




"""simple docstring"""





from typing import TYPE_CHECKING

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


A				=							{"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A				=							[
					    """UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """UniSpeechForCTC""",
					    """UniSpeechForPreTraining""",
					    """UniSpeechForSequenceClassification""",
					    """UniSpeechModel""",
					    """UniSpeechPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_unispeech import (
										    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
										    UniSpeechForCTC,
										    UniSpeechForPreTraining,
										    UniSpeechForSequenceClassification,
										    UniSpeechModel,
										    UniSpeechPreTrainedModel,
										)

else:
					import sys

					A				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

a__		     =     '''lm_head.decoder.weight'''
a__		     =     '''lm_head.weight'''





def 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)


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






