




from typing import TYPE_CHECKING

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


UpperCamelCase__            =     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""LEDTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LEDForConditionalGeneration""",
					    """LEDForQuestionAnswering""",
					    """LEDForSequenceClassification""",
					    """LEDModel""",
					    """LEDPreTrainedModel""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
					from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
					from .tokenization_led import LEDTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_led_fast import LEDTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_led import (
										    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LEDForConditionalGeneration,
										    LEDForQuestionAnswering,
										    LEDForSequenceClassification,
										    LEDModel,
										    LEDPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
					import sys

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










from __future__ import annotations

from pathlib import Path







def       _a (		SCREAMING_SNAKE_CASE_				:							tuple[int, int] , SCREAMING_SNAKE_CASE_				:							tuple[int, int]	):
					return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					__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       _a (		SCREAMING_SNAKE_CASE_				:							str = "p102_triangles.txt"	):
					__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() = }''')




