



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 				__A					( A_      ):
			UpperCamelCase					:int     =			['''image_processor''', '''tokenizer''']
			UpperCamelCase					:Optional[int]     =			'''Pix2StructImageProcessor'''
			UpperCamelCase					:Tuple     =			('''T5Tokenizer''', '''T5TokenizerFast''')





			def __init__(self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		False
						super().__init__(__magic_name__							,    __magic_name__				)





			def __call__(self							,    __magic_name__=None							,    __magic_name__ = None							,    __magic_name__ = True							,    __magic_name__ = False							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = 2048							,    __magic_name__ = 0							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = False							,    __magic_name__ = True							,    __magic_name__ = None							,    **__magic_name__							,    ):
						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:
									lowerCamelCase__ :      Optional[Any]								=		self.tokenizer
									lowerCamelCase__ :      Dict								=		self.tokenizer(
									    text=__magic_name__							,    add_special_tokens=__magic_name__							,    padding=__magic_name__							,    truncation=__magic_name__							,    max_length=__magic_name__							,    stride=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    return_attention_mask=__magic_name__							,    return_overflowing_tokens=__magic_name__							,    return_special_tokens_mask=__magic_name__							,    return_offsets_mapping=__magic_name__							,    return_token_type_ids=__magic_name__							,    return_length=__magic_name__							,    verbose=__magic_name__							,    return_tensors=__magic_name__							,    **__magic_name__							,    )
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									lowerCamelCase__ :      Any								=		self.image_processor(
									    __magic_name__							,    return_tensors=__magic_name__							,    max_patches=__magic_name__							,    **__magic_name__				)
						else:
									# add pixel_values and bbox
									lowerCamelCase__ :      Dict								=		self.image_processor(
									    __magic_name__							,    return_tensors=__magic_name__							,    max_patches=__magic_name__							,    header_text=__magic_name__							,    **__magic_name__				)

						if text is not None and not self.image_processor.is_vqa:
									lowerCamelCase__ :      Tuple								=		self.tokenizer(
									    text=__magic_name__							,    add_special_tokens=__magic_name__							,    padding=__magic_name__							,    truncation=__magic_name__							,    max_length=__magic_name__							,    stride=__magic_name__							,    pad_to_multiple_of=__magic_name__							,    return_attention_mask=__magic_name__							,    return_overflowing_tokens=__magic_name__							,    return_special_tokens_mask=__magic_name__							,    return_offsets_mapping=__magic_name__							,    return_token_type_ids=__magic_name__							,    return_length=__magic_name__							,    verbose=__magic_name__							,    return_tensors=__magic_name__							,    **__magic_name__							,    )

									if "attention_mask" in text_encoding:
												lowerCamelCase__ :      str								=		text_encoding.pop("""attention_mask"""				)
									if "input_ids" in text_encoding:
												lowerCamelCase__ :      Union[str, Any]								=		text_encoding.pop("""input_ids"""				)
						else:
									lowerCamelCase__ :      List[str]								=		None

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

						return encoding_image_processor





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.batch_decode(*__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.decode(*__magic_name__							,    **__magic_name__				)





			@property
			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.tokenizer.model_input_names
						lowerCamelCase__ :      Union[str, Any]								=		self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(UpperCamelCase ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(UpperCamelCase )] == word:
															lowerCamelCase__ :      list[list[str]]								=		[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(UpperCamelCase )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(UpperCamelCase )]:
						combination.reverse()

			return table[len(UpperCamelCase )]


if __name__ == "__main__":
				print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
				print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
				print(
				    all_construct(
				        '''hexagonosaurus''',
				        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
				    )
				)



