



'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    List[Any]           =		["image_processor", "tokenizer"]
						UpperCAmelCase							:    Tuple           =		"AutoImageProcessor"
						UpperCAmelCase							:    str           =		"AutoTokenizer"


						def __init__(					self	,       A_=None	,       A_=None	,       **A_      )	->	Any:
							lowerCAmelCase    =	None
							if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""	,       A_	,       )
								lowerCAmelCase    =	kwargs.pop("""feature_extractor"""      )

							lowerCAmelCase    =	image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
								raise ValueError("""You need to specify an `image_processor`."""      )
							if tokenizer is None:
								raise ValueError("""You need to specify a `tokenizer`."""      )

							super().__init__(A_	,       A_      )
							lowerCAmelCase    =	self.image_processor
							lowerCAmelCase    =	False


						def __call__(					self	,       *A_	,       **A_      )	->	str:
							# For backward compatibility
							if self._in_target_context_manager:
								return self.current_processor(*A_	,       **A_      )

							lowerCAmelCase    =	kwargs.pop("""images"""	,       A_      )
							lowerCAmelCase    =	kwargs.pop("""text"""	,       A_      )
							if len(A_      ) > 0:
								lowerCAmelCase    =	args[0]
								lowerCAmelCase    =	args[1:]

							if images is None and text is None:
								raise ValueError("""You need to specify either an `images` or `text` input to process."""      )

							if images is not None:
								lowerCAmelCase    =	self.image_processor(A_	,       *A_	,       **A_      )
							if text is not None:
								lowerCAmelCase    =	self.tokenizer(A_	,       **A_      )

							if text is None:
								return inputs
							elif images is None:
								return encodings
							else:
								lowerCAmelCase    =	encodings["""input_ids"""]
								return inputs


						def 				__snake_case    (					self	,       *A_	,       **A_      )	->	int:
							return self.tokenizer.batch_decode(*A_	,       **A_      )


						def 				__snake_case    (					self	,       *A_	,       **A_      )	->	Any:
							return self.tokenizer.decode(*A_	,       **A_      )


						@contextmanager
						def 				__snake_case    (					self      )	->	Tuple:
							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your images inputs, or in a separate call."""      )
							lowerCAmelCase    =	True
							lowerCAmelCase    =	self.tokenizer
							yield
							lowerCAmelCase    =	self.image_processor
							lowerCAmelCase    =	False


						@property
						def 				__snake_case    (					self      )	->	Optional[Any]:
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,       A_	,       )
							return self.image_processor_class




						@property
						def 				__snake_case    (					self      )	->	List[str]:
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,       A_	,       )
							return self.image_processor




'''simple docstring'''

from __future__ import annotations

from pathlib import Path






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						tuple[int, int]							,		_SCREAMING_SNAKE_CASE     :						tuple[int, int]							)			->						int:







	"""simple docstring"""
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						int							)			->						bool:







	"""simple docstring"""
	lowerCAmelCase    =	(xa, ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	(xa - xa, ya - ya)
	lowerCAmelCase    =	-vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	+vector_product(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							) / vector_product(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	return a > 0 and b > 0 and a + b < 1






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str = "p102_triangles.txt"							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	Path(_SCREAMING_SNAKE_CASE							).parent.joinpath(_SCREAMING_SNAKE_CASE							).read_text(encoding="""utf-8"""							)

	lowerCAmelCase    =	[]
	for line in data.strip().split("""\n"""							):
		triangles.append([int(_SCREAMING_SNAKE_CASE							) for number in line.split(""","""							)]							)

	lowerCAmelCase    =	0
	lowerCAmelCase    =	42

	for triangle in triangles:
		ret += contains_origin(*_SCREAMING_SNAKE_CASE							)

	return ret


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