






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase__     =	['small', 'medium', 'large']

UpperCamelCase__     =	'lm_head.decoder.weight'
UpperCamelCase__     =	'lm_head.weight'





def       _UpperCamelCase       (a__				:List[Any]				,     a__				:List[str]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     torch.load(_A    )
							UpperCamelCase__			       =     d.pop(_A    )
							os.makedirs(_A				,     exist_ok=_A    )
							torch.save(_A				,     os.path.join(_A				,     _A    )    )


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











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCamelCase__     =	logging.get_logger(__name__)

class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : List[Any]         =    """upernet"""






							def __init__(  self					,	__lowerCAmelCase=None					,	__lowerCAmelCase=512					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=[1, 2, 3, 6]					,	__lowerCAmelCase=True					,	__lowerCAmelCase=0.4					,	__lowerCAmelCase=384					,	__lowerCAmelCase=256					,	__lowerCAmelCase=1					,	__lowerCAmelCase=False					,	__lowerCAmelCase=255					,	**__lowerCAmelCase					,	):
														super().__init__(**__lowerCAmelCase					)

														if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																					UpperCamelCase__			       =     CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					)
														elif isinstance(__lowerCAmelCase					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     backbone_config.get("""model_type"""					)
																					UpperCamelCase__			       =     CONFIG_MAPPING[backbone_model_type]
																					UpperCamelCase__			       =     config_class.from_dict(__lowerCAmelCase					)

														UpperCamelCase__			       =     backbone_config
														UpperCamelCase__			       =     hidden_size
														UpperCamelCase__			       =     initializer_range
														UpperCamelCase__			       =     pool_scales
														UpperCamelCase__			       =     use_auxiliary_head
														UpperCamelCase__			       =     auxiliary_loss_weight
														UpperCamelCase__			       =     auxiliary_in_channels
														UpperCamelCase__			       =     auxiliary_channels
														UpperCamelCase__			       =     auxiliary_num_convs
														UpperCamelCase__			       =     auxiliary_concat_input
														UpperCamelCase__			       =     loss_ignore_index







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     copy.deepcopy(self.__dict__					)
														UpperCamelCase__			       =     self.backbone_config.to_dict()
														UpperCamelCase__			       =     self.__class__.model_type
														return output




