






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     a_						(      UpperCAmelCase__    ):
  lowercase							=				['image_processor', 'tokenizer']
  lowercase							=				'FlavaImageProcessor'
  lowercase							=				('BertTokenizer', 'BertTokenizerFast')

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=None      ,		**_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)
              UpperCamelCase      = kwargs.pop("""feature_extractor"""				)

        UpperCamelCase      = image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`."""				)
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`."""				)

        super().__init__(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.image_processor

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = False      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        if text is None and images is None:
              raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

        if text is not None:
              UpperCamelCase      = self.tokenizer(
                  text=_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE      ,		padding=_SCREAMING_SNAKE_CASE      ,		truncation=_SCREAMING_SNAKE_CASE      ,		max_length=_SCREAMING_SNAKE_CASE      ,		stride=_SCREAMING_SNAKE_CASE      ,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,		return_token_type_ids=_SCREAMING_SNAKE_CASE      ,		return_attention_mask=_SCREAMING_SNAKE_CASE      ,		return_overflowing_tokens=_SCREAMING_SNAKE_CASE      ,		return_special_tokens_mask=_SCREAMING_SNAKE_CASE      ,		return_offsets_mapping=_SCREAMING_SNAKE_CASE      ,		return_length=_SCREAMING_SNAKE_CASE      ,		verbose=_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)
        if images is not None:
              UpperCamelCase      = self.image_processor(
                  _SCREAMING_SNAKE_CASE      ,		return_image_mask=_SCREAMING_SNAKE_CASE      ,		return_codebook_pixels=_SCREAMING_SNAKE_CASE      ,		return_tensors=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        if text is not None and images is not None:
              encoding.update(_SCREAMING_SNAKE_CASE				)
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE				)      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.tokenizer.model_input_names
        UpperCamelCase      = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)
        return self.image_processor_class


  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)
        return self.image_processor








'''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   A__						(				self				)  ->				Optional[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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		)  ->				List[Any]:



        """simple docstring"""






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

        if ignore_case:
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)

        if ignore_punctuation:
              UpperCamelCase      = string.punctuation.maketrans(""""""      ,		""""""      ,		string.punctuation				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)

        if ignore_numbers:
              UpperCamelCase      = string.digits.maketrans(""""""      ,		""""""      ,		string.digits				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = predictions == references

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