

"""simple docstring"""
import re
import string

import numpy as np

import datasets


_A							=  "\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"

_A							=  "\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"

_A							=  "\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,					_KWARGS_DESCRIPTION   )
class       lowerCamelCase	(       datasets.Metric   ):


  '''simple docstring'''







  def  _a      (self	):


        """simple docstring"""

        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  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase=None							,       _lowerCamelCase=False							,       _lowerCamelCase=False							,       _lowerCamelCase=False							,       ):


        """simple docstring"""

        if regexes_to_ignore is not None:
              for s in regexes_to_ignore:
                    UpperCAmelCase__			:							int							=    np.array([re.sub(_lowerCamelCase							,       """"""							,       _lowerCamelCase	) for x in predictions]	)
                    UpperCAmelCase__			:							Any							=    np.array([re.sub(_lowerCamelCase							,       """"""							,       _lowerCamelCase	) for x in references]	)
        else:
              UpperCAmelCase__			:							Any							=    np.asarray(_lowerCamelCase	)
              UpperCAmelCase__			:							int							=    np.asarray(_lowerCamelCase	)

        if ignore_case:
              UpperCAmelCase__			:							List[Any]							=    np.char.lower(_lowerCamelCase	)
              UpperCAmelCase__			:							Any							=    np.char.lower(_lowerCamelCase	)

        if ignore_punctuation:
              UpperCAmelCase__			:							Optional[int]							=    string.punctuation.maketrans(""""""							,       """"""							,       string.punctuation	)
              UpperCAmelCase__			:							List[Any]							=    np.char.translate(_lowerCamelCase							,       table=_lowerCamelCase	)
              UpperCAmelCase__			:							Tuple							=    np.char.translate(_lowerCamelCase							,       table=_lowerCamelCase	)

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

        UpperCAmelCase__			:							Tuple							=    predictions == references

        return {"exact_match": np.mean(_lowerCamelCase	) * 100}







import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     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(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis 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(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
