


"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A     :   str			    =			{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   int			    =			["DeiTFeatureExtractor"]
	A     :   Tuple			    =			["DeiTImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeiTForImageClassification",
	    "DeiTForImageClassificationWithTeacher",
	    "DeiTForMaskedImageModeling",
	    "DeiTModel",
	    "DeiTPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			[
	    "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFDeiTForImageClassification",
	    "TFDeiTForImageClassificationWithTeacher",
	    "TFDeiTForMaskedImageModeling",
	    "TFDeiTModel",
	    "TFDeiTPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	A     :   List[Any]			    =			_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     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   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()		)}'''}       ,)




