'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						Optional[int]				)	->							Any:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Optional[Any]				)	->							List[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Dict				)	->							Dict:
      '''simple docstring'''


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

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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Tuple ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Tuple:
      '''simple docstring'''


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

      lowercase_     =      checkpoint["Downstream"]

      lowercase_     =      WavaVecaConfig.from_pretrained(__lowerCamelCase				)
      lowercase_     =      WavaVecaFeatureExtractor.from_pretrained(
          __lowerCamelCase ,		return_attention_mask=__lowerCamelCase ,		do_normalize=__lowerCamelCase				)

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

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

      hf_feature_extractor.save_pretrained(__lowerCamelCase				)
      hf_model.save_pretrained(__lowerCamelCase				)


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







'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")
         lowercase_     =      pipe.prepare_image_inputs([canny_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png")
         lowercase_     =      pipe.prepare_image_inputs([pose_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






