






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


snake_case     					=		False


class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Any=32   ):
       set_seed(0   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     UNetaDModel(sample_size=UpperCAmelCase_       ,			in_channels=3       ,			out_channels=3   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     torch.optim.SGD(model.parameters()       ,			lr=0.0_001   )
       return model, optimizer






     @slow
     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   str						    =     "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     DDPMScheduler(
           num_train_timesteps=1000       ,			beta_start=0.0_001       ,			beta_end=0.02       ,			beta_schedule="linear"       ,			clip_sample=UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   List[str]						    =     DDIMScheduler(
           num_train_timesteps=1000       ,			beta_start=0.0_001       ,			beta_end=0.02       ,			beta_schedule="linear"       ,			clip_sample=UpperCAmelCase_       ,			)

       assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

       # shared batches for DDPM and DDIM
       set_seed(0   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [torch.randn((4, 3, 32, 32)   ).clip(-1       ,			1   ).to(UpperCAmelCase_   ) for _ in range(4   )]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [torch.randn((4, 3, 32, 32)   ).to(UpperCAmelCase_   ) for _ in range(4   )]
       SCREAMING_SNAKE_CASE							:   Any						    =     [torch.randint(0       ,			1000       ,			(4,)   ).long().to(UpperCAmelCase_   ) for _ in range(4   )]

       # train with a DDPM scheduler
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Dict						    =     self.get_model_optimizer(resolution=32   )
       model.train().to(UpperCAmelCase_   )
       for i in range(4   ):
         optimizer.zero_grad()
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ddpm_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]   )
         SCREAMING_SNAKE_CASE							:   str						    =     model(UpperCAmelCase_       ,			timesteps[i]   ).sample
         SCREAMING_SNAKE_CASE							:   str						    =     torch.nn.functional.mse_loss(UpperCAmelCase_       ,			noise[i]   )
         loss.backward()
         optimizer.step()
       del model, optimizer

       # recreate the model and optimizer, and retry with DDIM
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     self.get_model_optimizer(resolution=32   )
       model.train().to(UpperCAmelCase_   )
       for i in range(4   ):
         optimizer.zero_grad()
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ddim_scheduler.add_noise(clean_images[i]       ,			noise[i]       ,			timesteps[i]   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     model(UpperCAmelCase_       ,			timesteps[i]   ).sample
         SCREAMING_SNAKE_CASE							:   int						    =     torch.nn.functional.mse_loss(UpperCAmelCase_       ,			noise[i]   )
         loss.backward()
         optimizer.step()
       del model, optimizer

       self.assertTrue(torch.allclose(UpperCAmelCase_       ,			UpperCAmelCase_       ,			atol=1E-5   )   )
       self.assertTrue(torch.allclose(UpperCAmelCase_       ,			UpperCAmelCase_       ,			atol=1E-5   )   )












'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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    )    ):
                  SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['output_conv.1.weight_v']
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint['output_conv.1.bias']

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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__":
      UpperCamelCase_    	=				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."
      )

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


