'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_	   =			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CanineForMultipleChoice",
        "CanineForQuestionAnswering",
        "CanineForSequenceClassification",
        "CanineForTokenClassification",
        "CanineLayer",
        "CanineModel",
        "CaninePreTrainedModel",
        "load_tf_weights_in_canine",
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


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

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


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