
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__lowerCAmelCase												=	logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						field(default=__snake_case					,							metadata={"help": "Whether to use SortishSampler or not."}				)
							lowerCAmelCase_ 						=						field(
							    default=__snake_case					,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}				)
							lowerCAmelCase_ 						=						field(
							    default=__snake_case					,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }					,							)
							lowerCAmelCase_ 						=						field(
							    default=__snake_case					,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }					,							)
							lowerCAmelCase_ 						=						field(
							    default=__snake_case					,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }					,							)

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  super().to_dict()
														for k, v in d.items():
																					if isinstance(_a  ,  _a  ):
																												_snake_case       =  v.to_dict()
														return d

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['EfficientFormerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EfficientFormerForImageClassification',
				    'EfficientFormerForImageClassificationWithTeacher',
				    'EfficientFormerModel',
				    'EfficientFormerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFEfficientFormerForImageClassification',
				    'TFEfficientFormerForImageClassificationWithTeacher',
				    'TFEfficientFormerModel',
				    'TFEfficientFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)