




from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['ImageGPTFeatureExtractor']
							UpperCamelCase__						=		['ImageGPTImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ImageGPTForCausalImageModeling',
							    'ImageGPTForImageClassification',
							    'ImageGPTModel',
							    'ImageGPTPreTrainedModel',
							    'load_tf_weights_in_imagegpt',
							]


if TYPE_CHECKING:
							from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
														from .image_processing_imagegpt import ImageGPTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_imagegpt import (
														    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ImageGPTForCausalImageModeling,
														    ImageGPTForImageClassification,
														    ImageGPTModel,
														    ImageGPTPreTrainedModel,
														    load_tf_weights_in_imagegpt,
														)

else:
							import sys

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






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_   							=      logging.get_logger(__name__)




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

       __lowerCamelCase  =		hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""      ):
              __lowerCamelCase  =		convert_classification(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForAudioFrameClassification"""      ):
              __lowerCamelCase  =		convert_diarization(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForXVector"""      ):
              __lowerCamelCase  =		convert_xvector(A__    ,     A__    ,     A__      )
       else:
              raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'      )

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       parser.add_argument(
           '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
       )
       parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
       parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

