code: 
  """simple docstring"""
  
  
  
  
  
  from sklearn.metrics import accuracy_score
  
  import datasets
  
  
  _a	 =							'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'
  
  
  _a	 =							'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'
  
  
  _a	 =							'\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,_KWARGS_DESCRIPTION   )
  class A_	(datasets.Metric   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION       ,     citation=_CITATION       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                   "references": datasets.Sequence(datasets.Value("int32"						)						),
               }
               if self.config_name == "multilabel"
               else {
                   "predictions": datasets.Value("int32"						),
                   "references": datasets.Value("int32"						),
               }						)       ,     reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]       ,     )
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_=True       ,     lowercase_=None						):
  
       """simple docstring"""
       return {
           "accuracy": float(
               accuracy_score(lowercase_       ,     lowercase_       ,     normalize=lowercase_       ,     sample_weight=lowercase_						)						)
       }
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  _a	 =							logging.get_logger(__name__)
  
  _a	 =							{
      'google/bigbird-pegasus-large-arxiv': (
          'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-pubmed': (
          'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
      ),
      'google/bigbird-pegasus-large-bigpatent': (
          'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
      ),
      # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
  }
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		Optional[int]						  =	"""bigbird_pegasus"""
    SCREAMING_SNAKE_CASE__    :		Any						  =	["""past_key_values"""]
    SCREAMING_SNAKE_CASE__    :		Tuple						  =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_=9_6103       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=16       ,     lowercase_=4096       ,     lowercase_=16       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=True       ,     lowercase_="gelu_new"       ,     lowercase_=1024       ,     lowercase_=0.1       ,     lowercase_=0.0       ,     lowercase_=0.0       ,     lowercase_=0.02       ,     lowercase_=2       ,     lowercase_=0.0       ,     lowercase_=True       ,     lowercase_=0       ,     lowercase_=2       ,     lowercase_=1       ,     lowercase_="block_sparse"       ,     lowercase_=64       ,     lowercase_=3       ,     lowercase_=False       ,     **lowercase_       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					vocab_size
       UpperCAmelCase_      :      Optional[Any]      =					max_position_embeddings
       UpperCAmelCase_      :      List[str]      =					d_model
       UpperCAmelCase_      :      str      =					encoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					encoder_layers
       UpperCAmelCase_      :      Optional[Any]      =					encoder_attention_heads
       UpperCAmelCase_      :      str      =					decoder_ffn_dim
       UpperCAmelCase_      :      Optional[Any]      =					decoder_layers
       UpperCAmelCase_      :      Optional[int]      =					decoder_attention_heads
       UpperCAmelCase_      :      Dict      =					dropout
       UpperCAmelCase_      :      Optional[int]      =					attention_dropout
       UpperCAmelCase_      :      Any      =					activation_dropout
       UpperCAmelCase_      :      str      =					activation_function
       UpperCAmelCase_      :      Tuple      =					init_std
       UpperCAmelCase_      :      Optional[int]      =					encoder_layerdrop
       UpperCAmelCase_      :      Union[str, Any]      =					decoder_layerdrop
       UpperCAmelCase_      :      int      =					classifier_dropout
       UpperCAmelCase_      :      Union[str, Any]      =					use_cache
       UpperCAmelCase_      :      List[str]      =					encoder_layers
       UpperCAmelCase_      :      str      =					scale_embedding  # scale factor will be sqrt(d_model) if True
  
       # extra config
       UpperCAmelCase_      :      List[Any]      =					attention_type
       UpperCAmelCase_      :      Any      =					block_size
       UpperCAmelCase_      :      str      =					num_random_blocks
       UpperCAmelCase_      :      Optional[Any]      =					use_bias
  
       super().__init__(
           pad_token_id=lowercase_       ,     bos_token_id=lowercase_       ,     eos_token_id=lowercase_       ,     is_encoder_decoder=lowercase_       ,     decoder_start_token_id=lowercase_       ,     **lowercase_       ,     )
  
  
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      List[str]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
  
          if self.use_past:
             UpperCAmelCase_      :      List[str]      =					{0: "batch"}
             UpperCAmelCase_      :      Any      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
             UpperCAmelCase_      :      List[str]      =					{0: "batch", 1: "decoder_sequence"}
             UpperCAmelCase_      :      Optional[Any]      =					{0: "batch", 1: "decoder_sequence"}
  
          if self.use_past:
             self.fill_with_past_key_values_(lowercase_       ,     direction="inputs"						)
       elif self.task == "causal-lm":
          # TODO: figure this case out.
          UpperCAmelCase_      :      str      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
              ]						)
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Tuple      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      List[str]      =					{0: "batch", 2: "past_sequence + sequence"}
       else:
          UpperCAmelCase_      :      Optional[Any]      =					OrderedDict(
              [
                  ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                  ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                  ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
              ]						)
  
       return common_inputs
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super().outputs
       else:
          UpperCAmelCase_      :      Optional[Any]      =					super(lowercase_       ,     self						).outputs
          if self.use_past:
             UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.num_layers
             for i in range(lowercase_						):
                UpperCAmelCase_      :      Any      =					{0: "batch", 2: "past_sequence + sequence"}
                UpperCAmelCase_      :      int      =					{0: "batch", 2: "past_sequence + sequence"}
       return common_outputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       # Generate decoder inputs
       UpperCAmelCase_      :      Union[str, Any]      =					seq_length if not self.use_past else 1
       UpperCAmelCase_      :      str      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       UpperCAmelCase_      :      List[Any]      =					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_      :      Optional[Any]      =					dict(**lowercase_       ,     **lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					common_inputs["input_ids"].shape
          UpperCAmelCase_      :      Union[str, Any]      =					common_inputs["decoder_input_ids"].shape[1]
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					self.num_attention_heads
          UpperCAmelCase_      :      Tuple      =					(
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          UpperCAmelCase_      :      Union[str, Any]      =					decoder_seq_length + 3
          UpperCAmelCase_      :      List[Any]      =					(
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )
  
          UpperCAmelCase_      :      List[str]      =					torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(lowercase_       ,     lowercase_						)]       ,     dim=1						)
  
          UpperCAmelCase_      :      Dict      =					[]
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					self.num_layers
          UpperCAmelCase_      :      List[str]      =					min(lowercase_       ,     lowercase_						)
          UpperCAmelCase_      :      Optional[int]      =					max(lowercase_       ,     lowercase_						) - min_num_layers
          UpperCAmelCase_      :      List[Any]      =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"
  
          for _ in range(lowercase_						):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                     torch.zeros(lowercase_						),
                 )						)
          # TODO: test this.
          UpperCAmelCase_      :      Optional[int]      =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(lowercase_       ,     lowercase_						):
             common_inputs["past_key_values"].append((torch.zeros(lowercase_						), torch.zeros(lowercase_						))						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
       if self.use_past:
          if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
          else:
             import torch
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					common_inputs["input_ids"].shape
          # Not using the same length for past_key_values
          UpperCAmelCase_      :      str      =					seqlen + 2
          UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.num_layers
          UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					self.num_attention_heads
          UpperCAmelCase_      :      Optional[int]      =					(
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
  
          UpperCAmelCase_      :      str      =					common_inputs["attention_mask"].dtype
          UpperCAmelCase_      :      Union[str, Any]      =					torch.cat(
              [common_inputs["attention_mask"], torch.ones(lowercase_       ,     lowercase_       ,     dtype=lowercase_						)]       ,     dim=1						)
          UpperCAmelCase_      :      List[str]      =					[
              (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
          ]
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase_      :      Any      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_batch       ,     num_token_to_add=0						)
  
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase_      :      List[str]      =					tokenizer.num_special_tokens_to_add(lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					compute_effective_axis_dimension(
           lowercase_       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowercase_						)
  
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_      :      str      =					[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_      :      int      =					dict(tokenizer(lowercase_       ,     return_tensors=lowercase_						)						)
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_ = -1       ,     lowercase_ = -1       ,     lowercase_ = False       ,     lowercase_ = None       ,     ):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Dict      =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       elif self.task == "causal-lm":
          UpperCAmelCase_      :      List[str]      =					self._generate_dummy_inputs_for_causal_lm(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
       else:
          UpperCAmelCase_      :      int      =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase_       ,     batch_size=lowercase_       ,     seq_length=lowercase_       ,     is_pair=lowercase_       ,     framework=lowercase_						)
  
       return common_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
          UpperCAmelCase_      :      Union[str, Any]      =					super()._flatten_past_key_values_(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
       else:
          UpperCAmelCase_      :      Dict      =					super(lowercase_       ,     self						)._flatten_past_key_values_(
              lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  