




from __future__ import annotations

from pathlib import Path




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    return pointa[0] * pointa[1] - pointa[1] * pointa[0]




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						bool:

    """simple docstring"""


    __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 (	lowercase_ = "p102_triangles.txt"   )							->						int:

    """simple docstring"""


    __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_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
