


"""simple docstring"""






UpperCamelCase__				:Optional[int]			   =	tuple[float, float, float]
UpperCamelCase__				:Union[str, Any]			   =	tuple[float, float, float]





def 				A_       ( snake_case__       ,					snake_case__		)					->  Vectorad:
			_UpperCamelCase					:int					     =	end_pointa[0] - end_pointa[0]
			_UpperCamelCase					:Tuple					     =	end_pointa[1] - end_pointa[1]
			_UpperCamelCase					:int					     =	end_pointa[2] - end_pointa[2]
			return (x, y, z)





def 				A_       ( snake_case__       ,					snake_case__		)					->  Vectorad:
			_UpperCamelCase					:Tuple					     =	ab[1] * ac[2] - ab[2] * ac[1]  # *i
			_UpperCamelCase					:Any					     =	(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
			_UpperCamelCase					:Tuple					     =	ab[0] * ac[1] - ab[1] * ac[0]  # *k
			return (x, y, z)





def 				A_       ( snake_case__       ,					snake_case__		)					->  bool:
			return tuple(round(snake_case__       ,					snake_case__		) for x in vector		) == (0, 0, 0)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__ = 10		)					->  bool:
			_UpperCamelCase					:List[Any]					     =	create_vector(snake_case__       ,					snake_case__		)
			_UpperCamelCase					:Optional[int]					     =	create_vector(snake_case__       ,					snake_case__		)
			return is_zero_vector(get_ad_vectors_cross(snake_case__       ,					snake_case__		)       ,					snake_case__		)





"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

