
"""simple docstring"""




from typing import Dict

from .base import GenericTensor, Pipeline



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											if tokenize_kwargs is None:
																		SCREAMING_SNAKE_CASE__						:       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)"""    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   truncation

											SCREAMING_SNAKE_CASE__						:       List[str]           =   tokenize_kwargs

											SCREAMING_SNAKE_CASE__						:       Any           =   {}
											if return_tensors is not None:
																		SCREAMING_SNAKE_CASE__						:       int           =   return_tensors

											return preprocess_params, {}, postprocess_params



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict[str, GenericTensor]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.framework
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.tokenizer(SCREAMING_SNAKE_CASE__   ,       return_tensors=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )
											return model_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.model(**SCREAMING_SNAKE_CASE__    )
											return model_outputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False    )					->		Optional[Any]:



											"""simple docstring"""




											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   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											return super().__call__(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )






"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




