






'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase    :      Optional[Any]  			=		{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase    :      str  			=		['DeiTFeatureExtractor']
							UpperCamelCase    :      List[str]  			=		['DeiTImageProcessor']

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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase    :      int  			=		[
							    '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

							UpperCamelCase    :      Dict  			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		argparse.ArgumentParser()
							parser.add_argument('--dialogpt_path', default='.', type=str)
							UpperCamelCase    :      Dict  			=		parser.parse_args()
							for MODEL in DIALOGPT_MODELS:
														UpperCamelCase    :      Any  			=		os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
														UpperCamelCase    :      str  			=		F'./DialoGPT-{MODEL}'
														convert_dialogpt_checkpoint(
														    checkpoint_path,
														    pytorch_dump_folder_path,
														)

