


'''simple docstring'''
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__snake_case							      =     logging.get_logger('''transformers.models.speecht5''')


def 			a      (			__a ,					__a ,					__a   )  ->	str:



					'''simple docstring'''






					hf_model.apply_weight_norm()

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

					for i in range(len(config.upsample_rates   )   ):
										UpperCamelCase__   :Optional[Any]												= checkpoint[f'''upsamples.{i}.1.weight_g''']
										UpperCamelCase__   :List[str]												= checkpoint[f'''upsamples.{i}.1.weight_v''']
										UpperCamelCase__   :Dict												= 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__   :int												= checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
															UpperCamelCase__   :Dict												= checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
															UpperCamelCase__   :Any												= checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

															UpperCamelCase__   :Union[str, Any]												= checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
															UpperCamelCase__   :Optional[Any]												= checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
															UpperCamelCase__   :str												= checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

					UpperCamelCase__   :Tuple												= checkpoint['''output_conv.1.weight_g''']
					UpperCamelCase__   :Dict												= checkpoint['''output_conv.1.weight_v''']
					UpperCamelCase__   :List[str]												= checkpoint['''output_conv.1.bias''']

					hf_model.remove_weight_norm()


@torch.no_grad()
def 			a      (			__a ,					__a ,					__a ,					__a=None ,					__a=None ,					)  ->	str:



					'''simple docstring'''






					if config_path is not None:
										UpperCamelCase__   :int												= SpeechTaHifiGanConfig.from_pretrained(__a   )
					else:
										UpperCamelCase__   :int												= SpeechTaHifiGanConfig()

					UpperCamelCase__   :Any												= SpeechTaHifiGan(__a   )

					UpperCamelCase__   :Tuple												= torch.load(__a   )
					load_weights(orig_checkpoint['''model''']['''generator'''] ,					__a ,					__a   )

					UpperCamelCase__   :Optional[int]												= np.load(__a   )
					UpperCamelCase__   :int												= stats[0].reshape(-1   )
					UpperCamelCase__   :Optional[int]												= stats[1].reshape(-1   )
					UpperCamelCase__   :str												= torch.from_numpy(__a   ).float()
					UpperCamelCase__   :List[Any]												= torch.from_numpy(__a   ).float()

					model.save_pretrained(__a   )

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


if __name__ == "__main__":
		__snake_case							      =     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							      =     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'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp