import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[int]:

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

        return match







    def 			UpperCamelCase__   (	self , __lowercase)       ->	int:

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

        __UpperCamelCase     :Optional[Any]			  =		[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)       ->	int:
        __UpperCamelCase     :Tuple			  =		Path('''./datasets''')
        __UpperCamelCase     :Tuple			  =		list(dataset_paths.absolute().glob('''**/*.py'''))

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







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :str			  =		Path('''./datasets''')
        __UpperCamelCase     :str			  =		list(dataset_paths.absolute().glob('''**/*.py'''))

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








'''simple docstring'''






import warnings

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






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = 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__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     int			):

   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:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer.model_input_names
   __lowercase							  = self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

