



'''simple docstring'''





import re
import string

import numpy as np

import datasets


__a          =		'''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

__a          =		'''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

__a          =		'''
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION	)
class     UpperCAmelCase_       (       datasets.Metric	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     List[str]						):
          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					:     str ,     snake_case_					:     List[Any] ,     snake_case_					:     int ,     snake_case_					:     Any=None ,     snake_case_					:     Any=False ,     snake_case_					:     Optional[Any]=False ,     snake_case_					:     List[Any]=False ,     ):
          if regexes_to_ignore is not None:
                for s in regexes_to_ignore:
                      snake_case__  :    Dict    						=      np.array([re.sub(UpperCamelCase__ ,     """""" ,     UpperCamelCase__						) for x in predictions]						)
                      snake_case__  :    Union[str, Any]    						=      np.array([re.sub(UpperCamelCase__ ,     """""" ,     UpperCamelCase__						) for x in references]						)
          else:
                snake_case__  :    int    						=      np.asarray(UpperCamelCase__						)
                snake_case__  :    Tuple    						=      np.asarray(UpperCamelCase__						)

          if ignore_case:
                snake_case__  :    Union[str, Any]    						=      np.char.lower(UpperCamelCase__						)
                snake_case__  :    Tuple    						=      np.char.lower(UpperCamelCase__						)

          if ignore_punctuation:
                snake_case__  :    Dict    						=      string.punctuation.maketrans("""""" ,     """""" ,     string.punctuation						)
                snake_case__  :    Union[str, Any]    						=      np.char.translate(UpperCamelCase__ ,     table=UpperCamelCase__						)
                snake_case__  :    Any    						=      np.char.translate(UpperCamelCase__ ,     table=UpperCamelCase__						)

          if ignore_numbers:
                snake_case__  :    int    						=      string.digits.maketrans("""""" ,     """""" ,     string.digits						)
                snake_case__  :    Optional[int]    						=      np.char.translate(UpperCamelCase__ ,     table=UpperCamelCase__						)
                snake_case__  :    Dict    						=      np.char.translate(UpperCamelCase__ ,     table=UpperCamelCase__						)

          snake_case__  :    Optional[Any]    						=      predictions == references

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








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			_A	:			Optional[Any]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

