
"""simple docstring"""





from typing import Dict

from .base import GenericTensor, Pipeline
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def 	snake_case_     (     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				if tokenize_kwargs is None:
						__SCREAMING_SNAKE_CASE            = {}

				if truncation is not None:
						if "truncation" in tokenize_kwargs:
								raise ValueError(
								    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)""")
						__SCREAMING_SNAKE_CASE            = truncation

				__SCREAMING_SNAKE_CASE            = tokenize_kwargs

				__SCREAMING_SNAKE_CASE            = {}
				if return_tensors is not None:
						__SCREAMING_SNAKE_CASE            = return_tensors

				return preprocess_params, {}, postprocess_params




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.framework
				__SCREAMING_SNAKE_CASE            = self.tokenizer(lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__)
				return model_inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				return model_outputs




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=False):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
						return model_outputs[0]
				if self.framework == "pt":
						return model_outputs[0].tolist()
				elif self.framework == "tf":
						return model_outputs[0].numpy().tolist()




		def __call__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return super().__call__(*lowerCAmelCase__							,  **lowerCAmelCase__)






"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




