




"""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_			,				)











"""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)





