





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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a__: Optional[int]							   =						logging.getLogger(__name__)




@dataclass
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      field(
     default=0.0							,   metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''Whether to SortishSamler or not.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''whether to use adafactor'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''Dropout probability. Goes into model.config.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default='''linear'''							,   metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''}							,   )












from typing import TYPE_CHECKING

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


a__: int							   =						{
    '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:
       a__: List[Any]							   =						[
           '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

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





