
"""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_						)						)
     }




"""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_						)


