





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class       __lowercase      (	SCREAMING_SNAKE_CASE_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["image_processor", "tokenizer"]
     SCREAMING_SNAKE_CASE   						=				"BlipImageProcessor"
     SCREAMING_SNAKE_CASE   						=				"AutoTokenizer"




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


         """simple docstring"""



         super().__init__(UpperCamelCase__ ,	UpperCamelCase__				)

         # add QFormer tokenizer
         __A         =	qformer_tokenizer




     def __call__(				self       :			Dict ,	UpperCamelCase_       :			Optional[Any] = None ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			str = True ,	UpperCamelCase_       :			int = False ,	UpperCamelCase_       :			int = None ,	UpperCamelCase_       :			Dict = None ,	UpperCamelCase_       :			Dict = 0 ,	UpperCamelCase_       :			int = None ,	UpperCamelCase_       :			Any = None ,	UpperCamelCase_       :			Any = False ,	UpperCamelCase_       :			Tuple = False ,	UpperCamelCase_       :			Union[str, Any] = False ,	UpperCamelCase_       :			Optional[Any] = False ,	UpperCamelCase_       :			str = False ,	UpperCamelCase_       :			Any = True ,	UpperCamelCase_       :			Dict = None ,	**UpperCamelCase_       :			str ,	):


         """simple docstring"""



         if images is None and text is None:
             raise ValueError("""You have to specify at least images or text."""				)

         __A         =	BatchFeature()

         if text is not None:
             __A         =	self.tokenizer(
                 text=UpperCamelCase__ ,	add_special_tokens=UpperCamelCase__ ,	padding=UpperCamelCase__ ,	truncation=UpperCamelCase__ ,	max_length=UpperCamelCase__ ,	stride=UpperCamelCase__ ,	pad_to_multiple_of=UpperCamelCase__ ,	return_attention_mask=UpperCamelCase__ ,	return_overflowing_tokens=UpperCamelCase__ ,	return_special_tokens_mask=UpperCamelCase__ ,	return_offsets_mapping=UpperCamelCase__ ,	return_token_type_ids=UpperCamelCase__ ,	return_length=UpperCamelCase__ ,	verbose=UpperCamelCase__ ,	return_tensors=UpperCamelCase__ ,	**UpperCamelCase__ ,	)
             encoding.update(UpperCamelCase__				)
             __A         =	self.qformer_tokenizer(
                 text=UpperCamelCase__ ,	add_special_tokens=UpperCamelCase__ ,	padding=UpperCamelCase__ ,	truncation=UpperCamelCase__ ,	max_length=UpperCamelCase__ ,	stride=UpperCamelCase__ ,	pad_to_multiple_of=UpperCamelCase__ ,	return_attention_mask=UpperCamelCase__ ,	return_overflowing_tokens=UpperCamelCase__ ,	return_special_tokens_mask=UpperCamelCase__ ,	return_offsets_mapping=UpperCamelCase__ ,	return_token_type_ids=UpperCamelCase__ ,	return_length=UpperCamelCase__ ,	verbose=UpperCamelCase__ ,	return_tensors=UpperCamelCase__ ,	**UpperCamelCase__ ,	)
             __A         =	qformer_text_encoding.pop("""input_ids"""				)
             __A         =	qformer_text_encoding.pop("""attention_mask"""				)

         if images is not None:
             __A         =	self.image_processor(UpperCamelCase__ ,	return_tensors=UpperCamelCase__				)
             encoding.update(UpperCamelCase__				)

         return encoding




     def 		lowerCAmelCase_     (				self       :			List[str] ,	*UpperCamelCase_       :			List[Any] ,	**UpperCamelCase_       :			str				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	*UpperCamelCase_       :			int ,	**UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



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




     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any] ,	**UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         if os.path.isfile(UpperCamelCase__				):
             raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"				)
         os.makedirs(UpperCamelCase__ ,	exist_ok=UpperCamelCase__				)
         __A         =	os.path.join(UpperCamelCase__ ,	"""qformer_tokenizer"""				)
         self.qformer_tokenizer.save_pretrained(UpperCamelCase__				)
         return super().save_pretrained(UpperCamelCase__ ,	**UpperCamelCase__				)





     @classmethod
     def 		lowerCAmelCase_     (				cls       :			Dict ,	UpperCamelCase_       :			Any ,	**UpperCamelCase_       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	AutoTokenizer.from_pretrained(UpperCamelCase__ ,	subfolder="""qformer_tokenizer"""				)
         __A         =	cls._get_arguments_from_pretrained(UpperCamelCase__ ,	**UpperCamelCase__				)
         args.append(UpperCamelCase__				)
         return cls(*UpperCamelCase__				)












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




