



import argparse

import torch

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


logging.set_verbosity_info()
a_						:     List[Any]	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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_						:     str	     =		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_						:     Union[str, Any]	     =		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2