






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    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""image_processor""", """tokenizer"""]
				lowercase__       							= """Pix2StructImageProcessor"""
				lowercase__       							= ("""T5Tokenizer""", """T5TokenizerFast""")




				def __init__(  self		:					List[Any],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    False
								super().__init__(lowerCamelCase,						lowerCamelCase						)




				def __call__(  self		:					str,						lowerCamelCase		:					str=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		:					Optional[int] = 2_048,						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		:					Any,						):

								'''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:
												lowercase__     				=    self.tokenizer
												lowercase__     				=    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
												lowercase__     				=    self.image_processor(
												    lowerCamelCase,						return_tensors=lowerCamelCase,						max_patches=lowerCamelCase,						**lowerCamelCase						)
								else:
												# add pixel_values and bbox
												lowercase__     				=    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:
												lowercase__     				=    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:
																lowercase__     				=    text_encoding.pop('''attention_mask'''						)
												if "input_ids" in text_encoding:
																lowercase__     				=    text_encoding.pop('''input_ids'''						)
								else:
												lowercase__     				=    None

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

								return encoding_image_processor




				def        lowercase__     (  self		:					List[Any],						*lowerCamelCase		:					Optional[int],						**lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any],						*lowerCamelCase		:					Any,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,						**lowerCamelCase						)




				@property
				def        lowercase__     (  self		:					str						):

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












from itertools import count






def 						a    (		lowerCamelCase_ = 50       ):






				'''simple docstring'''
				lowercase__     				=    [1] * min_block_length

				for n in count(lowerCamelCase_       ):
								fill_count_functions.append(1       )

								for block_length in range(lowerCamelCase_	,     n + 1       ):
												for block_start in range(n - block_length       ):
																fill_count_functions[n] += fill_count_functions[
																    n - block_start - block_length - 1
																]

												fill_count_functions[n] += 1

								if fill_count_functions[n] > 100_0000:
												break

				return n


if __name__ == "__main__":
							print(F"{solution() = }")




