


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
snake_case							:       str										=	logging.get_logger('''transformers.models.speecht5''')
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      hf_model.apply_weight_norm()

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

      for i in range(len(config.upsample_rates		)		):
            a		:List[str]									=			checkpoint[F'''upsamples.{i}.1.weight_g''']
            a		:Dict									=			checkpoint[F'''upsamples.{i}.1.weight_v''']
            a		:Tuple									=			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		)		):
                  a		:Any									=			checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
                  a		:Dict									=			checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
                  a		:Tuple									=			checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

                  a		:List[Any]									=			checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
                  a		:List[Any]									=			checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
                  a		:int									=			checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

      a		:Any									=			checkpoint['''output_conv.1.weight_g''']
      a		:Tuple									=			checkpoint['''output_conv.1.weight_v''']
      a		:Tuple									=			checkpoint['''output_conv.1.bias''']

      hf_model.remove_weight_norm()
@torch.no_grad()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						int=None					,    UpperCAmelCase_		:						int=None					,    ):




      """simple docstring"""





      if config_path is not None:
            a		:Dict									=			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_		)
      else:
            a		:str									=			SpeechTaHifiGanConfig()

      a		:str									=			SpeechTaHifiGan(UpperCAmelCase_		)

      a		:Union[str, Any]									=			torch.load(UpperCAmelCase_		)
      load_weights(orig_checkpoint['''model''']['''generator''']					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      a		:str									=			np.load(UpperCAmelCase_		)
      a		:List[str]									=			stats[0].reshape(-1		)
      a		:str									=			stats[1].reshape(-1		)
      a		:str									=			torch.from_numpy(UpperCAmelCase_		).float()
      a		: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__":
     snake_case							:       Optional[int]										=	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.'''
     )

     snake_case							:       Dict										=	parser.parse_args()
     convert_hifigan_checkpoint(
         args.checkpoint_path,
         args.stats_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )









import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			UNetaDModel(
           block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2







@slow
@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2





