


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCamelCase								=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    field(default=UpperCamelCase					,							metadata={"help": "Whether to use SortishSampler or not."}			)
       snake_case      =    field(
           default=UpperCamelCase					,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
       snake_case      =    field(
           default=UpperCamelCase					,							metadata={
               "help": (
                   "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                   "to the `max_length` value of the model configuration."
               )
           }					,							)
       snake_case      =    field(
           default=UpperCamelCase					,							metadata={
               "help": (
                   "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                   "to the `num_beams` value of the model configuration."
               )
           }					,							)
       snake_case      =    field(
           default=UpperCamelCase					,							metadata={
               "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
           }					,							)



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




         '''simple docstring'''





         A_      :				List[Any]		     =					super().to_dict()
         for k, v in d.items():
           if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
             A_      :				Any		     =					v.to_dict()
         return d








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         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_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[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		)->Union[str, Any]:




         '''simple docstring'''





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




