
'''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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							["""image_processor""", """tokenizer"""]
				lowerCAmelCase_     			=							"""Pix2StructImageProcessor"""
				lowerCAmelCase_     			=							("""T5Tokenizer""", """T5TokenizerFast""")







				def __init__(       self						,				A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     False
											super().__init__(A_						,				A_     )







				def __call__(       self						,				A_=None						,				A_ = None						,				A_ = True						,				A_ = False						,				A_ = None						,				A_ = None						,				A_ = 2048						,				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 and not self.image_processor.is_vqa:
																		UpperCamelCase   			=     self.tokenizer
																		UpperCamelCase   			=     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

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

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

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

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

											return encoding_image_processor







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				*A_						,				**A_     )->		List[str]:







											'''simple docstring'''






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







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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












def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  bool:
   if not all(x.isalpha() for x in string							):
      raise ValueError("""String must only contain alphabetic characters."""							)

   __UpperCamelCase	:			List[str]					=					sorted(string.lower()							)
   return len(__lowerCAmelCase							) == len(set(__lowerCAmelCase							)							)


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

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



