




__SCREAMING_SNAKE_CASE 						=tuple[float, float, float]
__SCREAMING_SNAKE_CASE 						=tuple[float, float, float]

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    end_pointa[0] - end_pointa[0]
							SCREAMING_SNAKE_CASE_   	=    end_pointa[1] - end_pointa[1]
							SCREAMING_SNAKE_CASE_   	=    end_pointa[2] - end_pointa[2]
							return (x, y, z)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    ab[1] * ac[2] - ab[2] * ac[1]  # *i
							SCREAMING_SNAKE_CASE_   	=    (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							SCREAMING_SNAKE_CASE_   	=    ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							return tuple(round(__A  ,    __A     ) for x in vector     ) == (0, 0, 0)






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase = 1_0     ):
							SCREAMING_SNAKE_CASE_   	=    create_vector(__A  ,    __A     )
							SCREAMING_SNAKE_CASE_   	=    create_vector(__A  ,    __A     )
							return is_zero_vector(get_ad_vectors_cross(__A  ,    __A     )  ,    __A     )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


