
"""simple docstring"""




import re
import string

import numpy as np

import datasets


lowerCAmelCase__				=					'\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__				=					'\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__				=					'\n'





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

							def _lowercase       (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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							):

									"""simple docstring"""







									if regexes_to_ignore is not None:
											for s in regexes_to_ignore:
													SCREAMING_SNAKE_CASE_    			=			np.array([re.sub(SCREAMING_SNAKE_CASE_							,							''''''							,							SCREAMING_SNAKE_CASE_     ) for x in predictions]     )
													SCREAMING_SNAKE_CASE_    			=			np.array([re.sub(SCREAMING_SNAKE_CASE_							,							''''''							,							SCREAMING_SNAKE_CASE_     ) for x in references]     )
									else:
											SCREAMING_SNAKE_CASE_    			=			np.asarray(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			np.asarray(SCREAMING_SNAKE_CASE_     )

									if ignore_case:
											SCREAMING_SNAKE_CASE_    			=			np.char.lower(SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			np.char.lower(SCREAMING_SNAKE_CASE_     )

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

									if ignore_numbers:
											SCREAMING_SNAKE_CASE_    			=			string.digits.maketrans(''''''							,							''''''							,							string.digits     )
											SCREAMING_SNAKE_CASE_    			=			np.char.translate(SCREAMING_SNAKE_CASE_							,							table=SCREAMING_SNAKE_CASE_     )
											SCREAMING_SNAKE_CASE_    			=			np.char.translate(SCREAMING_SNAKE_CASE_							,							table=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			predictions == references

									return {"exact_match": np.mean(SCREAMING_SNAKE_CASE_     ) * 1_00}



import inspect
import unittest






class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											try:
																	import diffusers  # noqa: F401
											except ImportError:
																	assert False






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Any:




											'''simple docstring'''

											import diffusers
											from diffusers.dependency_versions_table import deps

											__snake_case				=		inspect.getmembers(SCREAMING_SNAKE_CASE  ,     inspect.isclass    )

											for cls_name, cls_module in all_classes:
																	if "dummy_" in cls_module.__module__:
																							for backend in cls_module._backends:
																													if backend == "k_diffusion":
																																			__snake_case				=		"k-diffusion"
																													elif backend == "invisible_watermark":
																																			__snake_case				=		"invisible-watermark"
																													assert backend in deps, f'''{backend} is not in the deps table!'''





