
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      """MarkupLMFeatureExtractor"""
						__A:						Optional[Any]				   =      ("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
						__A:						Dict				   =      True

						def __call__(					self		:			Union[str, Any]	,						_lowerCamelCase		:			List[Any]=None	,						_lowerCamelCase		:			Any=None	,						_lowerCamelCase		:			Tuple=None	,						_lowerCamelCase		:			List[str]=None	,						_lowerCamelCase		:			List[Any]=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		:			Optional[bool] = None	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[str, TensorType]] = None	,						**_lowerCamelCase		:			str	,						):





													if self.parse_html:
																				if html_strings is None:
																											raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"			)

																				if nodes is not None or xpaths is not None or node_labels is not None:
																											raise ValueError(
																											    "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"			)

																				_UpperCAmelCase  :			Union[str, Any]	       =		self.feature_extractor(_lowerCamelCase			)
																				_UpperCAmelCase  :			Tuple	       =		features["""nodes"""]
																				_UpperCAmelCase  :			Dict	       =		features["""xpaths"""]
													else:
																				if html_strings is not None:
																											raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."			)
																				if nodes is None or xpaths is None:
																											raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"			)

        # # second, apply the tokenizer
													if questions is not None and self.parse_html:
																				if isinstance(_lowerCamelCase	,						_lowerCamelCase			):
																											_UpperCAmelCase  :			List[Any]	       =		[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

													_UpperCAmelCase  :			str	       =		self.tokenizer(
													    text=questions if questions is not None else nodes	,						text_pair=nodes if questions is not None else None	,						xpaths=_lowerCamelCase	,						node_labels=_lowerCamelCase	,						add_special_tokens=_lowerCamelCase	,						padding=_lowerCamelCase	,						truncation=_lowerCamelCase	,						max_length=_lowerCamelCase	,						stride=_lowerCamelCase	,						pad_to_multiple_of=_lowerCamelCase	,						return_token_type_ids=_lowerCamelCase	,						return_attention_mask=_lowerCamelCase	,						return_overflowing_tokens=_lowerCamelCase	,						return_special_tokens_mask=_lowerCamelCase	,						return_offsets_mapping=_lowerCamelCase	,						return_length=_lowerCamelCase	,						verbose=_lowerCamelCase	,						return_tensors=_lowerCamelCase	,						**_lowerCamelCase	,						)

													return encoded_inputs

						def 						a__ (					self		:			Optional[int]	,						*_lowerCamelCase		:			Any	,						**_lowerCamelCase		:			Optional[int]			):
													return self.tokenizer.batch_decode(*_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Union[str, Any]	,						*_lowerCamelCase		:			Optional[int]	,						**_lowerCamelCase		:			Any			):
													return self.tokenizer.decode(*_lowerCamelCase	,						**_lowerCamelCase			)






						@property
						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			Optional[int]	       =		self.tokenizer.model_input_names
													return tokenizer_input_names












"""simple docstring"""



from itertools import combinations, product




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->bool:
						if xa == ya == 0 or xa == ya == 0:
												return False
						A__    :	List[str]			=	xa * xa + ya * ya
						A__    :	List[Any]			=	xa * xa + ya * ya
						A__    :	Optional[int]			=	(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 5_0   )		->int:
						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1   ),    repeat=2   ),    2   )
						    if is_right(*UpperCAmelCase__,    *UpperCAmelCase__   )   )


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






