




"""simple docstring"""

from __future__ import annotations

from pathlib import Path



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]    ,       lowercase__					:       Optional[Any]				):
		return pointa[0] * pointa[1] - pointa[1] * pointa[0]



def 					UpperCamelCase__       (						lowercase__					:       List[Any]    ,       lowercase__					:       Tuple    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       Dict    ,       lowercase__					:       int    ,       lowercase__					:       Tuple				):
		snake_case						:  Union[str, Any]        =			(xa, ya)
		snake_case						:  Dict        =			(xa - xa, ya - ya)
		snake_case						:  Optional[int]        =			(xa - xa, ya - ya)
		snake_case						:  str        =			-vector_product(_lowerCAmelCase    ,       _lowerCAmelCase				) / vector_product(
		    _lowerCAmelCase    ,       _lowerCAmelCase				)
		snake_case						:  List[str]        =			+vector_product(_lowerCAmelCase    ,       _lowerCAmelCase				) / vector_product(
		    _lowerCAmelCase    ,       _lowerCAmelCase				)

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



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any] = "p102_triangles.txt"				):
		snake_case						:  int        =			Path(_lowerCAmelCase				).parent.joinpath(_lowerCAmelCase				).read_text(encoding="utf-8"				)

		snake_case						:  Optional[Any]        =			[]
		for line in data.strip().split("\n"				):
				triangles.append([int(_lowerCAmelCase				) for number in line.split(","				)]				)

		snake_case						:  Tuple        =			0
		snake_case						:  Union[str, Any]        =			42

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

		return ret


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







"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

