

"""simple docstring"""




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


__A			:					List[Any]						=			{
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

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