



"""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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  ['''image_processor''', '''tokenizer''']
   A__		:	Tuple           =  '''BlipImageProcessor'''
   A__		:	str           =  ('''BertTokenizer''', '''BertTokenizerFast''')



   def __init__(       self    :	Any							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      False
     super().__init__(__lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      self.image_processor



   def __call__(       self    :	Optional[int]							,       __lowerCamelCase    :	ImageInput = None							,       __lowerCamelCase    :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Union[bool, str, PaddingStrategy] = False							,       __lowerCamelCase    :	Union[bool, str, TruncationStrategy] = None							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	int = 0							,       __lowerCamelCase    :	Optional[int] = None							,       __lowerCamelCase    :	Optional[bool] = None							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = False							,       __lowerCamelCase    :	bool = True							,       __lowerCamelCase    :	Optional[Union[str, TensorType]] = None							,       **__lowerCamelCase    :	List[str]							,       ):



     """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:
       _snake_case   	=      self.tokenizer
       _snake_case   	=      self.tokenizer(
           text=__lowerCamelCase							,       add_special_tokens=__lowerCamelCase							,       padding=__lowerCamelCase							,       truncation=__lowerCamelCase							,       max_length=__lowerCamelCase							,       stride=__lowerCamelCase							,       pad_to_multiple_of=__lowerCamelCase							,       return_attention_mask=__lowerCamelCase							,       return_overflowing_tokens=__lowerCamelCase							,       return_special_tokens_mask=__lowerCamelCase							,       return_offsets_mapping=__lowerCamelCase							,       return_token_type_ids=__lowerCamelCase							,       return_length=__lowerCamelCase							,       verbose=__lowerCamelCase							,       return_tensors=__lowerCamelCase							,       **__lowerCamelCase							,       )
       return text_encoding

     # add pixel_values
     _snake_case   	=      self.image_processor(__lowerCamelCase							,       return_tensors=__lowerCamelCase				)

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

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

     return encoding_image_processor



   def    __UpperCAmelCase    (       self    :	Dict							,       *__lowerCamelCase    :	List[str]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     return self.tokenizer.batch_decode(*__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       *__lowerCamelCase    :	Optional[Any]							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     return self.tokenizer.decode(*__lowerCamelCase							,       **__lowerCamelCase				)





   @property
   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      self.tokenizer.model_input_names
     _snake_case   	=      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)












def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		set()

       # Replace all the whitespace in our sentence
       A__		   =		input_str.replace(''' '''					,	''''''    )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()    )
       return len(UpperCamelCase__    ) == 26


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       A__		   =		[False] * 26
       for char in input_str:
              if char.islower():
                     A__		   =		True
              elif char.isupper():
                     A__		   =		True
       return all(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str = "The quick brown fox jumps over the lazy dog"					,	)->bool:
       return len({char for char in input_str.lower() if char.isalpha()}    ) == 26


def       UpperCamelCase__(			)->None:
       from timeit import timeit

       A__		   =		'''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'''
       print(timeit('''is_pangram()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_faster()'''					,	setup=UpperCamelCase__    )    )
       print(timeit('''is_pangram_fastest()'''					,	setup=UpperCamelCase__    )    )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()





