


'''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 						_snake_case   (       a__		):
					lowerCAmelCase :Tuple						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :List[str]						=   '''Pix2StructImageProcessor'''
					lowerCAmelCase :str						=   ('''T5Tokenizer''', '''T5TokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       False
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase=None							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 2048							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								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__					:   Optional[int]       				=       self.tokenizer
											UpperCAmelCase__					:   Tuple       				=       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
											UpperCAmelCase__					:   str       				=       self.image_processor(
											    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							max_patches=_lowerCamelCase							,							**_lowerCamelCase)
								else:
											# add pixel_values and bbox
											UpperCAmelCase__					:   Union[str, Any]       				=       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:
											UpperCAmelCase__					:   Optional[Any]       				=       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:
														UpperCAmelCase__					:   Optional[Any]       				=       text_encoding.pop("""attention_mask""")
											if "input_ids" in text_encoding:
														UpperCAmelCase__					:   Union[str, Any]       				=       text_encoding.pop("""input_ids""")
								else:
											UpperCAmelCase__					:   List[str]       				=       None

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

								return encoding_image_processor




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   int       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__ = 1_0	, UpperCamelCase__ = 2_2   ):
			UpperCAmelCase__					:   List[str]       				=       range(1	, UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       range(1	, UpperCamelCase__   )
			return sum(
			    1 for power in powers for base in bases if len(str(base**power   )   ) == power   )


if __name__ == "__main__":
		print(f"""{solution(10, 22) = }""")