



from typing import TYPE_CHECKING

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


_lowercase		:				List[str]      		={
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Union[str, Any]      		=["MaskFormerFeatureExtractor"]
		_lowercase		:				str      		=["MaskFormerImageProcessor"]


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				int      		=[
		    "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MaskFormerForInstanceSegmentation",
		    "MaskFormerModel",
		    "MaskFormerPreTrainedModel",
		]
		_lowercase		:				Tuple      		=[
		    "MaskFormerSwinBackbone",
		    "MaskFormerSwinModel",
		    "MaskFormerSwinPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
		from .configuration_maskformer_swin import MaskFormerSwinConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_maskformer import MaskFormerFeatureExtractor
				from .image_processing_maskformer import MaskFormerImageProcessor
		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_maskformer import (
				    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaskFormerForInstanceSegmentation,
				    MaskFormerModel,
				    MaskFormerPreTrainedModel,
				)
				from .modeling_maskformer_swin import (
				    MaskFormerSwinBackbone,
				    MaskFormerSwinModel,
				    MaskFormerSwinPreTrainedModel,
				)


else:
		import sys

		_lowercase		:				str      		=_LazyModule(__name__, globals()["__file__"], _import_structure)









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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




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




