

'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     str						 =			UniSpeechSatForSequenceClassification.from_pretrained(__snake_case  ,	config=__snake_case     )
 UpperCAmelCase	:     Optional[Any]						 =			downstream_dict["projector.weight"]
 UpperCAmelCase	:     List[str]						 =			downstream_dict["projector.bias"]
 UpperCAmelCase	:     Dict						 =			downstream_dict["model.post_net.linear.weight"]
 UpperCAmelCase	:     int						 =			downstream_dict["model.post_net.linear.bias"]
 return model




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case  ,	config=__snake_case     )
 UpperCAmelCase	:     Tuple						 =			downstream_dict["model.linear.weight"]
 UpperCAmelCase	:     List[str]						 =			downstream_dict["model.linear.bias"]
 return model




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			UniSpeechSatForXVector.from_pretrained(__snake_case  ,	config=__snake_case     )
 UpperCAmelCase	:     Tuple						 =			downstream_dict["connector.weight"]
 UpperCAmelCase	:     Union[str, Any]						 =			downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
  UpperCAmelCase	:     str						 =			downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  UpperCAmelCase	:     Union[str, Any]						 =			downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

 UpperCAmelCase	:     List[Any]						 =			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
 UpperCAmelCase	:     Tuple						 =			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
 UpperCAmelCase	:     Optional[int]						 =			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
 UpperCAmelCase	:     Any						 =			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
 UpperCAmelCase	:     Any						 =			downstream_dict["objective.W"]
 return model




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			torch.load(__snake_case  ,	map_location='cpu'     )

 UpperCAmelCase	:     str						 =			checkpoint["Downstream"]

 UpperCAmelCase	:     Tuple						 =			UniSpeechSatConfig.from_pretrained(__snake_case     )
 UpperCAmelCase	:     List[str]						 =			WavaVecaFeatureExtractor.from_pretrained(
     __snake_case  ,	return_attention_mask=__snake_case  ,	do_normalize=__snake_case     )

 UpperCAmelCase	:     Union[str, Any]						 =			hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'     ):
  UpperCAmelCase	:     Optional[int]						 =			convert_classification(__snake_case  ,	__snake_case  ,	__snake_case     )
 elif arch.endswith('ForAudioFrameClassification'     ):
  UpperCAmelCase	:     Optional[Any]						 =			convert_diarization(__snake_case  ,	__snake_case  ,	__snake_case     )
 elif arch.endswith('ForXVector'     ):
  UpperCAmelCase	:     Any						 =			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	:     Tuple						 =			checkpoint["Featurizer"]["weights"]

 hf_feature_extractor.save_pretrained(__snake_case     )
 hf_model.save_pretrained(__snake_case     )


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







'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




