
"""simple docstring"""




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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		['''image_processor''', '''tokenizer''']
       UpperCamelCase_ =		'''BlipImageProcessor'''
       UpperCamelCase_ =		'''AutoTokenizer'''
       def __init__(  self			,					A_			,					A_							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  False
         super().__init__(A_			,					A_							)
         _lowercase:      Optional[Any]   			=  self.image_processor
       def __call__(  self			,					A_ = None			,					A_ = None			,					A_ = True			,					A_ = False			,					A_ = None			,					A_ = None			,					A_ = 0			,					A_ = None			,					A_ = None			,					A_ = False			,					A_ = False			,					A_ = False			,					A_ = False			,					A_ = False			,					A_ = True			,					A_ = None			,					**A_			,					)  ->						BatchEncoding:





         """simple docstring"""




         if images is None and text is None:
           raise ValueError('''You have to specify either images or text.'''							)

         # Get only text
         if images is None:
           _lowercase:      Optional[Any]   			=  self.tokenizer
           _lowercase:      str   			=  self.tokenizer(
               text=A_			,					add_special_tokens=A_			,					padding=A_			,					truncation=A_			,					max_length=A_			,					stride=A_			,					pad_to_multiple_of=A_			,					return_attention_mask=A_			,					return_overflowing_tokens=A_			,					return_special_tokens_mask=A_			,					return_offsets_mapping=A_			,					return_token_type_ids=A_			,					return_length=A_			,					verbose=A_			,					return_tensors=A_			,					**A_			,					)
           return text_encoding

         # add pixel_values
         _lowercase:      Tuple   			=  self.image_processor(A_			,					return_tensors=A_							)

         if text is not None:
           _lowercase:      Any   			=  self.tokenizer(
               text=A_			,					add_special_tokens=A_			,					padding=A_			,					truncation=A_			,					max_length=A_			,					stride=A_			,					pad_to_multiple_of=A_			,					return_attention_mask=A_			,					return_overflowing_tokens=A_			,					return_special_tokens_mask=A_			,					return_offsets_mapping=A_			,					return_token_type_ids=A_			,					return_length=A_			,					verbose=A_			,					return_tensors=A_			,					**A_			,					)
         else:
           _lowercase:      Any   			=  None

         if text_encoding is not None:
           encoding_image_processor.update(A_							)

         return encoding_image_processor
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						str:





         """simple docstring"""




         return self.tokenizer.batch_decode(*A_			,					**A_							)
       def 				lowercase_				(  self			,					*A_			,					**A_							)  ->						Any:





         """simple docstring"""




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



       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      int   			=  self.tokenizer.model_input_names
         _lowercase:      Optional[Any]   			=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)


"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
