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

Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}

Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template

Implement the Python class `AttachmentInline` described below.

Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}

Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template

<|skeleton|>
class AttachmentInline:
    """Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""

    def get_context(self):
        """Renvoyer le contexte de rendu de l'inline"""
        <|body_0|>

    def get_template_name(self):
        """Renvoyer le chemin du template"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        from scoop.content.models import Attachment
        uuid = self.value
        link = Attachment.objects.get_link_by_uuid(uuid)
        return {'link': link}
<|end_body_0|>

<|body_start_1|>
        base = super(AttachmentInline, self).get_template_name()[0]
        path = 'content/{}'.format(base)
        return path
<|end_body_1|>

Solution:
class AttachmentInline:
    """Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""

    def get_context(self):
        """Renvoyer le contexte de rendu de l'inline"""
        from scoop.content.models import Attachment
        uuid = self.value
        link = Attachment.objects.get_link_by_uuid(uuid)
        return {'link': link}

    def get_template_name(self):
        """Renvoyer le chemin du template"""
        base = super(AttachmentInline, self).get_template_name()[0]
        path = 'content/{}'.format(base)
        return path


compute

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

Class description:
A class that iteratively detects peaks in the recording by applying a peak detector, waveform extraction, and waveform denoising node. The algorithm runs for a specified number of iterations or until no peaks are found.

Method signatures and docstrings:
- def __init__(self, recording: BaseRecording, peak_detector_node: PeakDetector, waveform_extraction_node: WaveformsNode, waveform_denoising_node, num_iterations: int=2, return_output: bool=True, tresholds: Optional[List[float]]=None): Initialize the iterative peak detector. Parameters ---------- recording : BaseRecording The recording to process. peak_detector_node : PeakDetector The peak detector node to use. waveform_extraction_node : WaveformsNode The waveform extraction node to use. waveform_denoising_node The waveform denoising node to use. num_iterations : int, optional, default=2 The number of iterations to run the algorithm. return_output : bool, optional, default=True
- def get_trace_margin(self) -> int: Calculate the maximum trace margin from the internal pipeline. Using the strategy as use by the Node pipeline Returns ------- int The maximum trace margin.
- def compute(self, traces_chunk, start_frame, end_frame, segment_index, max_margin) -> Tuple[np.ndarray, np.ndarray]: Perform the iterative peak detection, waveform extraction, and denoising. Parameters ---------- traces_chunk : array-like The chunk of traces to process. start_frame : int The starting frame for the chunk. end_frame : int The ending frame for the chunk. segment_index : int The segment index. max_margin : int The maximum margin for the traces. Returns ------- tuple of ndarray A tuple containing a single ndarray with the detected peaks.
- def substract_waveforms_from_traces(self, local_peaks: np.ndarray, traces_chunk: np.ndarray, waveforms: np.ndarray): Substract inplace the cleaned waveforms from the traces_chunk. Parameters ---------- sample_indices : ndarray The indices where the waveforms are maximum (peaks["sample_index"]). traces_chunk : ndarray A chunk of the traces. waveforms : ndarray The waveforms extracted from the traces.
- def add_iteration_to_peaks_dtype(self, local_peaks, iteration) -> np.ndarray: Add the iteration number to the peaks dtype. Parameters ---------- local_peaks : ndarray The array of local peaks. iteration : int The iteration number. Returns ------- ndarray An array of local peaks with the iteration number added.

Implement the Python class `IterativePeakDetector` described below.

Class description:
A class that iteratively detects peaks in the recording by applying a peak detector, waveform extraction, and waveform denoising node. The algorithm runs for a specified number of iterations or until no peaks are found.

Method signatures and docstrings:
- def __init__(self, recording: BaseRecording, peak_detector_node: PeakDetector, waveform_extraction_node: WaveformsNode, waveform_denoising_node, num_iterations: int=2, return_output: bool=True, tresholds: Optional[List[float]]=None): Initialize the iterative peak detector. Parameters ---------- recording : BaseRecording The recording to process. peak_detector_node : PeakDetector The peak detector node to use. waveform_extraction_node : WaveformsNode The waveform extraction node to use. waveform_denoising_node The waveform denoising node to use. num_iterations : int, optional, default=2 The number of iterations to run the algorithm. return_output : bool, optional, default=True
- def get_trace_margin(self) -> int: Calculate the maximum trace margin from the internal pipeline. Using the strategy as use by the Node pipeline Returns ------- int The maximum trace margin.
- def compute(self, traces_chunk, start_frame, end_frame, segment_index, max_margin) -> Tuple[np.ndarray, np.ndarray]: Perform the iterative peak detection, waveform extraction, and denoising. Parameters ---------- traces_chunk : array-like The chunk of traces to process. start_frame : int The starting frame for the chunk. end_frame : int The ending frame for the chunk. segment_index : int The segment index. max_margin : int The maximum margin for the traces. Returns ------- tuple of ndarray A tuple containing a single ndarray with the detected peaks.
- def substract_waveforms_from_traces(self, local_peaks: np.ndarray, traces_chunk: np.ndarray, waveforms: np.ndarray): Substract inplace the cleaned waveforms from the traces_chunk. Parameters ---------- sample_indices : ndarray The indices where the waveforms are maximum (peaks["sample_index"]). traces_chunk : ndarray A chunk of the traces. waveforms : ndarray The waveforms extracted from the traces.
- def add_iteration_to_peaks_dtype(self, local_peaks, iteration) -> np.ndarray: Add the iteration number to the peaks dtype. Parameters ---------- local_peaks : ndarray The array of local peaks. iteration : int The iteration number. Returns ------- ndarray An array of local peaks with the iteration number added.

<|skeleton|>
class IterativePeakDetector:
    """A class that iteratively detects peaks in the recording by applying a peak detector, waveform extraction, and waveform denoising node. The algorithm runs for a specified number of iterations or until no peaks are found."""

    def __init__(self, recording: BaseRecording, peak_detector_node: PeakDetector, waveform_extraction_node: WaveformsNode, waveform_denoising_node, num_iterations: int=2, return_output: bool=True, tresholds: Optional[List[float]]=None):
        """Initialize the iterative peak detector. Parameters ---------- recording : BaseRecording The recording to process. peak_detector_node : PeakDetector The peak detector node to use. waveform_extraction_node : WaveformsNode The waveform extraction node to use. waveform_denoising_node The waveform denoising node to use. num_iterations : int, optional, default=2 The number of iterations to run the algorithm. return_output : bool, optional, default=True"""
        <|body_0|>

    def get_trace_margin(self) -> int:
        """Calculate the maximum trace margin from the internal pipeline. Using the strategy as use by the Node pipeline Returns ------- int The maximum trace margin."""
        <|body_1|>

    def compute(self, traces_chunk, start_frame, end_frame, segment_index, max_margin) -> Tuple[np.ndarray, np.ndarray]:
        """Perform the iterative peak detection, waveform extraction, and denoising. Parameters ---------- traces_chunk : array-like The chunk of traces to process. start_frame : int The starting frame for the chunk. end_frame : int The ending frame for the chunk. segment_index : int The segment index. max_margin : int The maximum margin for the traces. Returns ------- tuple of ndarray A tuple containing a single ndarray with the detected peaks."""
        <|body_2|>

    def substract_waveforms_from_traces(self, local_peaks: np.ndarray, traces_chunk: np.ndarray, waveforms: np.ndarray):
        """Substract inplace the cleaned waveforms from the traces_chunk. Parameters ---------- sample_indices : ndarray The indices where the waveforms are maximum (peaks["sample_index"]). traces_chunk : ndarray A chunk of the traces. waveforms : ndarray The waveforms extracted from the traces."""
        <|body_3|>

    def add_iteration_to_peaks_dtype(self, local_peaks, iteration) -> np.ndarray:
        """Add the iteration number to the peaks dtype. Parameters ---------- local_peaks : ndarray The array of local peaks. iteration : int The iteration number. Returns ------- ndarray An array of local peaks with the iteration number added."""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        PeakDetector.__init__(self, recording, return_output=return_output)
        self.peak_detector_node = peak_detector_node
        self.waveform_extraction_node = waveform_extraction_node
        self.waveform_denoising_node = waveform_denoising_node
        self.num_iterations = num_iterations
        self.tresholds = tresholds
<|end_body_0|>

<|body_start_1|>
        internal_pipeline = (self.peak_detector_node, self.waveform_extraction_node, self.waveform_denoising_node)
        pipeline_margin = (node.get_trace_margin() for node in internal_pipeline if hasattr(node, 'get_trace_margin'))
        return max(pipeline_margin)
<|end_body_1|>

<|body_start_2|>
        traces_chunk = np.array(traces_chunk, copy=True, dtype='float32')
        local_peaks_list = []
        all_waveforms = []
        for iteration in range(self.num_iterations):
            if self.tresholds is not None:
                old_args = self.peak_detector_node.args
                old_detect_treshold = self.peak_detector_node.params['detect_threshold']
                old_abs_treshold = old_args[1]
                new_abs_treshold = old_abs_treshold * self.tresholds[iteration] / old_detect_treshold
                new_args = tuple((val if index != 1 else new_abs_treshold for index, val in enumerate(old_args)))
                self.peak_detector_node.args = new_args
            local_peaks, = self.peak_detector_node.compute(traces=traces_chunk, start_frame=start_frame, end_frame=end_frame, segment_index=segment_index, max_margin=max_margin)
            local_peaks = self.add_iteration_to_peaks_dtype(local_peaks=local_peaks, iteration=iteration)
            local_peaks_list.append(local_peaks)
            if local_peaks.size == 0:
                break
            waveforms = self.waveform_extraction_node.compute(traces=traces_chunk, peaks=local_peaks)
            denoised_waveforms = self.waveform_denoising_node.compute(traces=traces_chunk, peaks=local_peaks, waveforms=waveforms)
            self.substract_waveforms_from_traces(local_peaks=local_peaks, traces_chunk=traces_chunk, waveforms=denoised_waveforms)
            all_waveforms.append(waveforms)
        all_local_peaks = np.concatenate(local_peaks_list, axis=0)
        all_waveforms = np.concatenate(all_waveforms, axis=0) if len(all_waveforms) != 0 else np.empty((0, 0, 0))
        sorting_indices = np.argsort(all_local_peaks['sample_index'])
        all_local_peaks = all_local_peaks[sorting_indices]
        all_waveforms = all_waveforms[sorting_indices]
        return (all_local_peaks, all_waveforms)
<|end_body_2|>

<|body_start_3|>
        nbefore = self.waveform_extraction_node.nbefore
        nafter = self.waveform_extraction_node.nafter
        if isinstance(self.waveform_extraction_node, ExtractSparseWaveforms):
            neighbours_mask = self.waveform_extraction_node.neighbours_mask
        else:
            neighbours_mask = None
        for peak_index, peak in enumerate(local_peaks):
            center_sample = peak['sample_index']
            first_sample = center_sample - nbefore
            last_sample = center_sample + nafter
            if neighbours_mask is None:
                traces_chunk[first_sample:last_sample, :] -= waveforms[peak_index, :, :]
            else:
                channels, = np.nonzero(neighbours_mask[peak['channel_index']])
                traces_chunk[first_sample:last_sample, channels] -= waveforms[peak_index, :, :len(channels)]
<|end_body_3|>

<|body_start_4|>
        local_peaks_expanded = np.zeros_like(local_peaks, dtype=expanded_base_peak_dtype)
        fields_in_base_type = np.dtype(base_peak_dtype).names
        for field in fields_in_base_type:
            local_peaks_expanded[field] = local_peaks[field]
        local_peaks_expanded['iteration'] = iteration
        return local_peaks_expanded
<|end_body_4|>

Solution:
class IterativePeakDetector:
    """A class that iteratively detects peaks in the recording by applying a peak detector, waveform extraction, and waveform denoising node. The algorithm runs for a specified number of iterations or until no peaks are found."""

    def __init__(self, recording: BaseRecording, peak_detector_node: PeakDetector, waveform_extraction_node: WaveformsNode, waveform_denoising_node, num_iterations: int=2, return_output: bool=True, tresholds: Optional[List[float]]=None):
        """Initialize the iterative peak detector. Parameters ---------- recording : BaseRecording The recording to process. peak_detector_node : PeakDetector The peak detector node to use. waveform_extraction_node : WaveformsNode The waveform extraction node to use. waveform_denoising_node The waveform denoising node to use. num_iterations : int, optional, default=2 The number of iterations to run the algorithm. return_output : bool, optional, default=True"""
        PeakDetector.__init__(self, recording, return_output=return_output)
        self.peak_detector_node = peak_detector_node
        self.waveform_extraction_node = waveform_extraction_node
        self.waveform_denoising_node = waveform_denoising_node
        self.num_iterations = num_iterations
        self.tresholds = tresholds

    def get_trace_margin(self) -> int:
        """Calculate the maximum trace margin from the internal pipeline. Using the strategy as use by the Node pipeline Returns ------- int The maximum trace margin."""
        internal_pipeline = (self.peak_detector_node, self.waveform_extraction_node, self.waveform_denoising_node)
        pipeline_margin = (node.get_trace_margin() for node in internal_pipeline if hasattr(node, 'get_trace_margin'))
        return max(pipeline_margin)

    def compute(self, traces_chunk, start_frame, end_frame, segment_index, max_margin) -> Tuple[np.ndarray, np.ndarray]:
        """Perform the iterative peak detection, waveform extraction, and denoising. Parameters ---------- traces_chunk : array-like The chunk of traces to process. start_frame : int The starting frame for the chunk. end_frame : int The ending frame for the chunk. segment_index : int The segment index. max_margin : int The maximum margin for the traces. Returns ------- tuple of ndarray A tuple containing a single ndarray with the detected peaks."""
        traces_chunk = np.array(traces_chunk, copy=True, dtype='float32')
        local_peaks_list = []
        all_waveforms = []
        for iteration in range(self.num_iterations):
            if self.tresholds is not None:
                old_args = self.peak_detector_node.args
                old_detect_treshold = self.peak_detector_node.params['detect_threshold']
                old_abs_treshold = old_args[1]
                new_abs_treshold = old_abs_treshold * self.tresholds[iteration] / old_detect_treshold
                new_args = tuple((val if index != 1 else new_abs_treshold for index, val in enumerate(old_args)))
                self.peak_detector_node.args = new_args
            local_peaks, = self.peak_detector_node.compute(traces=traces_chunk, start_frame=start_frame, end_frame=end_frame, segment_index=segment_index, max_margin=max_margin)
            local_peaks = self.add_iteration_to_peaks_dtype(local_peaks=local_peaks, iteration=iteration)
            local_peaks_list.append(local_peaks)
            if local_peaks.size == 0:
                break
            waveforms = self.waveform_extraction_node.compute(traces=traces_chunk, peaks=local_peaks)
            denoised_waveforms = self.waveform_denoising_node.compute(traces=traces_chunk, peaks=local_peaks, waveforms=waveforms)
            self.substract_waveforms_from_traces(local_peaks=local_peaks, traces_chunk=traces_chunk, waveforms=denoised_waveforms)
            all_waveforms.append(waveforms)
        all_local_peaks = np.concatenate(local_peaks_list, axis=0)
        all_waveforms = np.concatenate(all_waveforms, axis=0) if len(all_waveforms) != 0 else np.empty((0, 0, 0))
        sorting_indices = np.argsort(all_local_peaks['sample_index'])
        all_local_peaks = all_local_peaks[sorting_indices]
        all_waveforms = all_waveforms[sorting_indices]
        return (all_local_peaks, all_waveforms)

    def substract_waveforms_from_traces(self, local_peaks: np.ndarray, traces_chunk: np.ndarray, waveforms: np.ndarray):
        """Substract inplace the cleaned waveforms from the traces_chunk. Parameters ---------- sample_indices : ndarray The indices where the waveforms are maximum (peaks["sample_index"]). traces_chunk : ndarray A chunk of the traces. waveforms : ndarray The waveforms extracted from the traces."""
        nbefore = self.waveform_extraction_node.nbefore
        nafter = self.waveform_extraction_node.nafter
        if isinstance(self.waveform_extraction_node, ExtractSparseWaveforms):
            neighbours_mask = self.waveform_extraction_node.neighbours_mask
        else:
            neighbours_mask = None
        for peak_index, peak in enumerate(local_peaks):
            center_sample = peak['sample_index']
            first_sample = center_sample - nbefore
            last_sample = center_sample + nafter
            if neighbours_mask is None:
                traces_chunk[first_sample:last_sample, :] -= waveforms[peak_index, :, :]
            else:
                channels, = np.nonzero(neighbours_mask[peak['channel_index']])
                traces_chunk[first_sample:last_sample, channels] -= waveforms[peak_index, :, :len(channels)]

    def add_iteration_to_peaks_dtype(self, local_peaks, iteration) -> np.ndarray:
        """Add the iteration number to the peaks dtype. Parameters ---------- local_peaks : ndarray The array of local peaks. iteration : int The iteration number. Returns ------- ndarray An array of local peaks with the iteration number added."""
        local_peaks_expanded = np.zeros_like(local_peaks, dtype=expanded_base_peak_dtype)
        fields_in_base_type = np.dtype(base_peak_dtype).names
        for field in fields_in_base_type:
            local_peaks_expanded[field] = local_peaks[field]
        local_peaks_expanded['iteration'] = iteration
        return local_peaks_expanded


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def climbStairs2(self, n): :type n: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def climbStairs2(self, n): :type n: int :rtype: int

<|skeleton|>
class Solution:

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

    def climbStairs2(self, n):
        """:type n: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if n < 3:
            return n
        one_step_before, two_steps_before = (2, 1)
        all_ways = 0
        for _ in range(3, n + 1):
            all_ways = one_step_before + two_steps_before
            two_steps_before = one_step_before
            one_step_before = all_ways
        return all_ways
<|end_body_0|>

<|body_start_1|>
        dp = [0] * (n + 1)
        dp[0] = 1
        while True:
            dp2 = [0] * (n + 1)
            dp2[-1] = dp[-1]
            for i in range(n):
                if i + 1 <= n:
                    dp2[i + 1] += dp[i]
                if i + 2 <= n:
                    dp2[i + 2] += dp[i]
            if dp == dp2:
                return dp2[-1]
            else:
                dp = dp2
        return 0
<|end_body_1|>

Solution:
class Solution:
    def climbStairs(self, n):
        """:type n: int :rtype: int"""
        if n < 3:
            return n
        one_step_before, two_steps_before = (2, 1)
        all_ways = 0
        for _ in range(3, n + 1):
            all_ways = one_step_before + two_steps_before
            two_steps_before = one_step_before
            one_step_before = all_ways
        return all_ways

    def climbStairs2(self, n):
        """:type n: int :rtype: int"""
        dp = [0] * (n + 1)
        dp[0] = 1
        while True:
            dp2 = [0] * (n + 1)
            dp2[-1] = dp[-1]
            for i in range(n):
                if i + 1 <= n:
                    dp2[i + 1] += dp[i]
                if i + 2 <= n:
                    dp2[i + 2] += dp[i]
            if dp == dp2:
                return dp2[-1]
            else:
                dp = dp2
        return 0


segment

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

Class description:
Implement the WordSegmentation class.

Method signatures and docstrings:
- def __init__(self, stop_words_file=None, allow_speech_tags=allow_speech_tags): Keyword arguments: stop_words_file -- 保存停止词的文件路径，utf8编码，每行一个停止词。若不是str类型，则使用默认的停止词 allow_speech_tags -- 词性列表，用于过滤
- def segment(self, text, lower=True, use_stop_words=True, use_speech_tags_filter=False): 对一段文本进行分词，返回list类型的分词结果 Keyword arguments: lower -- 是否将单词小写（针对英文） use_stop_words -- 若为True，则利用停止词集合来过滤（去掉停止词） use_speech_tags_filter -- 是否基于词性进行过滤。若为True，则使用self.default_speech_tag_filter过滤。否则，不过滤。
- def segment_sentences(self, sentences, lower=True, use_stop_words=True, use_speech_tags_filter=False): 将列表sequences中的每个元素/句子转换为由单词构成的列表。 sequences -- 列表，每个元素是一个句子（字符串类型）

Implement the Python class `WordSegmentation` described below.

Class description:
Implement the WordSegmentation class.

Method signatures and docstrings:
- def __init__(self, stop_words_file=None, allow_speech_tags=allow_speech_tags): Keyword arguments: stop_words_file -- 保存停止词的文件路径，utf8编码，每行一个停止词。若不是str类型，则使用默认的停止词 allow_speech_tags -- 词性列表，用于过滤
- def segment(self, text, lower=True, use_stop_words=True, use_speech_tags_filter=False): 对一段文本进行分词，返回list类型的分词结果 Keyword arguments: lower -- 是否将单词小写（针对英文） use_stop_words -- 若为True，则利用停止词集合来过滤（去掉停止词） use_speech_tags_filter -- 是否基于词性进行过滤。若为True，则使用self.default_speech_tag_filter过滤。否则，不过滤。
- def segment_sentences(self, sentences, lower=True, use_stop_words=True, use_speech_tags_filter=False): 将列表sequences中的每个元素/句子转换为由单词构成的列表。 sequences -- 列表，每个元素是一个句子（字符串类型）

<|skeleton|>
class WordSegmentation:

    def __init__(self, stop_words_file=None, allow_speech_tags=allow_speech_tags):
        """Keyword arguments: stop_words_file -- 保存停止词的文件路径，utf8编码，每行一个停止词。若不是str类型，则使用默认的停止词 allow_speech_tags -- 词性列表，用于过滤"""
        <|body_0|>

    def segment(self, text, lower=True, use_stop_words=True, use_speech_tags_filter=False):
        """对一段文本进行分词，返回list类型的分词结果 Keyword arguments: lower -- 是否将单词小写（针对英文） use_stop_words -- 若为True，则利用停止词集合来过滤（去掉停止词） use_speech_tags_filter -- 是否基于词性进行过滤。若为True，则使用self.default_speech_tag_filter过滤。否则，不过滤。"""
        <|body_1|>

    def segment_sentences(self, sentences, lower=True, use_stop_words=True, use_speech_tags_filter=False):
        """将列表sequences中的每个元素/句子转换为由单词构成的列表。 sequences -- 列表，每个元素是一个句子（字符串类型）"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        allow_speech_tags = [as_text(item) for item in allow_speech_tags]
        self.default_speech_tag_filter = allow_speech_tags
        self.stop_words = set()
        self.stop_words_file = stopwords_path
        if type(stop_words_file) is str:
            self.stop_words_file = stop_words_file
        for word in codecs.open(self.stop_words_file, 'r', 'utf-8', 'ignore'):
            self.stop_words.add(word.strip())
<|end_body_0|>

<|body_start_1|>
        text = as_text(text)
        jieba_result = pseg.cut(text)
        if use_speech_tags_filter == True:
            jieba_result = [w for w in jieba_result if w.flag in self.default_speech_tag_filter]
        else:
            jieba_result = [w for w in jieba_result]
        word_list = [w.word.strip() for w in jieba_result if w.flag != 'x']
        word_list = [word for word in word_list if len(word) > 0]
        if lower:
            word_list = [word.lower() for word in word_list]
        if use_stop_words:
            word_list = [word.strip() for word in word_list if word.strip() not in self.stop_words]
        return word_list
<|end_body_1|>

<|body_start_2|>
        res = []
        for sentence in sentences:
            res.append(self.segment(text=sentence, lower=lower, use_stop_words=use_stop_words, use_speech_tags_filter=use_speech_tags_filter))
        return res
<|end_body_2|>

Solution:
class WordSegmentation:
    def __init__(self, stop_words_file=None, allow_speech_tags=allow_speech_tags):
        """Keyword arguments: stop_words_file -- 保存停止词的文件路径，utf8编码，每行一个停止词。若不是str类型，则使用默认的停止词 allow_speech_tags -- 词性列表，用于过滤"""
        allow_speech_tags = [as_text(item) for item in allow_speech_tags]
        self.default_speech_tag_filter = allow_speech_tags
        self.stop_words = set()
        self.stop_words_file = stopwords_path
        if type(stop_words_file) is str:
            self.stop_words_file = stop_words_file
        for word in codecs.open(self.stop_words_file, 'r', 'utf-8', 'ignore'):
            self.stop_words.add(word.strip())

    def segment(self, text, lower=True, use_stop_words=True, use_speech_tags_filter=False):
        """对一段文本进行分词，返回list类型的分词结果 Keyword arguments: lower -- 是否将单词小写（针对英文） use_stop_words -- 若为True，则利用停止词集合来过滤（去掉停止词） use_speech_tags_filter -- 是否基于词性进行过滤。若为True，则使用self.default_speech_tag_filter过滤。否则，不过滤。"""
        text = as_text(text)
        jieba_result = pseg.cut(text)
        if use_speech_tags_filter == True:
            jieba_result = [w for w in jieba_result if w.flag in self.default_speech_tag_filter]
        else:
            jieba_result = [w for w in jieba_result]
        word_list = [w.word.strip() for w in jieba_result if w.flag != 'x']
        word_list = [word for word in word_list if len(word) > 0]
        if lower:
            word_list = [word.lower() for word in word_list]
        if use_stop_words:
            word_list = [word.strip() for word in word_list if word.strip() not in self.stop_words]
        return word_list

    def segment_sentences(self, sentences, lower=True, use_stop_words=True, use_speech_tags_filter=False):
        """将列表sequences中的每个元素/句子转换为由单词构成的列表。 sequences -- 列表，每个元素是一个句子（字符串类型）"""
        res = []
        for sentence in sentences:
            res.append(self.segment(text=sentence, lower=lower, use_stop_words=use_stop_words, use_speech_tags_filter=use_speech_tags_filter))
        return res


serialize

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

Class description:
Implement the ServiceAnnouncementAttachment class.

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

Implement the Python class `ServiceAnnouncementAttachment` described below.

Class description:
Implement the ServiceAnnouncementAttachment class.

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

<|skeleton|>
class ServiceAnnouncementAttachment:

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

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

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

<|end_skeleton|>

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

<|body_start_1|>
        from .entity import Entity
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'content': lambda n: setattr(self, 'content', n.get_bytes_value()), 'contentType': lambda n: setattr(self, 'content_type', n.get_str_value()), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'name': lambda n: setattr(self, 'name', n.get_str_value()), 'size': lambda n: setattr(self, 'size', n.get_int_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields
<|end_body_1|>

<|body_start_2|>
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_bytes_value('content', self.content)
        writer.write_str_value('contentType', self.content_type)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_str_value('name', self.name)
        writer.write_int_value('size', self.size)
<|end_body_2|>

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

    def get_field_deserializers(self) -> Dict[str, Callable[[ParseNode], None]]:
        """The deserialization information for the current model Returns: Dict[str, Callable[[ParseNode], None]]"""
        from .entity import Entity
        from .entity import Entity
        fields: Dict[str, Callable[[Any], None]] = {'content': lambda n: setattr(self, 'content', n.get_bytes_value()), 'contentType': lambda n: setattr(self, 'content_type', n.get_str_value()), 'lastModifiedDateTime': lambda n: setattr(self, 'last_modified_date_time', n.get_datetime_value()), 'name': lambda n: setattr(self, 'name', n.get_str_value()), 'size': lambda n: setattr(self, 'size', n.get_int_value())}
        super_fields = super().get_field_deserializers()
        fields.update(super_fields)
        return fields

    def serialize(self, writer: SerializationWriter) -> None:
        """Serializes information the current object Args: writer: Serialization writer to use to serialize this model"""
        if not writer:
            raise TypeError('writer cannot be null.')
        super().serialize(writer)
        writer.write_bytes_value('content', self.content)
        writer.write_str_value('contentType', self.content_type)
        writer.write_datetime_value('lastModifiedDateTime', self.last_modified_date_time)
        writer.write_str_value('name', self.name)
        writer.write_int_value('size', self.size)


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

Class description:
Implement the Importer class.

Method signatures and docstrings:
- def __init__(self, io_loop, source, zk_locations, max_concurrency): Args: io_loop: an instance of tornado IOLoop. source: an instance of import Source (e.g.: S3Source). zk_locations: a list - Zookeeper locations. max_concurrency: an int - maximum number of concurrent jobs.
- async def import_(self): Starts concurrent jobs for importing all search indexes. Then it waits for all started jobs to be completed.
- async def import_index(self, project_id, namespace, index): Starts concurrent jobs for importing entire index. import_ method will wait for these jobs to be completed. Args: project_id: a str - GAE project ID. namespace: a str - GAE search service namespace. index: a str - GAE Search index name.
- async def import_page(self, object_key): Imports a single object from backup. An object is instance of search_pb2.IndexDocumentResponse. Args: object_key: a str - storage object identifier.

Implement the Python class `Importer` described below.

Class description:
Implement the Importer class.

Method signatures and docstrings:
- def __init__(self, io_loop, source, zk_locations, max_concurrency): Args: io_loop: an instance of tornado IOLoop. source: an instance of import Source (e.g.: S3Source). zk_locations: a list - Zookeeper locations. max_concurrency: an int - maximum number of concurrent jobs.
- async def import_(self): Starts concurrent jobs for importing all search indexes. Then it waits for all started jobs to be completed.
- async def import_index(self, project_id, namespace, index): Starts concurrent jobs for importing entire index. import_ method will wait for these jobs to be completed. Args: project_id: a str - GAE project ID. namespace: a str - GAE search service namespace. index: a str - GAE Search index name.
- async def import_page(self, object_key): Imports a single object from backup. An object is instance of search_pb2.IndexDocumentResponse. Args: object_key: a str - storage object identifier.

<|skeleton|>
class Importer:

    def __init__(self, io_loop, source, zk_locations, max_concurrency):
        """Args: io_loop: an instance of tornado IOLoop. source: an instance of import Source (e.g.: S3Source). zk_locations: a list - Zookeeper locations. max_concurrency: an int - maximum number of concurrent jobs."""
        <|body_0|>

    async def import_(self):
        """Starts concurrent jobs for importing all search indexes. Then it waits for all started jobs to be completed."""
        <|body_1|>

    async def import_index(self, project_id, namespace, index):
        """Starts concurrent jobs for importing entire index. import_ method will wait for these jobs to be completed. Args: project_id: a str - GAE project ID. namespace: a str - GAE search service namespace. index: a str - GAE Search index name."""
        <|body_2|>

    async def import_page(self, object_key):
        """Imports a single object from backup. An object is instance of search_pb2.IndexDocumentResponse. Args: object_key: a str - storage object identifier."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        zk_client = KazooClient(hosts=','.join(zk_locations), connection_retry=ZK_PERSISTENT_RECONNECTS)
        zk_client.start()
        self.ioloop = io_loop
        self.source = source
        self.start_time = None
        self.status = 'Not started'
        self.finish_time = None
        self.api_methods = api_methods.APIMethods(zk_client)
        self.scheduled_indexes = set()
        self.scheduled_jobs = set()
        self.failed_jobs = set()
        self.succeeded_jobs = set()
        self.max_concurrency = max_concurrency
        self.docs_imported = 0
<|end_body_0|>

<|body_start_1|>
        self.start_time = self.ioloop.time()
        self.status = 'In progress'
        for project_id, namespace, index in self.source.iter_indexes():
            self.scheduled_indexes.add((project_id, namespace, index))
            self.ioloop.add_callback(self.import_index, project_id, namespace, index)
        logger.info('Waiting for all import jobs to be completed')
        while self.scheduled_indexes or self.scheduled_jobs:
            await gen.sleep(0.25)
        logger.info('Import has been finished and took {:.2f}s'.format(self.ioloop.time() - self.start_time))
        logger.info(' - {} jobs failed'.format(len(self.failed_jobs)))
        logger.info(' - {} jobs succeeded ({} documents)'.format(len(self.succeeded_jobs), self.docs_imported))
        self.status = 'Done'
        self.finish_time = self.ioloop.time()
<|end_body_1|>

<|body_start_2|>
        logger.info('Starting import of index: {}/{}/{}'.format(project_id, namespace, index))
        first_page = True
        for key in self.source.iter_object_keys(project_id, namespace, index):
            while len(self.scheduled_jobs) > self.max_concurrency:
                await gen.sleep(0.25 * (random.random() + 0.5))
            self.scheduled_jobs.add(key)
            self.ioloop.add_callback(self.import_page, key)
            if first_page:
                await gen.sleep(1)
                first_page = False
        self.scheduled_indexes.remove((project_id, namespace, index))
<|end_body_2|>

<|body_start_3|>
        logger.debug('Starting import of object: {}'.format(object_key))
        for attempt in range(self.max_retries):
            try:
                index_documents_pb = self.source.get_index_documents_pb(object_key)
                response = search_pb2.IndexDocumentResponse()
                await self.api_methods.index_document(index_documents_pb, response)
                self.docs_imported += len(index_documents_pb.params.document)
                self.succeeded_jobs.add(object_key)
                self.scheduled_jobs.remove(object_key)
                logger.debug('Successfully imported object: {}'.format(object_key))
                break
            except Exception as err:
                logger.error('Failed to import object with key "{}" ({})'.format(object_key, err))
                if attempt < self.max_retries - 1:
                    backoff = 0.2 * 2 ** attempt
                    logger.info('Retrying in {:.1f}s'.format(backoff))
                    await gen.sleep(backoff)
                else:
                    self.failed_jobs.add(object_key)
                    self.scheduled_jobs.remove(object_key)
                    raise
<|end_body_3|>

Solution:
class Importer:
    def __init__(self, io_loop, source, zk_locations, max_concurrency):
        """Args: io_loop: an instance of tornado IOLoop. source: an instance of import Source (e.g.: S3Source). zk_locations: a list - Zookeeper locations. max_concurrency: an int - maximum number of concurrent jobs."""
        zk_client = KazooClient(hosts=','.join(zk_locations), connection_retry=ZK_PERSISTENT_RECONNECTS)
        zk_client.start()
        self.ioloop = io_loop
        self.source = source
        self.start_time = None
        self.status = 'Not started'
        self.finish_time = None
        self.api_methods = api_methods.APIMethods(zk_client)
        self.scheduled_indexes = set()
        self.scheduled_jobs = set()
        self.failed_jobs = set()
        self.succeeded_jobs = set()
        self.max_concurrency = max_concurrency
        self.docs_imported = 0

    async def import_(self):
        """Starts concurrent jobs for importing all search indexes. Then it waits for all started jobs to be completed."""
        self.start_time = self.ioloop.time()
        self.status = 'In progress'
        for project_id, namespace, index in self.source.iter_indexes():
            self.scheduled_indexes.add((project_id, namespace, index))
            self.ioloop.add_callback(self.import_index, project_id, namespace, index)
        logger.info('Waiting for all import jobs to be completed')
        while self.scheduled_indexes or self.scheduled_jobs:
            await gen.sleep(0.25)
        logger.info('Import has been finished and took {:.2f}s'.format(self.ioloop.time() - self.start_time))
        logger.info(' - {} jobs failed'.format(len(self.failed_jobs)))
        logger.info(' - {} jobs succeeded ({} documents)'.format(len(self.succeeded_jobs), self.docs_imported))
        self.status = 'Done'
        self.finish_time = self.ioloop.time()

    async def import_index(self, project_id, namespace, index):
        """Starts concurrent jobs for importing entire index. import_ method will wait for these jobs to be completed. Args: project_id: a str - GAE project ID. namespace: a str - GAE search service namespace. index: a str - GAE Search index name."""
        logger.info('Starting import of index: {}/{}/{}'.format(project_id, namespace, index))
        first_page = True
        for key in self.source.iter_object_keys(project_id, namespace, index):
            while len(self.scheduled_jobs) > self.max_concurrency:
                await gen.sleep(0.25 * (random.random() + 0.5))
            self.scheduled_jobs.add(key)
            self.ioloop.add_callback(self.import_page, key)
            if first_page:
                await gen.sleep(1)
                first_page = False
        self.scheduled_indexes.remove((project_id, namespace, index))

    async def import_page(self, object_key):
        """Imports a single object from backup. An object is instance of search_pb2.IndexDocumentResponse. Args: object_key: a str - storage object identifier."""
        logger.debug('Starting import of object: {}'.format(object_key))
        for attempt in range(self.max_retries):
            try:
                index_documents_pb = self.source.get_index_documents_pb(object_key)
                response = search_pb2.IndexDocumentResponse()
                await self.api_methods.index_document(index_documents_pb, response)
                self.docs_imported += len(index_documents_pb.params.document)
                self.succeeded_jobs.add(object_key)
                self.scheduled_jobs.remove(object_key)
                logger.debug('Successfully imported object: {}'.format(object_key))
                break
            except Exception as err:
                logger.error('Failed to import object with key "{}" ({})'.format(object_key, err))
                if attempt < self.max_retries - 1:
                    backoff = 0.2 * 2 ** attempt
                    logger.info('Retrying in {:.1f}s'.format(backoff))
                    await gen.sleep(backoff)
                else:
                    self.failed_jobs.add(object_key)
                    self.scheduled_jobs.remove(object_key)
                    raise


generateXML

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

Class description:
A type that allows a set list of strings

Method signatures and docstrings:
- def createClass(cls, name, xmlType, enumList): creates a new enumeration type. @ In, name, string, the name of the type @ In, xmlType, string, the name used for the xml type. @ In, enumList, [string], a list of allowable strings. @ Out, None
- def generateXML(cls, xsdNode): Generates the xml data. @ In, xsdNode, xml.etree.ElementTree.Element, the element to add the new xml type to. @ Out, None

Implement the Python class `EnumBaseType` described below.

Class description:
A type that allows a set list of strings

Method signatures and docstrings:
- def createClass(cls, name, xmlType, enumList): creates a new enumeration type. @ In, name, string, the name of the type @ In, xmlType, string, the name used for the xml type. @ In, enumList, [string], a list of allowable strings. @ Out, None
- def generateXML(cls, xsdNode): Generates the xml data. @ In, xsdNode, xml.etree.ElementTree.Element, the element to add the new xml type to. @ Out, None

<|skeleton|>
class EnumBaseType:
    """A type that allows a set list of strings"""

    def createClass(cls, name, xmlType, enumList):
        """creates a new enumeration type. @ In, name, string, the name of the type @ In, xmlType, string, the name used for the xml type. @ In, enumList, [string], a list of allowable strings. @ Out, None"""
        <|body_0|>

    def generateXML(cls, xsdNode):
        """Generates the xml data. @ In, xsdNode, xml.etree.ElementTree.Element, the element to add the new xml type to. @ Out, None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        cls.__name__ = str(name + 'Spec')
        cls.name = name
        cls.xmlType = xmlType
        cls.needGenerating = True
        cls.enumList = enumList
<|end_body_0|>

<|body_start_1|>
        simpleType = ET.SubElement(xsdNode, 'xsd:simpleType')
        simpleType.set('name', cls.getXMLType())
        restriction = ET.SubElement(simpleType, 'xsd:restriction')
        restriction.set('base', 'xsd:string')
        for enum in cls.enumList:
            enumNode = ET.SubElement(restriction, 'xsd:enumeration')
            enumNode.set('value', enum)
<|end_body_1|>

Solution:
class EnumBaseType:
    """A type that allows a set list of strings"""

    def createClass(cls, name, xmlType, enumList):
        """creates a new enumeration type. @ In, name, string, the name of the type @ In, xmlType, string, the name used for the xml type. @ In, enumList, [string], a list of allowable strings. @ Out, None"""
        cls.__name__ = str(name + 'Spec')
        cls.name = name
        cls.xmlType = xmlType
        cls.needGenerating = True
        cls.enumList = enumList

    def generateXML(cls, xsdNode):
        """Generates the xml data. @ In, xsdNode, xml.etree.ElementTree.Element, the element to add the new xml type to. @ Out, None"""
        simpleType = ET.SubElement(xsdNode, 'xsd:simpleType')
        simpleType.set('name', cls.getXMLType())
        restriction = ET.SubElement(simpleType, 'xsd:restriction')
        restriction.set('base', 'xsd:string')
        for enum in cls.enumList:
            enumNode = ET.SubElement(restriction, 'xsd:enumeration')
            enumNode.set('value', enum)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lastRemaining(self, n, m): :type n: int :type m: int :rtype: int
- def lastRemaining2(self, n, m): :type n: int :type m: int :rtype: int

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lastRemaining(self, n, m): :type n: int :type m: int :rtype: int
- def lastRemaining2(self, n, m): :type n: int :type m: int :rtype: int

<|skeleton|>
class Solution:

    def lastRemaining(self, n, m):
        """:type n: int :type m: int :rtype: int"""
        <|body_0|>

    def lastRemaining2(self, n, m):
        """:type n: int :type m: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        nums = [i for i in range(n)]
        index = 0
        while n > 1:
            index = (index + m - 1) % n
            nums.pop(index)
            n -= 1
        return nums[0]
<|end_body_0|>

<|body_start_1|>
        if n == 1:
            return 0
        return (self.lastRemaining2(n - 1, m) + m) % n
<|end_body_1|>

Solution:
class Solution:
    def lastRemaining(self, n, m):
        """:type n: int :type m: int :rtype: int"""
        nums = [i for i in range(n)]
        index = 0
        while n > 1:
            index = (index + m - 1) % n
            nums.pop(index)
            n -= 1
        return nums[0]

    def lastRemaining2(self, n, m):
        """:type n: int :type m: int :rtype: int"""
        if n == 1:
            return 0
        return (self.lastRemaining2(n - 1, m) + m) % n


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

Class description:
Generator for fully connected networks.

Method signatures and docstrings:
- def __init__(self, dim_input=1, dim_output=1, layer_sizes=(64,), activation_fn=tf.nn.tanh): Creates fully connected neural networks. Args: dim_input: Dimensionality of input (integer > 0). dim_output: Dimensionality of output (integer > 0). layer_sizes: non-empty list with number of neurons per internal layer. activation_fn: activation function for hidden layers
- def construct_network_weights(self, scope='weights'): Creates weights for fully connected neural network. Args: scope: variable scope Returns: A dict with weights (network parameters).
- def construct_network(self, network_input, weights, scope='network'): Creates a fully connected neural network with given weights and input. Args: network_input: Network input (1d). weights: network parameters (see construct_network_weights). scope: name scope. Returns: neural network output op

Implement the Python class `FullyConnectedNetworkGenerator` described below.

Class description:
Generator for fully connected networks.

Method signatures and docstrings:
- def __init__(self, dim_input=1, dim_output=1, layer_sizes=(64,), activation_fn=tf.nn.tanh): Creates fully connected neural networks. Args: dim_input: Dimensionality of input (integer > 0). dim_output: Dimensionality of output (integer > 0). layer_sizes: non-empty list with number of neurons per internal layer. activation_fn: activation function for hidden layers
- def construct_network_weights(self, scope='weights'): Creates weights for fully connected neural network. Args: scope: variable scope Returns: A dict with weights (network parameters).
- def construct_network(self, network_input, weights, scope='network'): Creates a fully connected neural network with given weights and input. Args: network_input: Network input (1d). weights: network parameters (see construct_network_weights). scope: name scope. Returns: neural network output op

<|skeleton|>
class FullyConnectedNetworkGenerator:
    """Generator for fully connected networks."""

    def __init__(self, dim_input=1, dim_output=1, layer_sizes=(64,), activation_fn=tf.nn.tanh):
        """Creates fully connected neural networks. Args: dim_input: Dimensionality of input (integer > 0). dim_output: Dimensionality of output (integer > 0). layer_sizes: non-empty list with number of neurons per internal layer. activation_fn: activation function for hidden layers"""
        <|body_0|>

    def construct_network_weights(self, scope='weights'):
        """Creates weights for fully connected neural network. Args: scope: variable scope Returns: A dict with weights (network parameters)."""
        <|body_1|>

    def construct_network(self, network_input, weights, scope='network'):
        """Creates a fully connected neural network with given weights and input. Args: network_input: Network input (1d). weights: network parameters (see construct_network_weights). scope: name scope. Returns: neural network output op"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.layer_sizes = layer_sizes
        self.activation_fn = activation_fn
<|end_body_0|>

<|body_start_1|>
        weights = {}
        with tf.variable_scope(scope):
            weights['w_0'] = tf.Variable(tf.truncated_normal([self.dim_input, self.layer_sizes[0]], stddev=0.1), name='w_0')
            weights['b_0'] = tf.Variable(tf.zeros([self.layer_sizes[0]]), name='b_0')
            for i in range(1, len(self.layer_sizes)):
                weights['w_%d' % i] = tf.Variable(tf.truncated_normal([self.layer_sizes[i - 1], self.layer_sizes[i]], stddev=0.1), name='w_%d' % i)
                weights['b_%d' % i] = tf.Variable(tf.zeros([self.layer_sizes[i]]), name='b_%d' % i)
            weights['w_out'] = tf.Variable(tf.truncated_normal([self.layer_sizes[-1], self.dim_output], stddev=0.1), name='w_out')
            weights['b_out'] = tf.Variable(tf.zeros([self.dim_output]), name='b_out')
        return weights
<|end_body_1|>

<|body_start_2|>
        num_layers = len(self.layer_sizes)
        with tf.name_scope(scope):
            hidden = self.activation_fn(tf.nn.xw_plus_b(network_input, weights['w_0'], weights['b_0'], name='hidden_0'))
            for i in range(1, num_layers):
                hidden = self.activation_fn(tf.nn.xw_plus_b(hidden, weights['w_%d' % i], weights['b_%d' % i], name='hidden_%d' % i))
            return tf.nn.xw_plus_b(hidden, weights['w_out'], weights['b_out'], name='output')
<|end_body_2|>

Solution:
class FullyConnectedNetworkGenerator:
    """Generator for fully connected networks."""

    def __init__(self, dim_input=1, dim_output=1, layer_sizes=(64,), activation_fn=tf.nn.tanh):
        """Creates fully connected neural networks. Args: dim_input: Dimensionality of input (integer > 0). dim_output: Dimensionality of output (integer > 0). layer_sizes: non-empty list with number of neurons per internal layer. activation_fn: activation function for hidden layers"""
        self.dim_input = dim_input
        self.dim_output = dim_output
        self.layer_sizes = layer_sizes
        self.activation_fn = activation_fn

    def construct_network_weights(self, scope='weights'):
        """Creates weights for fully connected neural network. Args: scope: variable scope Returns: A dict with weights (network parameters)."""
        weights = {}
        with tf.variable_scope(scope):
            weights['w_0'] = tf.Variable(tf.truncated_normal([self.dim_input, self.layer_sizes[0]], stddev=0.1), name='w_0')
            weights['b_0'] = tf.Variable(tf.zeros([self.layer_sizes[0]]), name='b_0')
            for i in range(1, len(self.layer_sizes)):
                weights['w_%d' % i] = tf.Variable(tf.truncated_normal([self.layer_sizes[i - 1], self.layer_sizes[i]], stddev=0.1), name='w_%d' % i)
                weights['b_%d' % i] = tf.Variable(tf.zeros([self.layer_sizes[i]]), name='b_%d' % i)
            weights['w_out'] = tf.Variable(tf.truncated_normal([self.layer_sizes[-1], self.dim_output], stddev=0.1), name='w_out')
            weights['b_out'] = tf.Variable(tf.zeros([self.dim_output]), name='b_out')
        return weights

    def construct_network(self, network_input, weights, scope='network'):
        """Creates a fully connected neural network with given weights and input. Args: network_input: Network input (1d). weights: network parameters (see construct_network_weights). scope: name scope. Returns: neural network output op"""
        num_layers = len(self.layer_sizes)
        with tf.name_scope(scope):
            hidden = self.activation_fn(tf.nn.xw_plus_b(network_input, weights['w_0'], weights['b_0'], name='hidden_0'))
            for i in range(1, num_layers):
                hidden = self.activation_fn(tf.nn.xw_plus_b(hidden, weights['w_%d' % i], weights['b_%d' % i], name='hidden_%d' % i))
            return tf.nn.xw_plus_b(hidden, weights['w_out'], weights['b_out'], name='output')


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

Class description:
View that handles viewing messages to be moderated

Method signatures and docstrings:
- def get_context_data(self, **kwargs): Add additional context to the moderation page
- def get_queryset(self): Get the queryset for the page

Implement the Python class `ModeratorView` described below.

Class description:
View that handles viewing messages to be moderated

Method signatures and docstrings:
- def get_context_data(self, **kwargs): Add additional context to the moderation page
- def get_queryset(self): Get the queryset for the page

<|skeleton|>
class ModeratorView:
    """View that handles viewing messages to be moderated"""

    def get_context_data(self, **kwargs):
        """Add additional context to the moderation page"""
        <|body_0|>

    def get_queryset(self):
        """Get the queryset for the page"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        context = super(ModeratorView, self).get_context_data(**kwargs)
        group_id = self.kwargs.get('group')
        if group_id:
            context['group'] = get_object_or_404(Group, pk=group_id)
        return context
<|end_body_0|>

<|body_start_1|>
        queryset = self.request.user.messages_to_moderate
        group = self.kwargs.get('group')
        if group:
            queryset = queryset.filter(thread__group_id=group)
        queryset = queryset.select_related('thread', 'thread__group', 'thread__group__group', 'sender')
        return queryset
<|end_body_1|>

Solution:
class ModeratorView:
    """View that handles viewing messages to be moderated"""

    def get_context_data(self, **kwargs):
        """Add additional context to the moderation page"""
        context = super(ModeratorView, self).get_context_data(**kwargs)
        group_id = self.kwargs.get('group')
        if group_id:
            context['group'] = get_object_or_404(Group, pk=group_id)
        return context

    def get_queryset(self):
        """Get the queryset for the page"""
        queryset = self.request.user.messages_to_moderate
        group = self.kwargs.get('group')
        if group:
            queryset = queryset.filter(thread__group_id=group)
        queryset = queryset.select_related('thread', 'thread__group', 'thread__group__group', 'sender')
        return queryset


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

Class description:
Parameter representing a :py:class:`pyrate.shared.RasterBase` sub class.

Method signatures and docstrings:
- def parse(self, string): override of :py:meth:`DictParam.parse`.
- def serialize(self, rasterBase): override of :py:meth:`DictParam.serialize`.

Implement the Python class `RasterParam` described below.

Class description:
Parameter representing a :py:class:`pyrate.shared.RasterBase` sub class.

Method signatures and docstrings:
- def parse(self, string): override of :py:meth:`DictParam.parse`.
- def serialize(self, rasterBase): override of :py:meth:`DictParam.serialize`.

<|skeleton|>
class RasterParam:
    """Parameter representing a :py:class:`pyrate.shared.RasterBase` sub class."""

    def parse(self, string):
        """override of :py:meth:`DictParam.parse`."""
        <|body_0|>

    def serialize(self, rasterBase):
        """override of :py:meth:`DictParam.serialize`."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        dct = super(RasterParam, self).parse(string)
        raster_type = dct['type']
        path = dct['path']
        if raster_type == 'DEM':
            return DEM(path)
        elif raster_type == 'Ifg':
            return Ifg(path)
        elif raster_type == 'Incidence':
            return Incidence(path)
        else:
            raise luigi.parameter.UnknownParameterException('rasterBase must be an inscance DEM, Ifg or Incidence is valid')
<|end_body_0|>

<|body_start_1|>
        path = rasterBase.data_path
        if isinstance(rasterBase, DEM):
            d = {'type': 'DEM', 'path': path}
        elif isinstance(rasterBase, Ifg):
            d = {'type': 'Ifg', 'path': path}
        elif isinstance(rasterBase, Incidence):
            d = {'type': 'Incidence', 'path': path}
        else:
            raise luigi.parameter.UnknownParameterException('rasterBase must be an inscance DEM, Ifg or Incidence is valid')
        return super(RasterParam, self).serialize(d)
<|end_body_1|>

Solution:
class RasterParam:
    """Parameter representing a :py:class:`pyrate.shared.RasterBase` sub class."""

    def parse(self, string):
        """override of :py:meth:`DictParam.parse`."""
        dct = super(RasterParam, self).parse(string)
        raster_type = dct['type']
        path = dct['path']
        if raster_type == 'DEM':
            return DEM(path)
        elif raster_type == 'Ifg':
            return Ifg(path)
        elif raster_type == 'Incidence':
            return Incidence(path)
        else:
            raise luigi.parameter.UnknownParameterException('rasterBase must be an inscance DEM, Ifg or Incidence is valid')

    def serialize(self, rasterBase):
        """override of :py:meth:`DictParam.serialize`."""
        path = rasterBase.data_path
        if isinstance(rasterBase, DEM):
            d = {'type': 'DEM', 'path': path}
        elif isinstance(rasterBase, Ifg):
            d = {'type': 'Ifg', 'path': path}
        elif isinstance(rasterBase, Incidence):
            d = {'type': 'Incidence', 'path': path}
        else:
            raise luigi.parameter.UnknownParameterException('rasterBase must be an inscance DEM, Ifg or Incidence is valid')
        return super(RasterParam, self).serialize(d)


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def translateNum_1(self, num: int) -> int: 方法一：字符串遍历 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(N)： 字符串 s 使用 O(N) 大小的额外空间。 :param num: :return:
- def translateNum_2(self, num: int) -> int: 方法二：数字求余 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(1)： 几个变量使用常数大小的额外空间。 :param num: :return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def translateNum_1(self, num: int) -> int: 方法一：字符串遍历 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(N)： 字符串 s 使用 O(N) 大小的额外空间。 :param num: :return:
- def translateNum_2(self, num: int) -> int: 方法二：数字求余 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(1)： 几个变量使用常数大小的额外空间。 :param num: :return:

<|skeleton|>
class Solution:

    def translateNum_1(self, num: int) -> int:
        """方法一：字符串遍历 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(N)： 字符串 s 使用 O(N) 大小的额外空间。 :param num: :return:"""
        <|body_0|>

    def translateNum_2(self, num: int) -> int:
        """方法二：数字求余 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(1)： 几个变量使用常数大小的额外空间。 :param num: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        s = str(num)
        a = b = 1
        for i in range(2, len(s) + 1):
            a, b = (a + b if '10' <= s[i - 2:i] <= '25' else a, a)
        return a
<|end_body_0|>

<|body_start_1|>
        a = b = 1
        y = num % 10
        while num != 0:
            num //= 10
            x = num % 10
            a, b = (a + b if 10 <= 10 * x + y <= 25 else a, a)
            y = x
        return a
<|end_body_1|>

Solution:
class Solution:
    def translateNum_1(self, num: int) -> int:
        """方法一：字符串遍历 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(N)： 字符串 s 使用 O(N) 大小的额外空间。 :param num: :return:"""
        s = str(num)
        a = b = 1
        for i in range(2, len(s) + 1):
            a, b = (a + b if '10' <= s[i - 2:i] <= '25' else a, a)
        return a

    def translateNum_2(self, num: int) -> int:
        """方法二：数字求余 时间复杂度 O(N)： N 为字符串 s 的长度（即数字 num 的位数 log(num) ），其决定了循环次数。 空间复杂度 O(1)： 几个变量使用常数大小的额外空间。 :param num: :return:"""
        a = b = 1
        y = num % 10
        while num != 0:
            num //= 10
            x = num % 10
            a, b = (a + b if 10 <= 10 * x + y <= 25 else a, a)
            y = x
        return a


update

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

Class description:
Find neareast id for a embedding by using kd-tree

Method signatures and docstrings:
- def match(self, emb, top_matches=Config.Matcher.MAX_TOP_MATCHES, threshold=None, return_dists=False, always_return_closest=True): See superclass doc
- def fit(self, embs, labels): Fit current matcher to new embs and labels :param embs: list of embs :param labels: list of label (face id) for each emb
- def update(self, new_embs, new_labels): add new embs and labels to current matcher :param new_embs: list of embs :param new_labels: list of label (face id) for each emb

Implement the Python class `KdTreeMatcher` described below.

Class description:
Find neareast id for a embedding by using kd-tree

Method signatures and docstrings:
- def match(self, emb, top_matches=Config.Matcher.MAX_TOP_MATCHES, threshold=None, return_dists=False, always_return_closest=True): See superclass doc
- def fit(self, embs, labels): Fit current matcher to new embs and labels :param embs: list of embs :param labels: list of label (face id) for each emb
- def update(self, new_embs, new_labels): add new embs and labels to current matcher :param new_embs: list of embs :param new_labels: list of label (face id) for each emb

<|skeleton|>
class KdTreeMatcher:
    """Find neareast id for a embedding by using kd-tree"""

    def match(self, emb, top_matches=Config.Matcher.MAX_TOP_MATCHES, threshold=None, return_dists=False, always_return_closest=True):
        """See superclass doc"""
        <|body_0|>

    def fit(self, embs, labels):
        """Fit current matcher to new embs and labels :param embs: list of embs :param labels: list of label (face id) for each emb"""
        <|body_1|>

    def update(self, new_embs, new_labels):
        """add new embs and labels to current matcher :param new_embs: list of embs :param new_labels: list of label (face id) for each emb"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if threshold is None:
            threshold = self._threshold
        if self._classifier is not None:
            top_matches = min(top_matches, self._matcher_tup.length - 1)
            dists, inds = self._classifier.query(emb, k=top_matches)
            dists = np.squeeze(dists).tolist()
            inds = np.squeeze(inds).tolist()
            predict_id = self._matcher_tup.labels[inds[0]]
            min_dist = dists[0]
            if min_dist <= threshold:
                top_match_ids = [self._matcher_tup.labels[idx] for i, idx in enumerate(inds) if dists[i] <= threshold]
            else:
                top_match_ids = [predict_id]
            dists = dists[0:len(top_match_ids)]
        else:
            top_match_ids = [Config.Matcher.NEW_FACE]
            dists = [-1]
            predict_id = Config.Matcher.NEW_FACE
            min_dist = -1
        if return_dists:
            return (top_match_ids, dists)
        return top_match_ids
<|end_body_0|>

<|body_start_1|>
        length = len(embs)
        print('Fit', length)
        if length > 0:
            reg_mat = np.asarray(embs).reshape((length, Config.Matcher.EMB_LENGTH))
            self._classifier = neighbors.KDTree(reg_mat, leaf_size=Config.Matcher.INDEX_LEAF_SIZE, metric='euclidean')
            self._matcher_tup = KdTreeTuple(embs, labels, length)
        else:
            self._matcher_tup = None
            self._classifier = None
<|end_body_1|>

<|body_start_2|>
        if self._matcher_tup is None:
            self.fit(new_embs, new_labels)
        else:
            old_embs = self._matcher_tup.embs
            old_labels = self._matcher_tup.labels
            embs = old_embs + new_embs
            labels = old_labels + new_labels
            self.fit(embs, labels)
<|end_body_2|>

Solution:
class KdTreeMatcher:
    """Find neareast id for a embedding by using kd-tree"""

    def match(self, emb, top_matches=Config.Matcher.MAX_TOP_MATCHES, threshold=None, return_dists=False, always_return_closest=True):
        """See superclass doc"""
        if threshold is None:
            threshold = self._threshold
        if self._classifier is not None:
            top_matches = min(top_matches, self._matcher_tup.length - 1)
            dists, inds = self._classifier.query(emb, k=top_matches)
            dists = np.squeeze(dists).tolist()
            inds = np.squeeze(inds).tolist()
            predict_id = self._matcher_tup.labels[inds[0]]
            min_dist = dists[0]
            if min_dist <= threshold:
                top_match_ids = [self._matcher_tup.labels[idx] for i, idx in enumerate(inds) if dists[i] <= threshold]
            else:
                top_match_ids = [predict_id]
            dists = dists[0:len(top_match_ids)]
        else:
            top_match_ids = [Config.Matcher.NEW_FACE]
            dists = [-1]
            predict_id = Config.Matcher.NEW_FACE
            min_dist = -1
        if return_dists:
            return (top_match_ids, dists)
        return top_match_ids

    def fit(self, embs, labels):
        """Fit current matcher to new embs and labels :param embs: list of embs :param labels: list of label (face id) for each emb"""
        length = len(embs)
        print('Fit', length)
        if length > 0:
            reg_mat = np.asarray(embs).reshape((length, Config.Matcher.EMB_LENGTH))
            self._classifier = neighbors.KDTree(reg_mat, leaf_size=Config.Matcher.INDEX_LEAF_SIZE, metric='euclidean')
            self._matcher_tup = KdTreeTuple(embs, labels, length)
        else:
            self._matcher_tup = None
            self._classifier = None

    def update(self, new_embs, new_labels):
        """add new embs and labels to current matcher :param new_embs: list of embs :param new_labels: list of label (face id) for each emb"""
        if self._matcher_tup is None:
            self.fit(new_embs, new_labels)
        else:
            old_embs = self._matcher_tup.embs
            old_labels = self._matcher_tup.labels
            embs = old_embs + new_embs
            labels = old_labels + new_labels
            self.fit(embs, labels)


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

Class description:
OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte project uid (in little-endian bytes) * 8 byte unsigned long long (little-endian): JSON start location in file Following the header is a binary data blob. Following the binary is a UTF-8 encoded JSON dictionary containing all elements of the project keyed by UID string. Objects can reference each other by UID, and arrays and images contain pointers to their data in the binary blob.

Method signatures and docstrings:
- def __init__(self, project, fname): Project serialization is performed on OMFWriter init Binary data is written during project serialization
- def initialize_header(fopen, uid): Write magic number, version string, project uid, and zero bytes Total header length = 60 bytes 4 (magic number) + 32 (version) + 16 (uid in bytes) + 8 (JSON start, written later)
- def update_header(fopen): Return to header and write the correct JSON start location

Implement the Python class `OMFWriter` described below.

Class description:
OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte project uid (in little-endian bytes) * 8 byte unsigned long long (little-endian): JSON start location in file Following the header is a binary data blob. Following the binary is a UTF-8 encoded JSON dictionary containing all elements of the project keyed by UID string. Objects can reference each other by UID, and arrays and images contain pointers to their data in the binary blob.

Method signatures and docstrings:
- def __init__(self, project, fname): Project serialization is performed on OMFWriter init Binary data is written during project serialization
- def initialize_header(fopen, uid): Write magic number, version string, project uid, and zero bytes Total header length = 60 bytes 4 (magic number) + 32 (version) + 16 (uid in bytes) + 8 (JSON start, written later)
- def update_header(fopen): Return to header and write the correct JSON start location

<|skeleton|>
class OMFWriter:
    """OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte project uid (in little-endian bytes) * 8 byte unsigned long long (little-endian): JSON start location in file Following the header is a binary data blob. Following the binary is a UTF-8 encoded JSON dictionary containing all elements of the project keyed by UID string. Objects can reference each other by UID, and arrays and images contain pointers to their data in the binary blob."""

    def __init__(self, project, fname):
        """Project serialization is performed on OMFWriter init Binary data is written during project serialization"""
        <|body_0|>

    def initialize_header(fopen, uid):
        """Write magic number, version string, project uid, and zero bytes Total header length = 60 bytes 4 (magic number) + 32 (version) + 16 (uid in bytes) + 8 (JSON start, written later)"""
        <|body_1|>

    def update_header(fopen):
        """Return to header and write the correct JSON start location"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if len(fname) < 4 or fname[-4:] != '.omf':
            fname = fname + '.omf'
        self.fname = fname
        with open(fname, 'wb') as fopen:
            self.initialize_header(fopen, project.uid)
            self.project_json = project.serialize(open_file=fopen)
            self.update_header(fopen)
            fopen.write(json.dumps(self.project_json).encode('utf-8'))
<|end_body_0|>

<|body_start_1|>
        fopen.seek(0, 0)
        fopen.write(b'\x84\x83\x82\x81')
        fopen.write(struct.pack('<32s', COMPATIBILITY_VERSION.ljust(32, b'\x00')))
        fopen.write(struct.pack('<16s', uid.bytes))
        fopen.seek(8, 1)
<|end_body_1|>

<|body_start_2|>
        json_start = fopen.tell()
        fopen.seek(52, 0)
        fopen.write(struct.pack('<Q', json_start))
        fopen.seek(json_start)
<|end_body_2|>

Solution:
class OMFWriter:
    """OMFWriter serializes a OMF project to a file .. code:: proj = omf.project() ... omf.OMFWriter(proj, 'outfile.omf') The output file starts with a 60 byte header: * 4 byte magic number: :code:`b'\\x81\\x82\\x83\\x84'` * 32 byte version string: :code:`'OMF-v0.9.0'` (other bytes empty) * 16 byte project uid (in little-endian bytes) * 8 byte unsigned long long (little-endian): JSON start location in file Following the header is a binary data blob. Following the binary is a UTF-8 encoded JSON dictionary containing all elements of the project keyed by UID string. Objects can reference each other by UID, and arrays and images contain pointers to their data in the binary blob."""

    def __init__(self, project, fname):
        """Project serialization is performed on OMFWriter init Binary data is written during project serialization"""
        if len(fname) < 4 or fname[-4:] != '.omf':
            fname = fname + '.omf'
        self.fname = fname
        with open(fname, 'wb') as fopen:
            self.initialize_header(fopen, project.uid)
            self.project_json = project.serialize(open_file=fopen)
            self.update_header(fopen)
            fopen.write(json.dumps(self.project_json).encode('utf-8'))

    def initialize_header(fopen, uid):
        """Write magic number, version string, project uid, and zero bytes Total header length = 60 bytes 4 (magic number) + 32 (version) + 16 (uid in bytes) + 8 (JSON start, written later)"""
        fopen.seek(0, 0)
        fopen.write(b'\x84\x83\x82\x81')
        fopen.write(struct.pack('<32s', COMPATIBILITY_VERSION.ljust(32, b'\x00')))
        fopen.write(struct.pack('<16s', uid.bytes))
        fopen.seek(8, 1)

    def update_header(fopen):
        """Return to header and write the correct JSON start location"""
        json_start = fopen.tell()
        fopen.seek(52, 0)
        fopen.write(struct.pack('<Q', json_start))
        fopen.seek(json_start)


tokenize

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

Class description:
Word-level language model corpus.

Method signatures and docstrings:
- def __init__(self, path, thd=0): Initialization. Args: path: path to corpus location, the folder should include 'train.txt', 'valid.txt' and 'test.txt' thd: tokens that appears less then thd times in train.txt will be replaced by <unk>
- def tokenize(self, path, build_dict=False, thd=0): Tokenizes a text file.

Implement the Python class `Corpus` described below.

Class description:
Word-level language model corpus.

Method signatures and docstrings:
- def __init__(self, path, thd=0): Initialization. Args: path: path to corpus location, the folder should include 'train.txt', 'valid.txt' and 'test.txt' thd: tokens that appears less then thd times in train.txt will be replaced by <unk>
- def tokenize(self, path, build_dict=False, thd=0): Tokenizes a text file.

<|skeleton|>
class Corpus:
    """Word-level language model corpus."""

    def __init__(self, path, thd=0):
        """Initialization. Args: path: path to corpus location, the folder should include 'train.txt', 'valid.txt' and 'test.txt' thd: tokens that appears less then thd times in train.txt will be replaced by <unk>"""
        <|body_0|>

    def tokenize(self, path, build_dict=False, thd=0):
        """Tokenizes a text file."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not os.path.exists(path):
            os.mkdir(path)
        dict_file_name = os.path.join(path, 'dict.pkl')
        if os.path.exists(dict_file_name):
            print('Loading dictionary...')
            self.dictionary = pickle.load(open(dict_file_name, 'rb'))
            build_dict = False
        else:
            self.dictionary = Dictionary()
            build_dict = True
        train_path = os.path.join(path, 'train.txt')
        if not os.path.exists(train_path):
            wget.download(urls['train'], train_path)
        self.train = self.tokenize(train_path, build_dict=build_dict, thd=thd)
        valid_path = os.path.join(path, 'valid.txt')
        if not os.path.exists(valid_path):
            wget.download(urls['valid'], valid_path)
        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
        test_path = os.path.join(path, 'test.txt')
        if not os.path.exists(test_path):
            wget.download(urls['test'], test_path)
        self.test = self.tokenize(os.path.join(path, 'test.txt'))
        if build_dict:
            print('Saving dictionary...')
            dict_file_name = os.path.join(path, 'dict.pkl')
            pickle.dump(self.dictionary, open(dict_file_name, 'wb'))
<|end_body_0|>

<|body_start_1|>
        assert os.path.exists(path)
        if build_dict:
            with open(path, 'r') as f:
                for line in f:
                    words = line.split()
                    for word in words:
                        self.dictionary.add_word(word)
            if thd > 1:
                self.dictionary.rebuild_by_freq(thd)
        ids_list = []
        with open(path, 'r') as f:
            for line in f:
                words = line.split()
                ids = []
                for word in words:
                    ids.append(self.dictionary[word])
                ids_list.append(ids)
        return ids_list
<|end_body_1|>

Solution:
class Corpus:
    """Word-level language model corpus."""

    def __init__(self, path, thd=0):
        """Initialization. Args: path: path to corpus location, the folder should include 'train.txt', 'valid.txt' and 'test.txt' thd: tokens that appears less then thd times in train.txt will be replaced by <unk>"""
        if not os.path.exists(path):
            os.mkdir(path)
        dict_file_name = os.path.join(path, 'dict.pkl')
        if os.path.exists(dict_file_name):
            print('Loading dictionary...')
            self.dictionary = pickle.load(open(dict_file_name, 'rb'))
            build_dict = False
        else:
            self.dictionary = Dictionary()
            build_dict = True
        train_path = os.path.join(path, 'train.txt')
        if not os.path.exists(train_path):
            wget.download(urls['train'], train_path)
        self.train = self.tokenize(train_path, build_dict=build_dict, thd=thd)
        valid_path = os.path.join(path, 'valid.txt')
        if not os.path.exists(valid_path):
            wget.download(urls['valid'], valid_path)
        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
        test_path = os.path.join(path, 'test.txt')
        if not os.path.exists(test_path):
            wget.download(urls['test'], test_path)
        self.test = self.tokenize(os.path.join(path, 'test.txt'))
        if build_dict:
            print('Saving dictionary...')
            dict_file_name = os.path.join(path, 'dict.pkl')
            pickle.dump(self.dictionary, open(dict_file_name, 'wb'))

    def tokenize(self, path, build_dict=False, thd=0):
        """Tokenizes a text file."""
        assert os.path.exists(path)
        if build_dict:
            with open(path, 'r') as f:
                for line in f:
                    words = line.split()
                    for word in words:
                        self.dictionary.add_word(word)
            if thd > 1:
                self.dictionary.rebuild_by_freq(thd)
        ids_list = []
        with open(path, 'r') as f:
            for line in f:
                words = line.split()
                ids = []
                for word in words:
                    ids.append(self.dictionary[word])
                ids_list.append(ids)
        return ids_list


和上面用斐波那契数列方法本质是一样的 :param n: :return:

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def dynamic_method(self, n): 和上面用斐波那契数列方法本质是一样的 :param n: :return:
- def recursive_method(self, n): 这个方法超时了 :param n: :return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def dynamic_method(self, n): 和上面用斐波那契数列方法本质是一样的 :param n: :return:
- def recursive_method(self, n): 这个方法超时了 :param n: :return:

<|skeleton|>
class Solution:

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

    def dynamic_method(self, n):
        """和上面用斐波那契数列方法本质是一样的 :param n: :return:"""
        <|body_1|>

    def recursive_method(self, n):
        """这个方法超时了 :param n: :return:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if n == 1 or n == 0:
            return n
        a, b = (1, 2)
        for _ in range(2, n):
            tmp = a + b
            a = b
            b = tmp
        return b
<|end_body_0|>

<|body_start_1|>
        if n == 0 or n == 1:
            return n
        a = [1, 2]
        for i in range(2, n):
            a.append(a[i - 1] + a[i - 2])
        return a[-1]
<|end_body_1|>

<|body_start_2|>
        if n == 1 or n == 0:
            return n
        if n == 2:
            return 2
        return self.climbStairs(n - 1) + self.climbStairs(n - 2)
<|end_body_2|>

Solution:
class Solution:
    def climbStairs(self, n):
        """:type n: int :rtype: int"""
        if n == 1 or n == 0:
            return n
        a, b = (1, 2)
        for _ in range(2, n):
            tmp = a + b
            a = b
            b = tmp
        return b

    def dynamic_method(self, n):
        """和上面用斐波那契数列方法本质是一样的 :param n: :return:"""
        if n == 0 or n == 1:
            return n
        a = [1, 2]
        for i in range(2, n):
            a.append(a[i - 1] + a[i - 2])
        return a[-1]

    def recursive_method(self, n):
        """这个方法超时了 :param n: :return:"""
        if n == 1 or n == 0:
            return n
        if n == 2:
            return 2
        return self.climbStairs(n - 1) + self.climbStairs(n - 2)


testCompareNonWildcard

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

Class description:
Implement the WebTestExpectationUnittest class.

Method signatures and docstrings:
- def testCompareWildcard(self): Tests that wildcard comparisons work as expected.
- def testCompareNonWildcard(self): Tests that non-wildcard comparisons work as expected.

Implement the Python class `WebTestExpectationUnittest` described below.

Class description:
Implement the WebTestExpectationUnittest class.

Method signatures and docstrings:
- def testCompareWildcard(self): Tests that wildcard comparisons work as expected.
- def testCompareNonWildcard(self): Tests that non-wildcard comparisons work as expected.

<|skeleton|>
class WebTestExpectationUnittest:

    def testCompareWildcard(self):
        """Tests that wildcard comparisons work as expected."""
        <|body_0|>

    def testCompareNonWildcard(self):
        """Tests that non-wildcard comparisons work as expected."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        e = data_types.WebTestExpectation('test*', ['tag1'], 'Failure')
        self.assertTrue(e._CompareWildcard('testing123'))
        self.assertTrue(e._CompareWildcard('virtual/some-identifier/testing123'))
        self.assertTrue(e._CompareWildcard('test'))
        self.assertTrue(e._CompareWildcard('virtual/some-identifier/test'))
        self.assertFalse(e._CompareWildcard('tes'))
        self.assertFalse(e._CompareWildcard('/virtual/some-identifier/test'))
        self.assertFalse(e._CompareWildcard('virtual/some/malformed/test'))
<|end_body_0|>

<|body_start_1|>
        e = data_types.WebTestExpectation('test', ['tag1'], 'Failure')
        self.assertTrue(e._CompareNonWildcard('test'))
        self.assertTrue(e._CompareNonWildcard('virtual/some-identifier/test'))
        self.assertFalse(e._CompareNonWildcard('tes'))
        self.assertFalse(e._CompareNonWildcard('/virtual/some-identifier/test'))
        self.assertFalse(e._CompareNonWildcard('virtual/some/malformed/test'))
<|end_body_1|>

Solution:
class WebTestExpectationUnittest:
    def testCompareWildcard(self):
        """Tests that wildcard comparisons work as expected."""
        e = data_types.WebTestExpectation('test*', ['tag1'], 'Failure')
        self.assertTrue(e._CompareWildcard('testing123'))
        self.assertTrue(e._CompareWildcard('virtual/some-identifier/testing123'))
        self.assertTrue(e._CompareWildcard('test'))
        self.assertTrue(e._CompareWildcard('virtual/some-identifier/test'))
        self.assertFalse(e._CompareWildcard('tes'))
        self.assertFalse(e._CompareWildcard('/virtual/some-identifier/test'))
        self.assertFalse(e._CompareWildcard('virtual/some/malformed/test'))

    def testCompareNonWildcard(self):
        """Tests that non-wildcard comparisons work as expected."""
        e = data_types.WebTestExpectation('test', ['tag1'], 'Failure')
        self.assertTrue(e._CompareNonWildcard('test'))
        self.assertTrue(e._CompareNonWildcard('virtual/some-identifier/test'))
        self.assertFalse(e._CompareNonWildcard('tes'))
        self.assertFalse(e._CompareNonWildcard('/virtual/some-identifier/test'))
        self.assertFalse(e._CompareNonWildcard('virtual/some/malformed/test'))


forward

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

Class description:
This class implements the patch merging approach which is essential a strided convolution with normalization before

Method signatures and docstrings:
- def __init__(self, in_channels: int) -> None: Constructor method :param in_channels: (int) Number of input channels
- def forward(self, input: torch.Tensor) -> torch.Tensor: Forward pass :param input: (torch.Tensor) Input tensor of the shape [batch size, in channels, height, width] :return: (torch.Tensor) Output tensor of the shape [batch size, 2 * in channels, height // 2, width // 2]

Implement the Python class `PatchMerging` described below.

Class description:
This class implements the patch merging approach which is essential a strided convolution with normalization before

Method signatures and docstrings:
- def __init__(self, in_channels: int) -> None: Constructor method :param in_channels: (int) Number of input channels
- def forward(self, input: torch.Tensor) -> torch.Tensor: Forward pass :param input: (torch.Tensor) Input tensor of the shape [batch size, in channels, height, width] :return: (torch.Tensor) Output tensor of the shape [batch size, 2 * in channels, height // 2, width // 2]

<|skeleton|>
class PatchMerging:
    """This class implements the patch merging approach which is essential a strided convolution with normalization before"""

    def __init__(self, in_channels: int) -> None:
        """Constructor method :param in_channels: (int) Number of input channels"""
        <|body_0|>

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        """Forward pass :param input: (torch.Tensor) Input tensor of the shape [batch size, in channels, height, width] :return: (torch.Tensor) Output tensor of the shape [batch size, 2 * in channels, height // 2, width // 2]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(PatchMerging, self).__init__()
        self.normalization: nn.Module = nn.LayerNorm(normalized_shape=4 * in_channels)
        self.linear_mapping: nn.Module = nn.Linear(in_features=4 * in_channels, out_features=2 * in_channels, bias=False)
<|end_body_0|>

<|body_start_1|>
        batch_size, channels, height, width = input.shape
        input: torch.Tensor = bchw_to_bhwc(input)
        input: torch.Tensor = input.unfold(dimension=1, size=2, step=2).unfold(dimension=2, size=2, step=2)
        input: torch.Tensor = input.reshape(batch_size, input.shape[1], input.shape[2], -1)
        input: torch.Tensor = self.normalization(input)
        output: torch.Tensor = bhwc_to_bchw(self.linear_mapping(input))
        return output
<|end_body_1|>

Solution:
class PatchMerging:
    """This class implements the patch merging approach which is essential a strided convolution with normalization before"""

    def __init__(self, in_channels: int) -> None:
        """Constructor method :param in_channels: (int) Number of input channels"""
        super(PatchMerging, self).__init__()
        self.normalization: nn.Module = nn.LayerNorm(normalized_shape=4 * in_channels)
        self.linear_mapping: nn.Module = nn.Linear(in_features=4 * in_channels, out_features=2 * in_channels, bias=False)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        """Forward pass :param input: (torch.Tensor) Input tensor of the shape [batch size, in channels, height, width] :return: (torch.Tensor) Output tensor of the shape [batch size, 2 * in channels, height // 2, width // 2]"""
        batch_size, channels, height, width = input.shape
        input: torch.Tensor = bchw_to_bhwc(input)
        input: torch.Tensor = input.unfold(dimension=1, size=2, step=2).unfold(dimension=2, size=2, step=2)
        input: torch.Tensor = input.reshape(batch_size, input.shape[1], input.shape[2], -1)
        input: torch.Tensor = self.normalization(input)
        output: torch.Tensor = bhwc_to_bchw(self.linear_mapping(input))
        return output


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]: 减法思维
- def combinationSum2_1(self, candidates: List[int], target: int) -> List[List[int]]: 加法思维

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]: 减法思维
- def combinationSum2_1(self, candidates: List[int], target: int) -> List[List[int]]: 加法思维

<|skeleton|>
class Solution:

    def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:
        """减法思维"""
        <|body_0|>

    def combinationSum2_1(self, candidates: List[int], target: int) -> List[List[int]]:
        """加法思维"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        size, res, path = (len(candidates), [], [])
        candidates.sort()

        def dfs(candidates, begin, target, res, path):
            if target == 0:
                res.append(path[:])
                return
            for i in range(begin, size):
                if candidates[i] > target:
                    break
                if i > begin and candidates[i] == candidates[i - 1]:
                    continue
                path.append(candidates[i])
                dfs(candidates, i + 1, target - candidates[i], res, path)
                path.pop()
        dfs(candidates, 0, target, res, path)
        return res
<|end_body_0|>

<|body_start_1|>
        size, count, res, path = (len(candidates), 0, [], [])
        candidates.sort()

        def dfs(candidates, begin, count, res, path):
            if count == target:
                res.append(path[:])
                return
            for i in range(begin, size):
                if candidates[i] > target:
                    break
                if candidates[i] + count > target:
                    continue
                if i > begin and candidates[i] == candidates[i - 1]:
                    continue
                path.append(candidates[i])
                dfs(candidates, i + 1, sum(path), res, path)
                count -= candidates[i]
                path.pop()
        dfs(candidates, 0, count, res, path)
        return res
<|end_body_1|>

Solution:
class Solution:
    def combinationSum2(self, candidates: List[int], target: int) -> List[List[int]]:
        """减法思维"""
        size, res, path = (len(candidates), [], [])
        candidates.sort()

        def dfs(candidates, begin, target, res, path):
            if target == 0:
                res.append(path[:])
                return
            for i in range(begin, size):
                if candidates[i] > target:
                    break
                if i > begin and candidates[i] == candidates[i - 1]:
                    continue
                path.append(candidates[i])
                dfs(candidates, i + 1, target - candidates[i], res, path)
                path.pop()
        dfs(candidates, 0, target, res, path)
        return res

    def combinationSum2_1(self, candidates: List[int], target: int) -> List[List[int]]:
        """加法思维"""
        size, count, res, path = (len(candidates), 0, [], [])
        candidates.sort()

        def dfs(candidates, begin, count, res, path):
            if count == target:
                res.append(path[:])
                return
            for i in range(begin, size):
                if candidates[i] > target:
                    break
                if candidates[i] + count > target:
                    continue
                if i > begin and candidates[i] == candidates[i - 1]:
                    continue
                path.append(candidates[i])
                dfs(candidates, i + 1, sum(path), res, path)
                count -= candidates[i]
                path.pop()
        dfs(candidates, 0, count, res, path)
        return res