
"""simple docstring"""




from typing import TYPE_CHECKING

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


A__      :	Union[str, Any]          =    {
    """configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
    """feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
    """processing_wav2vec2""": ["""Wav2Vec2Processor"""],
    """tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__      :	Dict          =    [
        """WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """Wav2Vec2ForAudioFrameClassification""",
        """Wav2Vec2ForCTC""",
        """Wav2Vec2ForMaskedLM""",
        """Wav2Vec2ForPreTraining""",
        """Wav2Vec2ForSequenceClassification""",
        """Wav2Vec2ForXVector""",
        """Wav2Vec2Model""",
        """Wav2Vec2PreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__      :	Optional[Any]          =    [
        """TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFWav2Vec2ForCTC""",
        """TFWav2Vec2Model""",
        """TFWav2Vec2PreTrainedModel""",
        """TFWav2Vec2ForSequenceClassification""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__      :	Optional[int]          =    [
        """FlaxWav2Vec2ForCTC""",
        """FlaxWav2Vec2ForPreTraining""",
        """FlaxWav2Vec2Model""",
        """FlaxWav2Vec2PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
    from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
    from .processing_wavaveca import WavaVecaProcessor
    from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_wavaveca import (
            WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaForAudioFrameClassification,
            WavaVecaForCTC,
            WavaVecaForMaskedLM,
            WavaVecaForPreTraining,
            WavaVecaForSequenceClassification,
            WavaVecaForXVector,
            WavaVecaModel,
            WavaVecaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_wavaveca import (
            TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFWavaVecaForCTC,
            TFWavaVecaForSequenceClassification,
            TFWavaVecaModel,
            TFWavaVecaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_wavaveca import (
            FlaxWavaVecaForCTC,
            FlaxWavaVecaForPreTraining,
            FlaxWavaVecaModel,
            FlaxWavaVecaPreTrainedModel,
        )


else:
    import sys

    A__      :	Optional[Any]          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


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



