



'''simple docstring'''




class      _lowerCAmelCase							:


	def __init__(self	,				lowercase							):
				A_     :						Any     =					n
				A_     :						Any     =					[None] * self.n
				A_     :						Tuple     =					0  # index of the first element
				A_     :						str     =					0
				A_     :						List[Any]     =					0


	def __len__(self							):
				return self.size


	def    _a     (self							):
				return self.size == 0


	def    _a     (self							):
				return False if self.is_empty() else self.array[self.front]


	def    _a     (self	,				lowercase							):
				if self.size >= self.n:
							raise Exception("""QUEUE IS FULL"""							)

				A_     :						Union[str, Any]     =					data
				A_     :						Optional[int]     =					(self.rear + 1) % self.n
				self.size += 1
				return self


	def    _a     (self							):
				if self.size == 0:
							raise Exception("""UNDERFLOW"""							)

				A_     :						Optional[int]     =					self.array[self.front]
				A_     :						Optional[int]     =					None
				A_     :						Tuple     =					(self.front + 1) % self.n
				self.size -= 1
				return temp




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				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)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=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	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)