





import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class        _lowerCAmelCase    (			__a							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    with open(A__      ,				encoding='utf-8'							) as input_file:
      _snake_case   :				Dict														=	re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'							)
      _snake_case   :				Any														=	input_file.read()
      _snake_case   :				List[str]														=	regexp.search(A__							)

    return match







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					int							):


    '''simple docstring'''





    with open(A__      ,				encoding='utf-8'							) as input_file:
      _snake_case   :				Optional[Any]														=	re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'      ,				re.DOTALL							)
      _snake_case   :				int														=	input_file.read()
      # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
      _snake_case   :				Optional[int]														=	regexp.finditer(A__							)

    _snake_case   :				int														=	[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	Path('./datasets'							)
    _snake_case   :				Any														=	list(dataset_paths.absolute().glob('**/*.py'							)							)

    for dataset in dataset_files:
      if self._no_encoding_on_file_open(str(A__							)							):
        raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	Path('./datasets'							)
    _snake_case   :				Any														=	list(dataset_paths.absolute().glob('**/*.py'							)							)

    for dataset in dataset_files:
      if self._no_print_statements(str(A__							)							):
        raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				int														=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'      ,				UpperCamelCase      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	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__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''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:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				Optional[int]														=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase							)      ,				tensor_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.tokenizer.model_input_names
    _snake_case   :				List[Any]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




