from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=["image_processor", "tokenizer"]
			UpperCamelCase__    							="BlipImageProcessor"
			UpperCamelCase__    							="AutoTokenizer"





			def __init__(       self	:	int  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[Any]						):
										_lowerCAmelCase							=							False
										super().__init__(lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							self.image_processor





			def __call__(       self	:	Optional[Any]  ,  lowercase__	:	ImageInput = None  ,  lowercase__	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,  lowercase__	:	bool = True  ,  lowercase__	:	Union[bool, str, PaddingStrategy] = False  ,  lowercase__	:	Union[bool, str, TruncationStrategy] = None  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	int = 0  ,  lowercase__	:	Optional[int] = None  ,  lowercase__	:	Optional[bool] = None  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = False  ,  lowercase__	:	bool = True  ,  lowercase__	:	Optional[Union[str, TensorType]] = None  ,  **lowercase__	:	Optional[Any]  ,  ):
										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:
																	_lowerCAmelCase							=							self.tokenizer
																	_lowerCAmelCase							=							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__  ,  )
																	return text_encoding

										# add pixel_values
										_lowerCAmelCase							=							self.image_processor(lowercase__  ,  return_tensors=lowercase__						)

										if text is not None:
																	_lowerCAmelCase							=							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__  ,  )
										else:
																	_lowerCAmelCase							=							None

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

										return encoding_image_processor





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  *lowercase__	:	Union[str, Any]  ,  **lowercase__	:	List[str]						):
										return self.tokenizer.batch_decode(*lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  *lowercase__	:	Union[str, Any]  ,  **lowercase__	:	str						):
										return self.tokenizer.decode(*lowercase__  ,  **lowercase__						)





			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							self.tokenizer.model_input_names
										_lowerCAmelCase							=							self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







from math import ceil, sqrt






def _lowerCamelCase  (		snake_case = 1_000_000  ):
							_lowerCAmelCase							=							0

							for outer_width in range(3     , (limit // 4) + 2  ):
														if outer_width**2 > limit:
																					_lowerCAmelCase							=							max(ceil(sqrt(outer_width**2 - limit  )  )     , 1  )
														else:
																					_lowerCAmelCase							=							1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


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






