






"""simple docstring"""







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard







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






    def     _UpperCamelCase							(			self ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/sd2-inpaint/init_image.png""" )
          UpperCAmelCase          =	load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )

          UpperCAmelCase          =	"""xvjiarui/stable-diffusion-2-inpainting"""
          UpperCAmelCase          =	FlaxStableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase_     ,safety_checker=lowerCamelCase_ )

          UpperCAmelCase          =	"""Face of a yellow cat, high resolution, sitting on a park bench"""

          UpperCAmelCase          =	jax.random.PRNGKey(0 )
          UpperCAmelCase          =	50

          UpperCAmelCase          =	jax.device_count()
          UpperCAmelCase          =	num_samples * [prompt]
          UpperCAmelCase          =	num_samples * [init_image]
          UpperCAmelCase          =	num_samples * [mask_image]
          UpperCAmelCase          =	pipeline.prepare_inputs(lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_ )

          # shard inputs and rng
          UpperCAmelCase          =	replicate(lowerCamelCase_ )
          UpperCAmelCase          =	jax.random.split(lowerCamelCase_     ,jax.device_count() )
          UpperCAmelCase          =	shard(lowerCamelCase_ )
          UpperCAmelCase          =	shard(lowerCamelCase_ )
          UpperCAmelCase          =	shard(lowerCamelCase_ )

          UpperCAmelCase          =	pipeline(
              lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_     ,jit=lowerCamelCase_ )

          UpperCAmelCase          =	output.images.reshape(lowerCamelCase_     ,512     ,512     ,3 )

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

          UpperCAmelCase          =	jnp.asarray(jax.device_get(image_slice.flatten() ) )
          UpperCAmelCase          =	jnp.array(
              [0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
          print(F'''output_slice: {output_slice}''' )
          assert jnp.abs(output_slice - 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)



