






"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCamelCase					      =  False







class  lowerCamelCase__ (	unittest.TestCase			):
    pass







@slow
@require_torch_gpu
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
          pipe.to(SCREAMING_SNAKE_CASE_ )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )

          UpperCAmelCase          =	load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
          UpperCAmelCase          =	torch.manual_seed(0 )
          UpperCAmelCase          =	pipe(
              image=SCREAMING_SNAKE_CASE_     ,generator=SCREAMING_SNAKE_CASE_     ,guidance_scale=7.5     ,num_inference_steps=50     ,output_type="""numpy"""     ,).images

          UpperCAmelCase          =	image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          UpperCAmelCase          =	np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

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











"""simple docstring"""







import argparse

import torch

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


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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""projector.weight"""]
      UpperCAmelCase          =	downstream_dict["""projector.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""connector.weight"""]
      UpperCAmelCase          =	downstream_dict["""connector.bias"""]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            UpperCAmelCase          =	downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCAmelCase          =	downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

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

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

      hf_feature_extractor.save_pretrained(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


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)



