'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE	:					int					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					int					=  {
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]      =						"longt5"
      __UpperCamelCase:  Optional[int]      =						["past_key_values"]
      __UpperCamelCase:  Optional[Any]      =						{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




      def __init__(	self						:    Tuple						, A						:    List[Any]=32128						, A						:    Dict=512						, A						:    Union[str, Any]=64						, A						:    List[Any]=2048						, A						:    Any=6						, A						:    List[str]=None						, A						:    Union[str, Any]=8						, A						:    int=127						, A						:    Any=16						, A						:    List[str]=32						, A						:    Tuple=128						, A						:    Optional[Any]=0.1						, A						:    Optional[int]=1E-6						, A						:    int=1.0						, A						:    Optional[int]="relu"						, A						:    Dict=True						, A						:    Union[str, Any]="local"						, A						:    str=True						, A						:    int=0						, A						:    Dict=1						, **A						:    Dict						, ):
             _UpperCAmelCase					:  List[str]											=   vocab_size
             _UpperCAmelCase					:  Optional[int]											=   d_model
             _UpperCAmelCase					:  List[Any]											=   d_kv
             _UpperCAmelCase					:  Any											=   d_ff
             _UpperCAmelCase					:  List[Any]											=   num_layers
             # default = symmetry
             _UpperCAmelCase					:  List[Any]											=   num_decoder_layers if num_decoder_layers is not None else self.num_layers
             _UpperCAmelCase					:  List[Any]											=   num_heads
             _UpperCAmelCase					:  str											=   local_radius
             _UpperCAmelCase					:  List[Any]											=   global_block_size
             _UpperCAmelCase					:  Dict											=   relative_attention_num_buckets
             _UpperCAmelCase					:  str											=   relative_attention_max_distance
             _UpperCAmelCase					:  Optional[Any]											=   dropout_rate
             _UpperCAmelCase					:  Optional[Any]											=   layer_norm_epsilon
             _UpperCAmelCase					:  List[str]											=   initializer_factor
             _UpperCAmelCase					:  str											=   feed_forward_proj
             _UpperCAmelCase					:  str											=   encoder_attention_type
             _UpperCAmelCase					:  Dict											=   use_cache

             _UpperCAmelCase					:  int											=   self.feed_forward_proj.split("-"	)
             _UpperCAmelCase					:  Any											=   act_info[-1]
             _UpperCAmelCase					:  Tuple											=   act_info[0] == "gated"

             if len(A	) > 1 and act_info[0] != "gated" or len(A	) > 2:
                    raise ValueError(
                        F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                        "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                        "'gated-gelu' or 'relu'"	)

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                    _UpperCAmelCase					:  int											=   "gelu_new"

             super().__init__(
                 pad_token_id=A						, eos_token_id=A						, is_encoder_decoder=A						, **A						, )

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      @property
      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Dict											=   {
                 "input_ids": {0: "batch", 1: "encoder_sequence"},
                 "attention_mask": {0: "batch", 1: "encoder_sequence"},
             }
             if self.use_past:
                    _UpperCAmelCase					:  Dict											=   "past_encoder_sequence + sequence"
                    _UpperCAmelCase					:  List[Any]											=   {0: "batch"}
                    _UpperCAmelCase					:  List[str]											=   {0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                    _UpperCAmelCase					:  List[str]											=   {0: "batch", 1: "decoder_sequence"}
                    _UpperCAmelCase					:  Dict											=   {0: "batch", 1: "decoder_sequence"}

             if self.use_past:
                    self.fill_with_past_key_values_(A						, direction="inputs"	)

             return common_inputs





      @property
      def 					_A     (	self						:    List[Any]	):
             return 13


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> 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])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             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 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

