code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import unittest
  
  import numpy as np
  import torch
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 			UpperCAmelCase_						(		__lowerCamelCase,     unittest.TestCase							):
      lowercase__		      =   KandinskyPriorPipeline
      lowercase__		      =   ['prompt']
      lowercase__		      =   ['prompt', 'negative_prompt']
      lowercase__		      =   [
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      lowercase__		      =   False
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Tuple   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       return 32
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Optional[int]   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       return 32
  
  
  
  
      @property
      def 							__magic_name__							(					self		: List[str]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       return self.time_input_dim
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Optional[Any]   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       return self.time_input_dim * 4
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Optional[int]   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       return 100
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Tuple   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__ =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
       return tokenizer
  
  
  
  
      @property
      def 							__magic_name__							(					self		: str   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       torch.manual_seed(0   )
       A__ =       CLIPTextConfig(
           bos_token_id=0			,				eos_token_id=2			,				hidden_size=self.text_embedder_hidden_size			,				projection_dim=self.text_embedder_hidden_size			,				intermediate_size=37			,				layer_norm_eps=1e-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1_000			,				)
       return CLIPTextModelWithProjection(snake_case_   )
  
  
  
  
      @property
      def 							__magic_name__							(					self		: str   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       torch.manual_seed(0   )
  
       A__ =       {
           "num_attention_heads": 2,
           "attention_head_dim": 12,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }
  
       A__ =       PriorTransformer(**snake_case_   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       A__ =       nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model
  
  
  
  
      @property
      def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       torch.manual_seed(0   )
       A__ =       CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size			,				image_size=224			,				projection_dim=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_channels=3			,				num_hidden_layers=5			,				patch_size=14			,				)
  
       A__ =       CLIPVisionModelWithProjection(snake_case_   )
       return model
  
  
  
  
      @property
      def 							__magic_name__							(					self		: int   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       CLIPImageProcessor(
           crop_size=224			,				do_center_crop=snake_case_			,				do_normalize=snake_case_			,				do_resize=snake_case_			,				image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]			,				image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,				resample=3			,				size=224			,				)
  
       return image_processor
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.dummy_prior
       A__ =       self.dummy_image_encoder
       A__ =       self.dummy_text_encoder
       A__ =       self.dummy_tokenizer
       A__ =       self.dummy_image_processor
  
       A__ =       UnCLIPScheduler(
           variance_type="fixed_small_log"			,				prediction_type="sample"			,				num_train_timesteps=1_000			,				clip_sample=snake_case_			,				clip_sample_range=10.0			,				)
  
       A__ =       {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }
  
       return components
  
  
  
  
      def 							__magic_name__							(					self		: str			,				snake_case_		: int			,				snake_case_		: Optional[int]=0   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       if str(snake_case_   ).startswith("mps"   ):
        A__ =       torch.manual_seed(snake_case_   )
       else:
        A__ =       torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
       A__ =       {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs
  
  
  
  
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__ =       "cpu"
  
       A__ =       self.get_dummy_components()
  
       A__ =       self.pipeline_class(**snake_case_   )
       A__ =       pipe.to(snake_case_   )
  
       pipe.set_progress_bar_config(disable=snake_case_   )
  
       A__ =       pipe(**self.get_dummy_inputs(snake_case_   )   )
       A__ =       output.image_embeds
  
       A__ =       pipe(
           **self.get_dummy_inputs(snake_case_   )			,				return_dict=snake_case_			,				)[0]
  
       A__ =       image[0, -10:]
       A__ =       image_from_tuple[0, -10:]
  
       assert image.shape == (1, 32)
  
       A__ =       np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]   )
  
       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
  
  
  
  
      @skip_mps
      def 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       torch_device == "cpu"
       A__ =       True
       A__ =       False
  
       self._test_inference_batch_single_identical(
           test_max_difference=snake_case_			,				relax_max_difference=snake_case_			,				test_mean_pixel_difference=snake_case_			,				)
  
  
  
  
      @skip_mps
      def 							__magic_name__							(					self		: Optional[Any]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       torch_device == "cpu"
       A__ =       False
  
       self._test_attention_slicing_forward_pass(
           test_max_difference=snake_case_			,				test_mean_pixel_difference=snake_case_			,				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = 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.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  