import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_    :		Optional[int]							=	logging.get_logger("""transformers.models.speecht5""")
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					hf_model.apply_weight_norm()

					__a		 =       checkpoint['input_conv.weight_g']
					__a		 =       checkpoint['input_conv.weight_v']
					__a		 =       checkpoint['input_conv.bias']

					for i in range(len(config.upsample_rates						)						):
										__a		 =       checkpoint[f'''upsamples.{i}.1.weight_g''']
										__a		 =       checkpoint[f'''upsamples.{i}.1.weight_v''']
										__a		 =       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		 =       checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
															__a		 =       checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
															__a		 =       checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

															__a		 =       checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
															__a		 =       checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
															__a		 =       checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

					__a		 =       checkpoint['output_conv.1.weight_g']
					__a		 =       checkpoint['output_conv.1.weight_v']
					__a		 =       checkpoint['output_conv.1.bias']

					hf_model.remove_weight_norm()
@torch.no_grad()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase=None				,    __lowerCamelCase=None				,    ):
					if config_path is not None:
										__a		 =       SpeechTaHifiGanConfig.from_pretrained(__lowerCamelCase						)
					else:
										__a		 =       SpeechTaHifiGanConfig()

					__a		 =       SpeechTaHifiGan(__lowerCamelCase						)

					__a		 =       torch.load(__lowerCamelCase						)
					load_weights(orig_checkpoint['model']['generator']				,    __lowerCamelCase				,    __lowerCamelCase						)

					__a		 =       np.load(__lowerCamelCase						)
					__a		 =       stats[0].reshape(-1						)
					__a		 =       stats[1].reshape(-1						)
					__a		 =       torch.from_numpy(__lowerCamelCase						).float()
					__a		 =       torch.from_numpy(__lowerCamelCase						).float()

					model.save_pretrained(__lowerCamelCase						)

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


if __name__ == "__main__":
	lowerCamelCase_    :		List[Any]							=	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."""
	)

	lowerCamelCase_    :		str							=	parser.parse_args()
	convert_hifigan_checkpoint(
	    args.checkpoint_path,
	    args.stats_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       '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 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




