



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase:  Dict			     =				{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase:  Tuple			     =				["""MobileNetV1FeatureExtractor"""]
		UpperCAmelCase:  Any			     =				["""MobileNetV1ImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase:  Optional[int]			     =				[
		    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileNetV1ForImageClassification""",
		    """MobileNetV1Model""",
		    """MobileNetV1PreTrainedModel""",
		    """load_tf_weights_in_mobilenet_v1""",
		]


if TYPE_CHECKING:
		from .configuration_mobilenet_va import (
		    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileNetVaConfig,
		    MobileNetVaOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
				from .image_processing_mobilenet_va import MobileNetVaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilenet_va import (
				    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileNetVaForImageClassification,
				    MobileNetVaModel,
				    MobileNetVaPreTrainedModel,
				    load_tf_weights_in_mobilenet_va,
				)


else:
		import sys

		UpperCAmelCase:  Dict			     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






'''simple docstring'''







from __future__ import annotations

from pathlib import Path






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     (xa, ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     -vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)
	_lowercase							=     +vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)

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






def 					_SCREAMING_SNAKE_CASE		(						snake_case_ = "p102_triangles.txt"	):
	_lowercase							=     Path(snake_case_	).parent.joinpath(snake_case_	).read_text(encoding="""utf-8"""	)

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

	_lowercase							=     0
	_lowercase							=     42

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

	return ret


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




