"""simple docstring"""
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      List[str]     					=  ["""image_processor""", """tokenizer"""]
    _lowerCAmelCase       :      List[str]     					=  """Pix2StructImageProcessor"""
    _lowerCAmelCase       :      List[Any]     					=  ("""T5Tokenizer""", """T5TokenizerFast""")

    def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     False
          super().__init__(lowerCAmelCase						,      lowerCAmelCase      )

    def __call__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = 20_48						,      lowerCAmelCase = 0						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      **lowerCAmelCase						,      ):






          """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 and not self.image_processor.is_vqa:
                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

          if not self.image_processor.is_vqa:
                # add pixel_values
                snake_case        =     self.image_processor(
                    lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      max_patches=lowerCAmelCase						,      **lowerCAmelCase      )
          else:
                # add pixel_values and bbox
                snake_case        =     self.image_processor(
                    lowerCAmelCase						,      return_tensors=lowerCAmelCase						,      max_patches=lowerCAmelCase						,      header_text=lowerCAmelCase						,      **lowerCAmelCase      )

          if text is not None and not self.image_processor.is_vqa:
                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						,      )

                if "attention_mask" in text_encoding:
                      snake_case        =     text_encoding.pop('attention_mask'      )
                if "input_ids" in text_encoding:
                      snake_case        =     text_encoding.pop('input_ids'      )
          else:
                snake_case        =     None

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

          return encoding_image_processor

    def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






          """simple docstring"""

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

    def 						snake_case	(			self						,      *lowerCAmelCase						,      **lowerCAmelCase      ):






          """simple docstring"""

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

    @property
    def 						snake_case	(			self      ):






          """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      )      )



"""simple docstring"""
def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			bool:





						"""simple docstring"""






						if not all(x.isalpha() for x in string   ):
												raise ValueError('String must only contain alphabetic characters.'   )

						snake_case        =     sorted(string.lower()   )
						return len(_UpperCamelCase   ) == len(set(_UpperCamelCase   )   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					input("Enter a string ").strip()

					SCREAMING_SNAKE_CASE__   =					is_isogram(input_str)
					print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")


