





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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_UpperCAmelCase             =							logging.getLogger(__name__)

@dataclass
class        snake_case_    (		__lowercase						):
  A_      							=   field(
      default=0.0					,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}						)
  A_      							=   field(default=__lowercase					,metadata={'help': 'Whether to SortishSamler or not.'}						)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}						)
  A_      							=   field(default=__lowercase					,metadata={'help': 'whether to use adafactor'}						)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}						)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}						)
  A_      							=   field(default=__lowercase					,metadata={'help': 'Dropout probability. Goes into model.config.'}						)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Attention dropout probability. Goes into model.config.'}						)
  A_      							=   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_torch_available,
)


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

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