


'''simple docstring'''
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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Union[str, Any]													=				torch.load(lowerCAmelCase__						)
    UpperCAmelCase__			:		Dict													=				d.pop(lowerCAmelCase__						)
    os.makedirs(lowerCAmelCase__  ,					exist_ok=lowerCAmelCase__						)
    torch.save(lowerCAmelCase__  ,					os.path.join(lowerCAmelCase__  ,					lowerCAmelCase__						)						)


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,
    )




'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__    					=      {
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      ['''VivitImageProcessor''']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''VivitModel''',
      '''VivitPreTrainedModel''',
      '''VivitForVideoClassification''',
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

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