

import re
import string

import numpy as np

import datasets


_lowerCamelCase    :       str        =							"\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    :       List[str]        =							"\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[Any]        =							"\n"





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



   def 	__magic_name__   (							self							:					Optional[int]						):
     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 	__magic_name__   (							self							:					List[Any],		__A							:					Tuple,		__A							:					Dict,		__A							:					str=None,		__A							:					List[Any]=False,		__A							:					Tuple=False,		__A							:					Optional[Any]=False,		):
     if regexes_to_ignore is not None:
       for s in regexes_to_ignore:
         UpperCAmelCase	:							Optional[Any]						=			np.array([re.sub(__A,		'''''',		__A						) for x in predictions]						)
         UpperCAmelCase	:							Dict						=			np.array([re.sub(__A,		'''''',		__A						) for x in references]						)
     else:
       UpperCAmelCase	:							Dict						=			np.asarray(__A						)
       UpperCAmelCase	:							int						=			np.asarray(__A						)

     if ignore_case:
       UpperCAmelCase	:							Tuple						=			np.char.lower(__A						)
       UpperCAmelCase	:							Tuple						=			np.char.lower(__A						)

     if ignore_punctuation:
       UpperCAmelCase	:							str						=			string.punctuation.maketrans('''''',		'''''',		string.punctuation						)
       UpperCAmelCase	:							Any						=			np.char.translate(__A,		table=__A						)
       UpperCAmelCase	:							Any						=			np.char.translate(__A,		table=__A						)

     if ignore_numbers:
       UpperCAmelCase	:							List[str]						=			string.digits.maketrans('''''',		'''''',		string.digits						)
       UpperCAmelCase	:							Optional[Any]						=			np.char.translate(__A,		table=__A						)
       UpperCAmelCase	:							List[Any]						=			np.char.translate(__A,		table=__A						)

     UpperCAmelCase	:							List[Any]						=			predictions == references

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








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





