




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   lowerCamelCase				(_UpperCAmelCase      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  ['''image_processor''', '''tokenizer''']
		_snake_case   :   Dict							=  '''BlipImageProcessor'''
		_snake_case   :   List[Any]							=  '''AutoTokenizer'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[Any]:
							super().__init__(lowercase_    ,		lowercase_       )

							# add QFormer tokenizer
							UpperCAmelCase_   :				Optional[int]						=						qformer_tokenizer



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

							UpperCAmelCase_   :				List[str]						=						BatchFeature()

							if text is not None:
												UpperCAmelCase_   :				Optional[Any]						=						self.tokenizer(
												    text=lowercase_    ,		add_special_tokens=lowercase_    ,		padding=lowercase_    ,		truncation=lowercase_    ,		max_length=lowercase_    ,		stride=lowercase_    ,		pad_to_multiple_of=lowercase_    ,		return_attention_mask=lowercase_    ,		return_overflowing_tokens=lowercase_    ,		return_special_tokens_mask=lowercase_    ,		return_offsets_mapping=lowercase_    ,		return_token_type_ids=lowercase_    ,		return_length=lowercase_    ,		verbose=lowercase_    ,		return_tensors=lowercase_    ,		**lowercase_    ,		)
												encoding.update(lowercase_       )
												UpperCAmelCase_   :				Tuple						=						self.qformer_tokenizer(
												    text=lowercase_    ,		add_special_tokens=lowercase_    ,		padding=lowercase_    ,		truncation=lowercase_    ,		max_length=lowercase_    ,		stride=lowercase_    ,		pad_to_multiple_of=lowercase_    ,		return_attention_mask=lowercase_    ,		return_overflowing_tokens=lowercase_    ,		return_special_tokens_mask=lowercase_    ,		return_offsets_mapping=lowercase_    ,		return_token_type_ids=lowercase_    ,		return_length=lowercase_    ,		verbose=lowercase_    ,		return_tensors=lowercase_    ,		**lowercase_    ,		)
												UpperCAmelCase_   :				List[Any]						=						qformer_text_encoding.pop('input_ids'       )
												UpperCAmelCase_   :				List[Any]						=						qformer_text_encoding.pop('attention_mask'       )

							if images is not None:
												UpperCAmelCase_   :				str						=						self.image_processor(lowercase_    ,		return_tensors=lowercase_       )
												encoding.update(lowercase_       )

							return encoding



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							int:
							return self.tokenizer.batch_decode(*lowercase_    ,		**lowercase_       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Any:
							return self.tokenizer.decode(*lowercase_    ,		**lowercase_       )



		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Tuple						=						self.tokenizer.model_input_names
							UpperCAmelCase_   :				Optional[Any]						=						self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[str, Any]:
							if os.path.isfile(lowercase_       ):
												raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"       )
							os.makedirs(lowercase_    ,		exist_ok=lowercase_       )
							UpperCAmelCase_   :				List[Any]						=						os.path.join(lowercase_    ,		'qformer_tokenizer'       )
							self.qformer_tokenizer.save_pretrained(lowercase_       )
							return super().save_pretrained(lowercase_    ,		**lowercase_       )



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Tuple						=						AutoTokenizer.from_pretrained(lowercase_    ,		subfolder='qformer_tokenizer'       )
							UpperCAmelCase_   :				List[Any]						=						cls._get_arguments_from_pretrained(lowercase_    ,		**lowercase_       )
							args.append(lowercase_       )
							return cls(*lowercase_       )














from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__				(					__snake_case   :							int = 1_000_000					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				defaultdict						=						defaultdict(__snake_case  )

					for outer_width in range(3					,	(t_limit // 4) + 2  ):
										if outer_width * outer_width > t_limit:
															UpperCAmelCase_   :				Union[str, Any]						=						max(
															    ceil(sqrt(outer_width * outer_width - t_limit  )  )					,	1  )
										else:
															UpperCAmelCase_   :				int						=						1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(__snake_case					,	outer_width - 1					,	2  ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10  )


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






