






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    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""image_processor""", """tokenizer"""]
				lowercase__       							= """BlipImageProcessor"""
				lowercase__       							= """AutoTokenizer"""




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

								'''simple docstring'''
								super().__init__(lowerCamelCase,						lowerCamelCase						)

								# add QFormer tokenizer
								lowercase__     				=    qformer_tokenizer




				def __call__(  self		:					Any,						lowerCamelCase		:					ImageInput = 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		:					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		:					List[str],						):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify at least images or text.'''						)

								lowercase__     				=    BatchFeature()

								if text is not None:
												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,						)
												encoding.update(lowerCamelCase						)
												lowercase__     				=    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,						)
												lowercase__     				=    qformer_text_encoding.pop('''input_ids'''						)
												lowercase__     				=    qformer_text_encoding.pop('''attention_mask'''						)

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

								return encoding




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

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




				def        lowercase__     (  self		:					Tuple,						*lowerCamelCase		:					Dict,						**lowerCamelCase		:					int						):

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




				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def        lowercase__     (  self		:					Optional[int]						):

								'''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						)						)




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

								'''simple docstring'''
								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						)
								lowercase__     				=    os.path.join(lowerCamelCase,						'''qformer_tokenizer'''						)
								self.qformer_tokenizer.save_pretrained(lowerCamelCase						)
								return super().save_pretrained(lowerCamelCase,						**lowerCamelCase						)




				@classmethod
				def        lowercase__     (  cls		:					Tuple,						lowerCamelCase		:					Optional[int],						**lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    AutoTokenizer.from_pretrained(lowerCamelCase,						subfolder='''qformer_tokenizer'''						)
								lowercase__     				=    cls._get_arguments_from_pretrained(lowerCamelCase,						**lowerCamelCase						)
								args.append(lowerCamelCase						)
								return cls(*lowerCamelCase						)












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() = }")




