

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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case    :    Union[str, Any]         =     logging.getLogger(__name__)


@dataclass
class     __SCREAMING_SNAKE_CASE					(   _a):
						_SCREAMING_SNAKE_CASE	:       Any   =					field(
						    default=0.0		,					metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''})
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					field(default=_a		,					metadata={'''help''': '''Whether to SortishSamler or not.'''})
						_SCREAMING_SNAKE_CASE	:       Dict   =					field(
						    default=_a		,					metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''})
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					field(default=_a		,					metadata={'''help''': '''whether to use adafactor'''})
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					field(
						    default=_a		,					metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''})
						_SCREAMING_SNAKE_CASE	:       str   =					field(
						    default=_a		,					metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[Any]   =					field(default=_a		,					metadata={'''help''': '''Dropout probability. Goes into model.config.'''})
						_SCREAMING_SNAKE_CASE	:       Tuple   =					field(
						    default=_a		,					metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''})
						_SCREAMING_SNAKE_CASE	:       str   =					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, is_vision_available


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





