



"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer




class 			lowercase__			(      __lowerCAmelCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					['''image_processor''', '''tokenizer''']
							_UpperCAmelCase      				=					'''BlipImageProcessor'''
							_UpperCAmelCase      				=					'''AutoTokenizer'''






							def __init__(  self	,       snake_case	,       snake_case	,       snake_case						)			->							List[str]:
									super().__init__(lowerCAmelCase_	,       lowerCAmelCase_						)

									# add QFormer tokenizer
									_UpperCAmelCase							 =		qformer_tokenizer






							def __call__(  self	,       snake_case = None	,       snake_case = None	,       snake_case = True	,       snake_case = False	,       snake_case = None	,       snake_case = None	,       snake_case = 0	,       snake_case = None	,       snake_case = None	,       snake_case = False	,       snake_case = False	,       snake_case = False	,       snake_case = False	,       snake_case = False	,       snake_case = True	,       snake_case = None	,       **snake_case	,       )			->							BatchFeature:
									if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'						)

									_UpperCAmelCase							 =		BatchFeature()

									if text is not None:
											_UpperCAmelCase							 =		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_	,       )
											encoding.update(lowerCAmelCase_						)
											_UpperCAmelCase							 =		self.qformer_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_	,       )
											_UpperCAmelCase							 =		qformer_text_encoding.pop('input_ids'						)
											_UpperCAmelCase							 =		qformer_text_encoding.pop('attention_mask'						)

									if images is not None:
											_UpperCAmelCase							 =		self.image_processor(lowerCAmelCase_	,       return_tensors=lowerCAmelCase_						)
											encoding.update(lowerCAmelCase_						)

									return encoding






							def        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							Dict:
									return self.tokenizer.batch_decode(*lowerCAmelCase_	,       **lowerCAmelCase_						)






							def        lowerCamelCase_   (  self	,       *snake_case	,       **snake_case						)			->							Any:
									return self.tokenizer.decode(*lowerCAmelCase_	,       **lowerCAmelCase_						)






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def        lowerCamelCase_   (  self						)			->							Any:
									_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_   (  self	,       snake_case	,       **snake_case						)			->							Dict:
									if os.path.isfile(lowerCAmelCase_						):
											raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'						)
									os.makedirs(lowerCAmelCase_	,       exist_ok=lowerCAmelCase_						)
									_UpperCAmelCase							 =		os.path.join(lowerCAmelCase_	,       'qformer_tokenizer'						)
									self.qformer_tokenizer.save_pretrained(lowerCAmelCase_						)
									return super().save_pretrained(lowerCAmelCase_	,       **lowerCAmelCase_						)






							@classmethod
							def        lowerCamelCase_   (  cls	,       snake_case	,       **snake_case						)			->							Dict:
									_UpperCAmelCase							 =		AutoTokenizer.from_pretrained(lowerCAmelCase_	,       subfolder='qformer_tokenizer'						)
									_UpperCAmelCase							 =		cls._get_arguments_from_pretrained(lowerCAmelCase_	,       **lowerCAmelCase_						)
									args.append(lowerCAmelCase_						)
									return cls(*lowerCAmelCase_						)






"""simple docstring"""





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		[[0 for _ in range(A       )] for _ in range(m + 1       )]
		for i in range(m + 1       ):
				_UpperCAmelCase							 =		1

		for n in range(m + 1       ):
				for k in range(1     ,				A       ):
						memo[n][k] += memo[n][k - 1]
						if n - k > 0:
								memo[n][k] += memo[n - k - 1][k]

		return memo[m][m - 1]


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										try:
															lowercase  							=					int(input('''Enter a number: ''').strip())
															print(partition(n))
										except ValueError:
															print('''Please enter a number.''')
					else:
										try:
															lowercase  							=					int(sys.argv[1])
															print(partition(n))
										except ValueError:
															print('''Please pass a number.''')


