




from __future__ import annotations

from pathlib import Path




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						tuple[int, int]       ,     __UpperCamelCase			:						tuple[int, int]      )  ->							Dict:
    return pointa[0] * pointa[1] - pointa[1] * pointa[0]




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int       ,     __UpperCamelCase			:						int      )  ->							List[str]:
    UpperCAmelCase_							 =     (xa, ya)
    UpperCAmelCase_							 =     (xa - xa, ya - ya)
    UpperCAmelCase_							 =     (xa - xa, ya - ya)
    UpperCAmelCase_							 =     -vector_product(lowercase_       ,     lowercase_      ) / vector_product(
        lowercase_       ,     lowercase_      )
    UpperCAmelCase_							 =     +vector_product(lowercase_       ,     lowercase_      ) / vector_product(
        lowercase_       ,     lowercase_      )

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str = "p102_triangles.txt"      )  ->							Optional[int]:
    UpperCAmelCase_							 =     Path(lowercase_      ).parent.joinpath(lowercase_      ).read_text(encoding='''utf-8'''      )

    UpperCAmelCase_							 =     []
    for line in data.strip().split('''\n'''      ):
        triangles.append([int(lowercase_      ) for number in line.split(''','''      )]      )

    UpperCAmelCase_							 =     0
    UpperCAmelCase_							 =     42

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

    return ret


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












"""simple docstring"""





from typing import TYPE_CHECKING

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


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''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

    _lowerCamelCase       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

