
"""simple docstring"""






import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE							=     '\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'

__SCREAMING_SNAKE_CASE							=     '\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'

__SCREAMING_SNAKE_CASE							=     '\n'





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



     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''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  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :str=None      ,  _lowerCamelCase :Tuple=False      ,  _lowerCamelCase :Dict=False      ,  _lowerCamelCase :Dict=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_     :					int   	=np.array([re.sub(_lowerCamelCase      ,  ''      ,  _lowerCamelCase   ) for x in references]   )
      else:
       UpperCamelCase_     :					int   	=np.asarray(_lowerCamelCase   )
       UpperCamelCase_     :					str   	=np.asarray(_lowerCamelCase   )

      if ignore_case:
       UpperCamelCase_     :					List[str]   	=np.char.lower(_lowerCamelCase   )
       UpperCamelCase_     :					int   	=np.char.lower(_lowerCamelCase   )

      if ignore_punctuation:
       UpperCamelCase_     :					List[Any]   	=string.punctuation.maketrans(''      ,  ''      ,  string.punctuation   )
       UpperCamelCase_     :					List[str]   	=np.char.translate(_lowerCamelCase      ,  table=_lowerCamelCase   )
       UpperCamelCase_     :					Tuple   	=np.char.translate(_lowerCamelCase      ,  table=_lowerCamelCase   )

      if ignore_numbers:
       UpperCamelCase_     :					Dict   	=string.digits.maketrans(''      ,  ''      ,  string.digits   )
       UpperCamelCase_     :					str   	=np.char.translate(_lowerCamelCase      ,  table=_lowerCamelCase   )
       UpperCamelCase_     :					Dict   	=np.char.translate(_lowerCamelCase      ,  table=_lowerCamelCase   )

      UpperCamelCase_     :					Optional[Any]   	=predictions == references

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


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MegaForCausalLM',
     'MegaForMaskedLM',
     'MegaForMultipleChoice',
     'MegaForQuestionAnswering',
     'MegaForSequenceClassification',
     'MegaForTokenClassification',
     'MegaModel',
     'MegaPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mega import (
      MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MegaForCausalLM,
      MegaForMaskedLM,
      MegaForMultipleChoice,
      MegaForQuestionAnswering,
      MegaForSequenceClassification,
      MegaForTokenClassification,
      MegaModel,
      MegaPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
