

'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     str						 =			checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates     ) * len(config.resblock_kernel_sizes     )     ):
  for j in range(len(config.resblock_dilation_sizes     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
 parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )

 lowercase__         =      parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )







'''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_	)




