






import re
import string

import numpy as np

import datasets


__lowerCamelCase	:		List[Any]										=   "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

__lowerCamelCase	:		int										=   "\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

__lowerCamelCase	:		List[str]										=   "\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""string"""				,			id="""sequence"""   ),
                   """references""": datasets.Value("""string"""				,			id="""sequence"""   ),
               }   )				,			reference_urls=[]				,			)



      def 						lowercase__					(     self				,			a				,			a				,			a=None				,			a=False				,			a=False				,			a=False				,			):




       if regexes_to_ignore is not None:
        for s in regexes_to_ignore:
         snake_case__  :    List[Any]							       =np.array([re.sub(lowerCamelCase_				,			""""""				,			lowerCamelCase_   ) for x in predictions]   )
         snake_case__  :    Any							       =np.array([re.sub(lowerCamelCase_				,			""""""				,			lowerCamelCase_   ) for x in references]   )
       else:
        snake_case__  :    Dict							       =np.asarray(lowerCamelCase_   )
        snake_case__  :    Optional[Any]							       =np.asarray(lowerCamelCase_   )

       if ignore_case:
        snake_case__  :    List[Any]							       =np.char.lower(lowerCamelCase_   )
        snake_case__  :    List[str]							       =np.char.lower(lowerCamelCase_   )

       if ignore_punctuation:
        snake_case__  :    Optional[Any]							       =string.punctuation.maketrans(""""""				,			""""""				,			string.punctuation   )
        snake_case__  :    Union[str, Any]							       =np.char.translate(lowerCamelCase_				,			table=lowerCamelCase_   )
        snake_case__  :    str							       =np.char.translate(lowerCamelCase_				,			table=lowerCamelCase_   )

       if ignore_numbers:
        snake_case__  :    str							       =string.digits.maketrans(""""""				,			""""""				,			string.digits   )
        snake_case__  :    Optional[Any]							       =np.char.translate(lowerCamelCase_				,			table=lowerCamelCase_   )
        snake_case__  :    Optional[int]							       =np.char.translate(lowerCamelCase_				,			table=lowerCamelCase_   )

       snake_case__  :    int							       =predictions == references

       return {"exact_match": np.mean(lowerCamelCase_   ) * 1_0_0}













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"	)
 model.save_pretrained(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--big_bird_config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained BERT model. \n"""
               """This specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





