




import warnings

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





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["image_processor", "tokenizer"]
 lowercase_							       =       "CLIPImageProcessor"
 lowercase_							       =       ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")



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

       SCREAMING_SNAKE_CASE_      		=     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__(_lowerCAmelCase							,							_lowerCAmelCase		)



 def __call__(				self    :						Tuple							,							_lowerCAmelCase    :						Optional[Any]=None							,							_lowerCAmelCase    :						Tuple=None							,							_lowerCAmelCase    :						Union[str, Any]=None							,							**_lowerCAmelCase    :						Optional[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:
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)

       if images is not None:
             SCREAMING_SNAKE_CASE_      		=     self.image_processor(_lowerCAmelCase							,							return_tensors=_lowerCAmelCase							,							**_lowerCAmelCase		)

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



 def  lowerCAmelCase_    (				self    :						str							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						Any		):
       return self.tokenizer.batch_decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							*_lowerCAmelCase    :						Union[str, Any]							,							**_lowerCAmelCase    :						str		):
       return self.tokenizer.decode(*_lowerCAmelCase							,							**_lowerCAmelCase		)




 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE_      		=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

SCREAMING_SNAKE_CASE			: int				   =					list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

SCREAMING_SNAKE_CASE			: Dict				   =					[file for file in filepaths if file != file.lower()]
if upper_files:
 print(F"{len(upper_files)} files contain uppercase characters:")
 print("\n".join(upper_files) + "\n")

SCREAMING_SNAKE_CASE			: List[Any]				   =					[file for file in filepaths if " " in file]
if space_files:
 print(F"{len(space_files)} files contain space characters:")
 print("\n".join(space_files) + "\n")

SCREAMING_SNAKE_CASE			: Any				   =					[file for file in filepaths if "-" in file]
if hyphen_files:
 print(F"{len(hyphen_files)} files contain hyphen characters:")
 print("\n".join(hyphen_files) + "\n")

SCREAMING_SNAKE_CASE			: str				   =					[file for file in filepaths if os.sep not in file]
if nodir_files:
 print(F"{len(nodir_files)} files are not in a directory:")
 print("\n".join(nodir_files) + "\n")

SCREAMING_SNAKE_CASE			: Tuple				   =					len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)


