


'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase	:						int       =	{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VanForImageClassification',
	    'VanModel',
	    'VanPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						Optional[int]       =	_LazyModule(__name__, globals()['__file__'], _import_structure)







'''simple docstring'''



import re
import string

import numpy as np

import datasets


__lowercase	:						Tuple       =	'\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'

__lowercase	:						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'

__lowercase	:						Any       =	'\n'





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




	def       __UpperCAmelCase							(   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       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=None						,				__a=False						,				__a=False						,				__a=False						,				):

						'''simple docstring'''





						if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
																__a			:       Tuple					    =  np.array([re.sub(__a						,				''						,				__a							) for x in predictions]							)
																__a			:       List[Any]					    =  np.array([re.sub(__a						,				''						,				__a							) for x in references]							)
						else:
											__a			:       int					    =  np.asarray(__a							)
											__a			:       str					    =  np.asarray(__a							)

						if ignore_case:
											__a			:       Dict					    =  np.char.lower(__a							)
											__a			:       List[str]					    =  np.char.lower(__a							)

						if ignore_punctuation:
											__a			:       Dict					    =  string.punctuation.maketrans(''						,				''						,				string.punctuation							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Dict					    =  np.char.translate(__a						,				table=__a							)

						if ignore_numbers:
											__a			:       Optional[int]					    =  string.digits.maketrans(''						,				''						,				string.digits							)
											__a			:       Tuple					    =  np.char.translate(__a						,				table=__a							)
											__a			:       Optional[int]					    =  np.char.translate(__a						,				table=__a							)

						__a			:       Any					    =  predictions == references

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



