






'''simple docstring'''







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 _lowercase	(				_lowercase		):
							a				      =       ["""image_processor""", """tokenizer"""]
							a				      =       """Pix2StructImageProcessor"""
							a				      =       ("""T5Tokenizer""", """T5TokenizerFast""")




							def __init__(							self:				Optional[int]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       False
														super().__init__(UpperCamelCase__			,							UpperCamelCase__     )




							def __call__(							self:				Union[str, Any]			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Union[bool, str, PaddingStrategy] = False			,							UpperCamelCase__:				Union[bool, str, TruncationStrategy] = None			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[int] = 2_048			,							UpperCamelCase__:				int = 0			,							UpperCamelCase__:				Optional[int] = None			,							UpperCamelCase__:				Optional[bool] = None			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = False			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				Optional[Union[str, TensorType]] = None			,							**UpperCamelCase__:				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 and not self.image_processor.is_vqa:
																					lowerCamelCase__					:	Dict         =       self.tokenizer
																					lowerCamelCase__					:	Any         =       self.tokenizer(
																					    text=UpperCamelCase__			,							add_special_tokens=UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__			,							max_length=UpperCamelCase__			,							stride=UpperCamelCase__			,							pad_to_multiple_of=UpperCamelCase__			,							return_attention_mask=UpperCamelCase__			,							return_overflowing_tokens=UpperCamelCase__			,							return_special_tokens_mask=UpperCamelCase__			,							return_offsets_mapping=UpperCamelCase__			,							return_token_type_ids=UpperCamelCase__			,							return_length=UpperCamelCase__			,							verbose=UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							**UpperCamelCase__			,							)
																					return text_encoding

														if not self.image_processor.is_vqa:
																					# add pixel_values
																					lowerCamelCase__					:	List[Any]         =       self.image_processor(
																					    UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							max_patches=UpperCamelCase__			,							**UpperCamelCase__     )
														else:
																					# add pixel_values and bbox
																					lowerCamelCase__					:	List[str]         =       self.image_processor(
																					    UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							max_patches=UpperCamelCase__			,							header_text=UpperCamelCase__			,							**UpperCamelCase__     )

														if text is not None and not self.image_processor.is_vqa:
																					lowerCamelCase__					:	Optional[Any]         =       self.tokenizer(
																					    text=UpperCamelCase__			,							add_special_tokens=UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__			,							max_length=UpperCamelCase__			,							stride=UpperCamelCase__			,							pad_to_multiple_of=UpperCamelCase__			,							return_attention_mask=UpperCamelCase__			,							return_overflowing_tokens=UpperCamelCase__			,							return_special_tokens_mask=UpperCamelCase__			,							return_offsets_mapping=UpperCamelCase__			,							return_token_type_ids=UpperCamelCase__			,							return_length=UpperCamelCase__			,							verbose=UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							**UpperCamelCase__			,							)

																					if "attention_mask" in text_encoding:
																												lowerCamelCase__					:	Dict         =       text_encoding.pop("""attention_mask"""     )
																					if "input_ids" in text_encoding:
																												lowerCamelCase__					:	int         =       text_encoding.pop("""input_ids"""     )
														else:
																					lowerCamelCase__					:	Optional[int]         =       None

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

														return encoding_image_processor




							def 		lowerCamelCase_				(							self:				str			,							*UpperCamelCase__:				Optional[Any]			,							**UpperCamelCase__:				List[str]     ):
														return self.tokenizer.batch_decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							*UpperCamelCase__:				Any			,							**UpperCamelCase__:				Optional[int]     ):
														return self.tokenizer.decode(*UpperCamelCase__			,							**UpperCamelCase__     )




							@property
							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Union[str, Any]         =       self.tokenizer.model_input_names
														lowerCamelCase__					:	List[Any]         =       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )





"""simple docstring"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						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() = }")
