



import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


@dataclass
class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Optional[float]			       =    field(
					    default=0.0							,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}      )
					A__				:		bool			       =    field(default=UpperCAmelCase_							,     metadata={"help": "Whether to SortishSamler or not."}      )
					A__				:		bool			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}      )
					A__				:		bool			       =    field(default=UpperCAmelCase_							,     metadata={"help": "whether to use adafactor"}      )
					A__				:		Optional[float]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}      )
					A__				:		Optional[float]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}      )
					A__				:		Optional[float]			       =    field(default=UpperCAmelCase_							,     metadata={"help": "Dropout probability. Goes into model.config."}      )
					A__				:		Optional[float]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Attention dropout probability. Goes into model.config."}      )
					A__				:		Optional[str]			       =    field(
					    default="linear"							,     metadata={"help": f"""Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"""}							,     )









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								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__       =    truncation

								A__       =    tokenize_kwargs

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

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""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					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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




