
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE				:Tuple									=							{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE				:Optional[int]									=							[
					    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ViTMSNModel',
					    'ViTMSNForImageClassification',
					    'ViTMSNPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_msn import (
										    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMSNForImageClassification,
										    ViTMSNModel,
										    ViTMSNPreTrainedModel,
										)

else:
					import sys

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






"""simple docstring"""

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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

