


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Union[str, Any]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__    ,   config=UpperCamelCase__   )
						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        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				List[str]   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__    ,   config=UpperCamelCase__   )
						lowerCAmelCase__       =						downstream_dict["""model.linear.weight"""]
						lowerCAmelCase__       =						downstream_dict["""model.linear.bias"""]
						return model


def        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Any   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						WavaVecaForXVector.from_pretrained(UpperCamelCase__    ,   config=UpperCamelCase__   )
						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        _a  (     UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				str   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						torch.load(UpperCamelCase__    ,   map_location="cpu"   )

						lowerCAmelCase__       =						checkpoint["""Downstream"""]

						lowerCAmelCase__       =						WavaVecaConfig.from_pretrained(UpperCamelCase__   )
						lowerCAmelCase__       =						WavaVecaFeatureExtractor.from_pretrained(
						    UpperCamelCase__    ,   return_attention_mask=UpperCamelCase__    ,   do_normalize=UpperCamelCase__   )

						lowerCAmelCase__       =						hf_config.architectures[0]
						if arch.endswith("ForSequenceClassification"   ):
												lowerCAmelCase__       =						convert_classification(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__   )
						elif arch.endswith("ForAudioFrameClassification"   ):
												lowerCAmelCase__       =						convert_diarization(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__   )
						elif arch.endswith("ForXVector"   ):
												lowerCAmelCase__       =						convert_xvector(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__   )
						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(UpperCamelCase__   )
						hf_model.save_pretrained(UpperCamelCase__   )


if __name__ == "__main__":
	a_							=    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.''')
	a_							=    parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)














# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)






