
"""simple docstring"""




import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


A__      :	str          =    logging.get_logger(__name__)

A__      :	Dict          =    OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

A__      :	str          =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               UpperCAmelCase__							: str 							=						model_type_to_module_name(__UpperCamelCase		)

               UpperCAmelCase__							: List[str] 							=						importlib.import_module(F".{module_name}"       ,    "transformers.models"		)
               try:
                    return getattr(__UpperCamelCase       ,    __UpperCamelCase		)
               except AttributeError:
                    continue

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(__UpperCamelCase       ,    "__name__"       ,    __UpperCamelCase		) == class_name:
               return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
     UpperCAmelCase__							: int 							=						importlib.import_module("transformers"		)
     if hasattr(__UpperCamelCase       ,    __UpperCamelCase		):
          return getattr(__UpperCamelCase       ,    __UpperCamelCase		)

     return None



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Tuple = None       ,    lowerCAmelCase	:       List[Any] = False       ,    lowerCAmelCase	:       Tuple = False       ,    lowerCAmelCase	:       str = None       ,    lowerCAmelCase	:       Tuple = None       ,    lowerCAmelCase	:       Any = None       ,    lowerCAmelCase	:       Optional[int] = False       ,    **lowerCAmelCase	:       List[str]       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						get_file_from_repo(
         __UpperCamelCase       ,    __UpperCamelCase       ,    cache_dir=__UpperCamelCase       ,    force_download=__UpperCamelCase       ,    resume_download=__UpperCamelCase       ,    proxies=__UpperCamelCase       ,    use_auth_token=__UpperCamelCase       ,    revision=__UpperCamelCase       ,    local_files_only=__UpperCamelCase       ,    )
     if resolved_config_file is None:
          logger.info(
              "Could not locate the feature extractor configuration file, will try to use the model config instead."		)
          return {}

     with open(__UpperCamelCase       ,    encoding="utf-8"		) as reader:
          return json.load(__UpperCamelCase		)







class   _lowercase						:


     '''simple docstring'''





     def __init__(      self  )->					Union[str, Any]:
          raise EnvironmentError(
              "AutoFeatureExtractor is designed to be instantiated "
              "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."  )



     @classmethod
     @replace_list_option_in_docstrings(_SCREAMING_SNAKE_CASE  )
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Tuple:
          UpperCAmelCase__							: Optional[Any] 							=						kwargs.pop("config" ,			_SCREAMING_SNAKE_CASE  )
          UpperCAmelCase__							: List[Any] 							=						kwargs.pop("trust_remote_code" ,			_SCREAMING_SNAKE_CASE  )
          UpperCAmelCase__							: Any 							=						True

          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						FeatureExtractionMixin.get_feature_extractor_dict(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )
          UpperCAmelCase__							: str 							=						config_dict.get("feature_extractor_type" ,			_SCREAMING_SNAKE_CASE  )
          UpperCAmelCase__							: Dict 							=						None
          if "AutoFeatureExtractor" in config_dict.get("auto_map" ,			{}  ):
               UpperCAmelCase__							: Tuple 							=						config_dict["auto_map"]["AutoFeatureExtractor"]

          # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
          if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE  ):
                    UpperCAmelCase__							: Tuple 							=						AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )
               # It could be in `config.feature_extractor_type``
               UpperCAmelCase__							: Dict 							=						getattr(_SCREAMING_SNAKE_CASE ,			"feature_extractor_type" ,			_SCREAMING_SNAKE_CASE  )
               if hasattr(_SCREAMING_SNAKE_CASE ,			"auto_map"  ) and "AutoFeatureExtractor" in config.auto_map:
                    UpperCAmelCase__							: List[Any] 							=						config.auto_map["AutoFeatureExtractor"]

          if feature_extractor_class is not None:
               UpperCAmelCase__							: Union[str, Any] 							=						feature_extractor_class_from_name(_SCREAMING_SNAKE_CASE  )

          UpperCAmelCase__							: List[Any] 							=						feature_extractor_auto_map is not None
          UpperCAmelCase__							: Dict 							=						feature_extractor_class is not None or type(_SCREAMING_SNAKE_CASE  ) in FEATURE_EXTRACTOR_MAPPING
          UpperCAmelCase__							: Any 							=						resolve_trust_remote_code(
              _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE  )

          if has_remote_code and trust_remote_code:
               UpperCAmelCase__							: Any 							=						get_class_from_dynamic_module(
                   _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )
               UpperCAmelCase__							: Optional[int] 							=						kwargs.pop("code_revision" ,			_SCREAMING_SNAKE_CASE  )
               if os.path.isdir(_SCREAMING_SNAKE_CASE  ):
                    feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )
          elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(_SCREAMING_SNAKE_CASE  ) in FEATURE_EXTRACTOR_MAPPING:
               UpperCAmelCase__							: Union[str, Any] 							=						FEATURE_EXTRACTOR_MAPPING[type(_SCREAMING_SNAKE_CASE  )]
               return feature_extractor_class.from_dict(_SCREAMING_SNAKE_CASE ,			**_SCREAMING_SNAKE_CASE  )

          raise ValueError(
              F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
              F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
              F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()  )}"  )



     @staticmethod
     def  lowerCAmelCase__       (      __UpperCamelCase ,			__UpperCamelCase  )->					str:
          FEATURE_EXTRACTOR_MAPPING.register(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE  )





"""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,
        )



