                sleep(0.5)
                if self.driver.find_elements_by_class_name(self.order_list):
                    return self.driver.find_elements_by_class_name(self.order_list)
                i += 1
                if i == len(conditions):
                    break
        return self.driver.find_elements_by_class_name(self.order_list)
<|end_body_1|>

<|body_start_2|>
        logging.info('========== single_output ==========')
        order_list = self.filter_order()
        if order_list:
            order_list[0].click()
            sleep(0.5)
            self.driver.find_element_by_id(self.stock_out).click()
            sleep(0.5)
            self.driver.find_element_by_xpath(self.confirm).click()
        else:
            logging.info('没有待出库的订单')
<|end_body_2|>

<|body_start_3|>
        logging.info('========== batch_outbound ==========')
        order_list = self.filter_order()
        if order_list:
            self.driver.find_element_by_id(self.batch).click()
            sleep(1)
            self.driver.find_element_by_xpath(self.batch_confirm).click()
            sleep(5)
        else:
            logging.info('没有待出库的订单')
<|end_body_3|>

Solution:
class Output:
    def enter_output_list(self):
        """进入出库单列表"""
        logging.info('========== enter_output_list ==========')
        self.driver.find_element_by_xpath(self.delivery_list).click()
        sleep(1)

    def filter_order(self):
        """筛选待出库的订单"""
        logging.info('========== filter_order ==========')
        self.driver.find_element_by_id(self.status_filter).click()
        sleep(0.5)
        self.driver.find_elements_by_class_name(self.check_label)[1].click()
        sleep(0.5)
        if not self.driver.find_elements_by_class_name(self.order_list):
            i = 1
            while True:
                self.driver.find_element_by_id(self.date_filter).click()
                sleep(0.5)
                conditions = self.driver.find_elements_by_class_name(self.check_label)
                conditions[i].click()
                sleep(0.5)
                if self.driver.find_elements_by_class_name(self.order_list):
                    return self.driver.find_elements_by_class_name(self.order_list)
                i += 1
                if i == len(conditions):
                    break
        return self.driver.find_elements_by_class_name(self.order_list)

    def single_output(self):
        """APP单个出库"""
        logging.info('========== single_output ==========')
        order_list = self.filter_order()
        if order_list:
            order_list[0].click()
            sleep(0.5)
            self.driver.find_element_by_id(self.stock_out).click()
            sleep(0.5)
            self.driver.find_element_by_xpath(self.confirm).click()
        else:
            logging.info('没有待出库的订单')

    def batch_output(self):
        """APP批量出库"""
        logging.info('========== batch_outbound ==========')
        order_list = self.filter_order()
        if order_list:
            self.driver.find_element_by_id(self.batch).click()
            sleep(1)
            self.driver.find_element_by_xpath(self.batch_confirm).click()
            sleep(5)
        else:
            logging.info('没有待出库的订单')


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

Class description:
A mixin with the stuff shared between EventSession{Create|Update}View

Method signatures and docstrings:
- def get_form(self, *args, **kwargs): The default range widgets are a bit shit because they eat the help_text and have no indication of which field is for what. So we add a nice placeholder. We also limit the event_location dropdown to only the current camps locations.
- def get_context_data(self, *args, **kwargs): Add event_type and location and existing sessions to context

Implement the Python class `EventSessionFormViewMixin` described below.

Class description:
A mixin with the stuff shared between EventSession{Create|Update}View

Method signatures and docstrings:
- def get_form(self, *args, **kwargs): The default range widgets are a bit shit because they eat the help_text and have no indication of which field is for what. So we add a nice placeholder. We also limit the event_location dropdown to only the current camps locations.
- def get_context_data(self, *args, **kwargs): Add event_type and location and existing sessions to context

<|skeleton|>
class EventSessionFormViewMixin:
    """A mixin with the stuff shared between EventSession{Create|Update}View"""

    def get_form(self, *args, **kwargs):
        """The default range widgets are a bit shit because they eat the help_text and have no indication of which field is for what. So we add a nice placeholder. We also limit the event_location dropdown to only the current camps locations."""
        <|body_0|>

    def get_context_data(self, *args, **kwargs):
        """Add event_type and location and existing sessions to context"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        form = super().get_form(*args, **kwargs)
        form.fields['when'].widget.widgets[0].attrs = {'placeholder': f'Start Date and Time (YYYY-MM-DD HH:MM). Time zone is {settings.TIME_ZONE}.'}
        form.fields['when'].widget.widgets[1].attrs = {'placeholder': f'End Date and Time (YYYY-MM-DD HH:MM). Time zone is {settings.TIME_ZONE}.'}
        if hasattr(form.fields, 'event_location'):
            form.fields['event_location'].queryset = EventLocation.objects.filter(camp=self.camp)
        return form
<|end_body_0|>

<|body_start_1|>
        context = super().get_context_data(*args, **kwargs)
        if not hasattr(self, 'event_type'):
            self.event_type = self.get_object().event_type
        context['event_type'] = self.event_type
        if not hasattr(self, 'event_location'):
            self.event_location = self.get_object().event_location
        context['event_location'] = self.event_location
        context['sessions'] = self.event_type.event_sessions.filter(camp=self.camp)
        return context
<|end_body_1|>

Solution:
class EventSessionFormViewMixin:
    """A mixin with the stuff shared between EventSession{Create|Update}View"""

    def get_form(self, *args, **kwargs):
        """The default range widgets are a bit shit because they eat the help_text and have no indication of which field is for what. So we add a nice placeholder. We also limit the event_location dropdown to only the current camps locations."""
        form = super().get_form(*args, **kwargs)
        form.fields['when'].widget.widgets[0].attrs = {'placeholder': f'Start Date and Time (YYYY-MM-DD HH:MM). Time zone is {settings.TIME_ZONE}.'}
        form.fields['when'].widget.widgets[1].attrs = {'placeholder': f'End Date and Time (YYYY-MM-DD HH:MM). Time zone is {settings.TIME_ZONE}.'}
        if hasattr(form.fields, 'event_location'):
            form.fields['event_location'].queryset = EventLocation.objects.filter(camp=self.camp)
        return form

    def get_context_data(self, *args, **kwargs):
        """Add event_type and location and existing sessions to context"""
        context = super().get_context_data(*args, **kwargs)
        if not hasattr(self, 'event_type'):
            self.event_type = self.get_object().event_type
        context['event_type'] = self.event_type
        if not hasattr(self, 'event_location'):
            self.event_location = self.get_object().event_location
        context['event_location'] = self.event_location
        context['sessions'] = self.event_type.event_sessions.filter(camp=self.camp)
        return context


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

Class description:
Implement the DetectionHeadHNM class.

Method signatures and docstrings:
- def __init__(self, classifier: Classifier, regressor: Regressor, coder: BoxCoderND, sampler: AbstractSampler, log_num_anchors: Optional[str]='mllogger'): Detection head with classifier and regression module. Uses hard negative example mining to compute loss Args: classifier: classifier module regressor: regression module sampler (AbstractSampler): sampler for select positive and negative examples log_num_anchors (str): name of logger to use; if None, no logging will be performed
- def compute_loss(self, prediction: Dict[str, Tensor], target_labels: List[Tensor], matched_gt_boxes: List[Tensor], anchors: List[Tensor]) -> Tuple[Dict[str, Tensor], torch.Tensor, torch.Tensor]: Compute regression and classification loss N anchors over all images; M anchors per image => sum(M) = N Args: prediction: detection predictions for loss computation box_logits (Tensor): classification logits for each anchor [N, num_classes] box_deltas (Tensor): offsets for each anchor (x1, y1, x2, y2, (z1, z2))[N, dim * 2] target_labels (List[Tensor]): target labels for each anchor (per image) [M] matched_gt_boxes: matched gt box for each anchor List[[N, dim * 2]], N=number of anchors per image anchors: anchors per image List[[N, dim * 2]] Returns: Tensor: dict with losses (reg for regression loss, cls for classification loss) Tensor: sampled positive indices of anchors (after concatenation)
- def select_indices(self, target_labels: List[Tensor], boxes_scores: Tensor) -> Tuple[Tensor, Tensor]: Sample positive and negative anchors from target labels Args: target_labels (List[Tensor]): target labels for each anchor (per image) [M] boxes_scores (Tensor): classification logits for each anchor [N, num_classes] Returns: Tensor: sampled positive indices [R] Tensor: sampled negative indices [R]

Implement the Python class `DetectionHeadHNM` described below.

Class description:
Implement the DetectionHeadHNM class.

Method signatures and docstrings:
- def __init__(self, classifier: Classifier, regressor: Regressor, coder: BoxCoderND, sampler: AbstractSampler, log_num_anchors: Optional[str]='mllogger'): Detection head with classifier and regression module. Uses hard negative example mining to compute loss Args: classifier: classifier module regressor: regression module sampler (AbstractSampler): sampler for select positive and negative examples log_num_anchors (str): name of logger to use; if None, no logging will be performed
- def compute_loss(self, prediction: Dict[str, Tensor], target_labels: List[Tensor], matched_gt_boxes: List[Tensor], anchors: List[Tensor]) -> Tuple[Dict[str, Tensor], torch.Tensor, torch.Tensor]: Compute regression and classification loss N anchors over all images; M anchors per image => sum(M) = N Args: prediction: detection predictions for loss computation box_logits (Tensor): classification logits for each anchor [N, num_classes] box_deltas (Tensor): offsets for each anchor (x1, y1, x2, y2, (z1, z2))[N, dim * 2] target_labels (List[Tensor]): target labels for each anchor (per image) [M] matched_gt_boxes: matched gt box for each anchor List[[N, dim * 2]], N=number of anchors per image anchors: anchors per image List[[N, dim * 2]] Returns: Tensor: dict with losses (reg for regression loss, cls for classification loss) Tensor: sampled positive indices of anchors (after concatenation)
- def select_indices(self, target_labels: List[Tensor], boxes_scores: Tensor) -> Tuple[Tensor, Tensor]: Sample positive and negative anchors from target labels Args: target_labels (List[Tensor]): target labels for each anchor (per image) [M] boxes_scores (Tensor): classification logits for each anchor [N, num_classes] Returns: Tensor: sampled positive indices [R] Tensor: sampled negative indices [R]

<|skeleton|>
class DetectionHeadHNM:

    def __init__(self, classifier: Classifier, regressor: Regressor, coder: BoxCoderND, sampler: AbstractSampler, log_num_anchors: Optional[str]='mllogger'):
        """Detection head with classifier and regression module. Uses hard negative example mining to compute loss Args: classifier: classifier module regressor: regression module sampler (AbstractSampler): sampler for select positive and negative examples log_num_anchors (str): name of logger to use; if None, no logging will be performed"""
        <|body_0|>

    def compute_loss(self, prediction: Dict[str, Tensor], target_labels: List[Tensor], matched_gt_boxes: List[Tensor], anchors: List[Tensor]) -> Tuple[Dict[str, Tensor], torch.Tensor, torch.Tensor]:
        """Compute regression and classification loss N anchors over all images; M anchors per image => sum(M) = N Args: prediction: detection predictions for loss computation box_logits (Tensor): classification logits for each anchor [N, num_classes] box_deltas (Tensor): offsets for each anchor (x1, y1, x2, y2, (z1, z2))[N, dim * 2] target_labels (List[Tensor]): target labels for each anchor (per image) [M] matched_gt_boxes: matched gt box for each anchor List[[N, dim * 2]], N=number of anchors per image anchors: anchors per image List[[N, dim * 2]] Returns: Tensor: dict with losses (reg for regression loss, cls for classification loss) Tensor: sampled positive indices of anchors (after concatenation)"""
        <|body_1|>

    def select_indices(self, target_labels: List[Tensor], boxes_scores: Tensor) -> Tuple[Tensor, Tensor]:
        """Sample positive and negative anchors from target labels Args: target_labels (List[Tensor]): target labels for each anchor (per image) [M] boxes_scores (Tensor): classification logits for each anchor [N, num_classes] Returns: Tensor: sampled positive indices [R] Tensor: sampled negative indices [R]"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(classifier=classifier, regressor=regressor, coder=coder)
        self.logger = None
        self.fg_bg_sampler = sampler
<|end_body_0|>

<|body_start_1|>
        box_logits, box_deltas = (prediction['box_logits'], prediction['box_deltas'])
        losses = {}
        sampled_pos_inds, sampled_neg_inds = self.select_indices(target_labels, box_logits)
        sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
        target_labels = torch.cat(target_labels, dim=0)
        with torch.no_grad():
            batch_matched_gt_boxes = torch.cat(matched_gt_boxes, dim=0)
            batch_anchors = torch.cat(anchors, dim=0)
            target_deltas_sampled = self.coder.encode_single(batch_matched_gt_boxes[sampled_pos_inds], batch_anchors[sampled_pos_inds])
        if sampled_pos_inds.numel() > 0:
            losses['reg'] = self.regressor.compute_loss(box_deltas[sampled_pos_inds], target_deltas_sampled) / max(1, sampled_pos_inds.numel())
        losses['cls'] = self.classifier.compute_loss(box_logits[sampled_inds], target_labels[sampled_inds])
        return (losses, sampled_pos_inds, sampled_neg_inds)
<|end_body_1|>

<|body_start_2|>
        boxes_max_fg_probs = self.classifier.box_logits_to_probs(boxes_scores)
        boxes_max_fg_probs = boxes_max_fg_probs.max(dim=1)[0]
        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(target_labels, boxes_max_fg_probs)
        sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
        sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
        return (sampled_pos_inds, sampled_neg_inds)
<|end_body_2|>

Solution:
class DetectionHeadHNM:
    def __init__(self, classifier: Classifier, regressor: Regressor, coder: BoxCoderND, sampler: AbstractSampler, log_num_anchors: Optional[str]='mllogger'):
        """Detection head with classifier and regression module. Uses hard negative example mining to compute loss Args: classifier: classifier module regressor: regression module sampler (AbstractSampler): sampler for select positive and negative examples log_num_anchors (str): name of logger to use; if None, no logging will be performed"""
        super().__init__(classifier=classifier, regressor=regressor, coder=coder)
        self.logger = None
        self.fg_bg_sampler = sampler

    def compute_loss(self, prediction: Dict[str, Tensor], target_labels: List[Tensor], matched_gt_boxes: List[Tensor], anchors: List[Tensor]) -> Tuple[Dict[str, Tensor], torch.Tensor, torch.Tensor]:
        """Compute regression and classification loss N anchors over all images; M anchors per image => sum(M) = N Args: prediction: detection predictions for loss computation box_logits (Tensor): classification logits for each anchor [N, num_classes] box_deltas (Tensor): offsets for each anchor (x1, y1, x2, y2, (z1, z2))[N, dim * 2] target_labels (List[Tensor]): target labels for each anchor (per image) [M] matched_gt_boxes: matched gt box for each anchor List[[N, dim * 2]], N=number of anchors per image anchors: anchors per image List[[N, dim * 2]] Returns: Tensor: dict with losses (reg for regression loss, cls for classification loss) Tensor: sampled positive indices of anchors (after concatenation)"""
        box_logits, box_deltas = (prediction['box_logits'], prediction['box_deltas'])
        losses = {}
        sampled_pos_inds, sampled_neg_inds = self.select_indices(target_labels, box_logits)
        sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
        target_labels = torch.cat(target_labels, dim=0)
        with torch.no_grad():
            batch_matched_gt_boxes = torch.cat(matched_gt_boxes, dim=0)
            batch_anchors = torch.cat(anchors, dim=0)
            target_deltas_sampled = self.coder.encode_single(batch_matched_gt_boxes[sampled_pos_inds], batch_anchors[sampled_pos_inds])
        if sampled_pos_inds.numel() > 0:
            losses['reg'] = self.regressor.compute_loss(box_deltas[sampled_pos_inds], target_deltas_sampled) / max(1, sampled_pos_inds.numel())
        losses['cls'] = self.classifier.compute_loss(box_logits[sampled_inds], target_labels[sampled_inds])
        return (losses, sampled_pos_inds, sampled_neg_inds)

    def select_indices(self, target_labels: List[Tensor], boxes_scores: Tensor) -> Tuple[Tensor, Tensor]:
        """Sample positive and negative anchors from target labels Args: target_labels (List[Tensor]): target labels for each anchor (per image) [M] boxes_scores (Tensor): classification logits for each anchor [N, num_classes] Returns: Tensor: sampled positive indices [R] Tensor: sampled negative indices [R]"""
        boxes_max_fg_probs = self.classifier.box_logits_to_probs(boxes_scores)
        boxes_max_fg_probs = boxes_max_fg_probs.max(dim=1)[0]
        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(target_labels, boxes_max_fg_probs)
        sampled_pos_inds = torch.where(torch.cat(sampled_pos_inds, dim=0))[0]
        sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
        return (sampled_pos_inds, sampled_neg_inds)


run

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

Class description:
Threaded code to push blocks using a given provider

Method signatures and docstrings:
- def __init__(self, provider, blocks, queue): Initialize the BlockPusher by providing blocks to store in a provider the queue to push the metablocks produced by the insertion Args: provider(Provider) -- The provider that will store the data blocks(dict(MetaBlock, bytes) -- The list of blocks that constitutes the file queue(dict) -- Queue where the metablocks should be stored once the block are stored
- def run(self): Loop through self.blocks, selecting those stored at indices listed in self.indices and feeding them to self.provider for storage

Implement the Python class `BlockPusher` described below.

Class description:
Threaded code to push blocks using a given provider

Method signatures and docstrings:
- def __init__(self, provider, blocks, queue): Initialize the BlockPusher by providing blocks to store in a provider the queue to push the metablocks produced by the insertion Args: provider(Provider) -- The provider that will store the data blocks(dict(MetaBlock, bytes) -- The list of blocks that constitutes the file queue(dict) -- Queue where the metablocks should be stored once the block are stored
- def run(self): Loop through self.blocks, selecting those stored at indices listed in self.indices and feeding them to self.provider for storage

<|skeleton|>
class BlockPusher:
    """Threaded code to push blocks using a given provider"""

    def __init__(self, provider, blocks, queue):
        """Initialize the BlockPusher by providing blocks to store in a provider the queue to push the metablocks produced by the insertion Args: provider(Provider) -- The provider that will store the data blocks(dict(MetaBlock, bytes) -- The list of blocks that constitutes the file queue(dict) -- Queue where the metablocks should be stored once the block are stored"""
        <|body_0|>

    def run(self):
        """Loop through self.blocks, selecting those stored at indices listed in self.indices and feeding them to self.provider for storage"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(BlockPusher, self).__init__()
        self.provider = provider
        self.blocks = blocks
        self.queue = queue
<|end_body_0|>

<|body_start_1|>
        loop_temp = 'Going to put block with key {:s} in provider {:s}'
        for metablock, data in self.blocks.iteritems():
            logger.debug(loop_temp.format(metablock.key, str(type(self.provider))))
            try:
                self.provider.put(data, metablock.key)
                index = extract_index_from_key(metablock.key)
                self.queue[index] = metablock
            except redis.ConnectionError:
                index = extract_index_from_key(metablock.key)
                self.queue[index] = CouldNotPushException(self.provider, metablock.key)
<|end_body_1|>

Solution:
class BlockPusher:
    """Threaded code to push blocks using a given provider"""

    def __init__(self, provider, blocks, queue):
        """Initialize the BlockPusher by providing blocks to store in a provider the queue to push the metablocks produced by the insertion Args: provider(Provider) -- The provider that will store the data blocks(dict(MetaBlock, bytes) -- The list of blocks that constitutes the file queue(dict) -- Queue where the metablocks should be stored once the block are stored"""
        super(BlockPusher, self).__init__()
        self.provider = provider
        self.blocks = blocks
        self.queue = queue

    def run(self):
        """Loop through self.blocks, selecting those stored at indices listed in self.indices and feeding them to self.provider for storage"""
        loop_temp = 'Going to put block with key {:s} in provider {:s}'
        for metablock, data in self.blocks.iteritems():
            logger.debug(loop_temp.format(metablock.key, str(type(self.provider))))
            try:
                self.provider.put(data, metablock.key)
                index = extract_index_from_key(metablock.key)
                self.queue[index] = metablock
            except redis.ConnectionError:
                index = extract_index_from_key(metablock.key)
                self.queue[index] = CouldNotPushException(self.provider, metablock.key)


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

Class description:
Implementation of topological sort relations between vertices can be presented as matrix N x N Since an adjacency matrix has n2 entries, it must be true that m <= n2 (from DAG definition) where n count of vertexes; m count of edges

Method signatures and docstrings:
- def get_in_degree(self): Create list of graph's nodes in-degree Count of vertices which depends on current one. Complexity: O(n+m) :Returns: *list* - list with in-degree of each node
- def sort(self): Make topological sort
- def main(self): main entry point

Implement the Python class `TopologicalSort` described below.

Class description:
Implementation of topological sort relations between vertices can be presented as matrix N x N Since an adjacency matrix has n2 entries, it must be true that m <= n2 (from DAG definition) where n count of vertexes; m count of edges

Method signatures and docstrings:
- def get_in_degree(self): Create list of graph's nodes in-degree Count of vertices which depends on current one. Complexity: O(n+m) :Returns: *list* - list with in-degree of each node
- def sort(self): Make topological sort
- def main(self): main entry point

<|skeleton|>
class TopologicalSort:
    """Implementation of topological sort relations between vertices can be presented as matrix N x N Since an adjacency matrix has n2 entries, it must be true that m <= n2 (from DAG definition) where n count of vertexes; m count of edges"""

    def get_in_degree(self):
        """Create list of graph's nodes in-degree Count of vertices which depends on current one. Complexity: O(n+m) :Returns: *list* - list with in-degree of each node"""
        <|body_0|>

    def sort(self):
        """Make topological sort"""
        <|body_1|>

    def main(self):
        """main entry point"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        edges = self.get_edges(True)
        _in_degree = [0] * len(edges)
        for edge_list in edges:
            for edge in edge_list:
                _in_degree[edge[1]] += 1
        return _in_degree
<|end_body_0|>

<|body_start_1|>
        _in_degree = self.get_in_degree()
        edges = self.get_edges(True)
        sequence = []
        _next = [inx for inx, item in enumerate(_in_degree) if item == 0]
        while _next:
            vertex = _next.pop()
            sequence.append(vertex)
            for edge in edges[vertex]:
                _in_degree[edge[1]] -= 1
                if _in_degree[edge[1]] <= 0:
                    _next.append(edge[1])
        return sequence
<|end_body_1|>

<|body_start_2|>
        indexes = self.sort()
        sequence = self.get_nodes_sequence(indexes)
        print(sequence)
<|end_body_2|>

Solution:
class TopologicalSort:
    """Implementation of topological sort relations between vertices can be presented as matrix N x N Since an adjacency matrix has n2 entries, it must be true that m <= n2 (from DAG definition) where n count of vertexes; m count of edges"""

    def get_in_degree(self):
        """Create list of graph's nodes in-degree Count of vertices which depends on current one. Complexity: O(n+m) :Returns: *list* - list with in-degree of each node"""
        edges = self.get_edges(True)
        _in_degree = [0] * len(edges)
        for edge_list in edges:
            for edge in edge_list:
                _in_degree[edge[1]] += 1
        return _in_degree

    def sort(self):
        """Make topological sort"""
        _in_degree = self.get_in_degree()
        edges = self.get_edges(True)
        sequence = []
        _next = [inx for inx, item in enumerate(_in_degree) if item == 0]
        while _next:
            vertex = _next.pop()
            sequence.append(vertex)
            for edge in edges[vertex]:
                _in_degree[edge[1]] -= 1
                if _in_degree[edge[1]] <= 0:
                    _next.append(edge[1])
        return sequence

    def main(self):
        """main entry point"""
        indexes = self.sort()
        sequence = self.get_nodes_sequence(indexes)
        print(sequence)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def sumNumbers_dfs(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_recuisive(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_bfs(self, root): :type root: TreeNode :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def sumNumbers_dfs(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_recuisive(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_bfs(self, root): :type root: TreeNode :rtype: int

<|skeleton|>
class Solution:

    def sumNumbers_dfs(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_0|>

    def sumNumbers_recuisive(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_1|>

    def sumNumbers_bfs(self, root):
        """:type root: TreeNode :rtype: int"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        def dfsSum(root, base):
            if not root:
                return 0
            rt = base * 10 + root.val
            if not root.left and (not root.right):
                return rt
            else:
                return dfsSum(root.left, rt) + dfsSum(root.right, rt)
        return dfsSum(root, 0)
<|end_body_0|>

<|body_start_1|>
        stk = [(root, 0)]
        s = 0
        while stk:
            p = stk.pop()
            if p[0]:
                base = p[1] * 10 + p[0].val
                if not p[0].left and (not p[0].right):
                    s += base
                else:
                    stk.append((p[0].left, base))
                    stk.append((p[0].right, base))
        return s
<|end_body_1|>

<|body_start_2|>
        queue = [(root, 0)]
        s = 0
        for rt, pre in queue:
            if rt:
                base = rt.val + pre * 10
                if not rt.left and (not rt.right):
                    s += base
                else:
                    queue.append((rt.left, base))
                    queue.append((rt.right, base))
        return s
<|end_body_2|>

Solution:
class Solution:
    def sumNumbers_dfs(self, root):
        """:type root: TreeNode :rtype: int"""
        def dfsSum(root, base):
            if not root:
                return 0
            rt = base * 10 + root.val
            if not root.left and (not root.right):
                return rt
            else:
                return dfsSum(root.left, rt) + dfsSum(root.right, rt)
        return dfsSum(root, 0)

    def sumNumbers_recuisive(self, root):
        """:type root: TreeNode :rtype: int"""
        stk = [(root, 0)]
        s = 0
        while stk:
            p = stk.pop()
            if p[0]:
                base = p[1] * 10 + p[0].val
                if not p[0].left and (not p[0].right):
                    s += base
                else:
                    stk.append((p[0].left, base))
                    stk.append((p[0].right, base))
        return s

    def sumNumbers_bfs(self, root):
        """:type root: TreeNode :rtype: int"""
        queue = [(root, 0)]
        s = 0
        for rt, pre in queue:
            if rt:
                base = rt.val + pre * 10
                if not rt.left and (not rt.right):
                    s += base
                else:
                    queue.append((rt.left, base))
                    queue.append((rt.right, base))
        return s


convert

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

Class description:
Implement the TikaConverter class.

Method signatures and docstrings:
- def __init__(self, tika_url: str='http://localhost:9998/tika', remove_numeric_tables: bool=False, valid_languages: Optional[List[str]]=None, id_hash_keys: Optional[List[str]]=None, timeout: Union[float, Tuple[float, float]]=10.0): :param tika_url: URL of the Tika server :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it
- def convert(self, file_path: Path, meta: Optional[Dict[str, str]]=None, remove_numeric_tables: Optional[bool]=None, valid_languages: Optional[List[str]]=None, encoding: Optional[str]=None, id_hash_keys: Optional[List[str]]=None) -> List[Document]: :param file_path: path of the file to convert :param meta: dictionary of meta data key-value pairs to append in the returned document. :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to

Implement the Python class `TikaConverter` described below.

Class description:
Implement the TikaConverter class.

Method signatures and docstrings:
- def __init__(self, tika_url: str='http://localhost:9998/tika', remove_numeric_tables: bool=False, valid_languages: Optional[List[str]]=None, id_hash_keys: Optional[List[str]]=None, timeout: Union[float, Tuple[float, float]]=10.0): :param tika_url: URL of the Tika server :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it
- def convert(self, file_path: Path, meta: Optional[Dict[str, str]]=None, remove_numeric_tables: Optional[bool]=None, valid_languages: Optional[List[str]]=None, encoding: Optional[str]=None, id_hash_keys: Optional[List[str]]=None) -> List[Document]: :param file_path: path of the file to convert :param meta: dictionary of meta data key-value pairs to append in the returned document. :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to

<|skeleton|>
class TikaConverter:

    def __init__(self, tika_url: str='http://localhost:9998/tika', remove_numeric_tables: bool=False, valid_languages: Optional[List[str]]=None, id_hash_keys: Optional[List[str]]=None, timeout: Union[float, Tuple[float, float]]=10.0):
        """:param tika_url: URL of the Tika server :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it"""
        <|body_0|>

    def convert(self, file_path: Path, meta: Optional[Dict[str, str]]=None, remove_numeric_tables: Optional[bool]=None, valid_languages: Optional[List[str]]=None, encoding: Optional[str]=None, id_hash_keys: Optional[List[str]]=None) -> List[Document]:
        """:param file_path: path of the file to convert :param meta: dictionary of meta data key-value pairs to append in the returned document. :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        tika_import.check()
        super().__init__(remove_numeric_tables=remove_numeric_tables, valid_languages=valid_languages, id_hash_keys=id_hash_keys)
        ping = requests.get(tika_url, timeout=timeout)
        if ping.status_code != 200:
            raise Exception(f"Apache Tika server is not reachable at the URL '{tika_url}'. To run it locallywith Docker, execute: 'docker run -p 9998:9998 apache/tika:1.28.4'")
        self.tika_url = tika_url
        super().__init__(remove_numeric_tables=remove_numeric_tables, valid_languages=valid_languages)
<|end_body_0|>

<|body_start_1|>
        if remove_numeric_tables is None:
            remove_numeric_tables = self.remove_numeric_tables
        if valid_languages is None:
            valid_languages = self.valid_languages
        if id_hash_keys is None:
            id_hash_keys = self.id_hash_keys
        parsed = tika_parser.from_file(file_path.as_posix(), self.tika_url, xmlContent=True)
        parser = TikaXHTMLParser()
        parser.feed(parsed['content'])
        cleaned_pages = []
        for page in parser.pages:
            lines = page.splitlines()
            cleaned_lines = []
            for line in lines:
                words = line.split()
                digits = [word for word in words if any((i.isdigit() for i in word))]
                if remove_numeric_tables:
                    if words and len(digits) / len(words) > 0.4 and (not line.strip().endswith('.')):
                        logger.debug("Removing line '%s' from %s", line, file_path)
                        continue
                cleaned_lines.append(line)
            page = '\n'.join(cleaned_lines)
            cleaned_pages.append(page)
        if valid_languages:
            document_text = ''.join(cleaned_pages)
            if not self.validate_language(document_text, valid_languages):
                logger.warning('The language for %s is not one of %s. The file may not have been decoded in the correct text format.', file_path, valid_languages)
        text = '\x0c'.join(cleaned_pages)
        document = Document(content=text, meta={**parsed['metadata'], **(meta or {})}, id_hash_keys=id_hash_keys)
        return [document]
<|end_body_1|>

Solution:
class TikaConverter:
    def __init__(self, tika_url: str='http://localhost:9998/tika', remove_numeric_tables: bool=False, valid_languages: Optional[List[str]]=None, id_hash_keys: Optional[List[str]]=None, timeout: Union[float, Tuple[float, float]]=10.0):
        """:param tika_url: URL of the Tika server :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to add test for encoding errors. If the extracted text is not one of the valid languages, then it"""
        tika_import.check()
        super().__init__(remove_numeric_tables=remove_numeric_tables, valid_languages=valid_languages, id_hash_keys=id_hash_keys)
        ping = requests.get(tika_url, timeout=timeout)
        if ping.status_code != 200:
            raise Exception(f"Apache Tika server is not reachable at the URL '{tika_url}'. To run it locallywith Docker, execute: 'docker run -p 9998:9998 apache/tika:1.28.4'")
        self.tika_url = tika_url
        super().__init__(remove_numeric_tables=remove_numeric_tables, valid_languages=valid_languages)

    def convert(self, file_path: Path, meta: Optional[Dict[str, str]]=None, remove_numeric_tables: Optional[bool]=None, valid_languages: Optional[List[str]]=None, encoding: Optional[str]=None, id_hash_keys: Optional[List[str]]=None) -> List[Document]:
        """:param file_path: path of the file to convert :param meta: dictionary of meta data key-value pairs to append in the returned document. :param remove_numeric_tables: This option uses heuristics to remove numeric rows from the tables. The tabular structures in documents might be noise for the reader model if it does not have table parsing capability for finding answers. However, tables may also have long strings that could possible candidate for searching answers. The rows containing strings are thus retained in this option. :param valid_languages: validate languages from a list of languages specified in the ISO 639-1 (https://en.wikipedia.org/wiki/ISO_639-1) format. This option can be used to"""
        if remove_numeric_tables is None:
            remove_numeric_tables = self.remove_numeric_tables
        if valid_languages is None:
            valid_languages = self.valid_languages
        if id_hash_keys is None:
            id_hash_keys = self.id_hash_keys
        parsed = tika_parser.from_file(file_path.as_posix(), self.tika_url, xmlContent=True)
        parser = TikaXHTMLParser()
        parser.feed(parsed['content'])
        cleaned_pages = []
        for page in parser.pages:
            lines = page.splitlines()
            cleaned_lines = []
            for line in lines:
                words = line.split()
                digits = [word for word in words if any((i.isdigit() for i in word))]
                if remove_numeric_tables:
                    if words and len(digits) / len(words) > 0.4 and (not line.strip().endswith('.')):
                        logger.debug("Removing line '%s' from %s", line, file_path)
                        continue
                cleaned_lines.append(line)
            page = '\n'.join(cleaned_lines)
            cleaned_pages.append(page)
        if valid_languages:
            document_text = ''.join(cleaned_pages)
            if not self.validate_language(document_text, valid_languages):
                logger.warning('The language for %s is not one of %s. The file may not have been decoded in the correct text format.', file_path, valid_languages)
        text = '\x0c'.join(cleaned_pages)
        document = Document(content=text, meta={**parsed['metadata'], **(meta or {})}, id_hash_keys=id_hash_keys)
        return [document]