






'''simple docstring'''



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


SCREAMING_SNAKE_CASE						=	logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			field(default=__A				,       metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
      UpperCamelCase_								=			field(
          default=__A				,       metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
      UpperCamelCase_								=			field(
          default=__A				,       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.'''
              )
          }				,       )
      UpperCamelCase_								=			field(
          default=__A				,       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.'''
              )
          }				,       )
      UpperCamelCase_								=			field(
          default=__A				,       metadata={
              '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
          }				,       )






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


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =super().to_dict()
       for k, v in d.items():
        if isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
         lowercase					:   List[str]				  =v.to_dict()
       return d











'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE						=	{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
          'M2M100ForConditionalGeneration',
          'M2M100Model',
          'M2M100PreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



