





from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}



class    _UpperCAmelCase							(		__lowerCamelCase     ):
      a						       =							'bert'
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=12      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=0      ,       a__="absolute"      ,       a__=True      ,       a__=None      ,       **a__      ,       ):
         super().__init__(pad_token_id=UpperCamelCase_      ,       **UpperCamelCase_       )

         A_     :				List[Any]			      =	vocab_size
         A_     :				str			      =	hidden_size
         A_     :				int			      =	num_hidden_layers
         A_     :				Any			      =	num_attention_heads
         A_     :				int			      =	hidden_act
         A_     :				List[str]			      =	intermediate_size
         A_     :				Any			      =	hidden_dropout_prob
         A_     :				Union[str, Any]			      =	attention_probs_dropout_prob
         A_     :				str			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[Any]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				int			      =	position_embedding_type
         A_     :				Dict			      =	use_cache
         A_     :				List[Any]			      =	classifier_dropout






class    _UpperCAmelCase							(		__lowerCamelCase     ):
      @property
      def 		_lowerCamelCase      (		self       ):
         if self.task == "multiple-choice":
            A_     :				Optional[int]			      =	{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
            A_     :				int			      =	{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
                 ("""token_type_ids""", dynamic_axis),
             ]       )













import datasets

from .evaluate import evaluate


_lowerCAmelCase 				=     """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

_lowerCAmelCase 				=     """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

_lowerCAmelCase 				=     """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION     )
class    _UpperCAmelCase							(		datasets.Metric     ):
      def 		_lowerCamelCase      (		self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
                 {
                     """predictions""": {"""id""": datasets.Value("""string"""       ), """prediction_text""": datasets.Value("""string"""       )},
                     """references""": {
                         """id""": datasets.Value("""string"""       ),
                         """answers""": datasets.features.Sequence(
                             {
                                 """text""": datasets.Value("""string"""       ),
                                 """answer_start""": datasets.Value("""int32"""       ),
                             }       ),
                     },
                 }       )      ,       codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]      ,       reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]      ,       )






      def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
         A_     :				Union[str, Any]			      =	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
         A_     :				Optional[Any]			      =	[
             {
                 """paragraphs""": [
                     {
                         """qas""": [
                             {
                                 """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                                 """id""": ref["""id"""],
                             }
                             for ref in references
                         ]
                     }
                 ]
             }
         ]
         A_     :				Union[str, Any]			      =	evaluate(dataset=a__      ,       predictions=a__       )
         return score






