


'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

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

			lowerCAmelCase					:    Optional[Any]      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"]
			lowerCAmelCase					:    Tuple      							= checkpoint[F"blocks.{i}.convs2.{j}.1.bias"]

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

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    int      							= 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__":
				_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  :		Union[str, Any]		     =	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 math
import sys

import cva
import numpy as np

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				int					,   _A    :				int					,   _A    :				int				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= np.zeros((kernel_size, kernel_size)				)
	for i in range(0					,   _A				):
		for j in range(0					,   _A				):
			lowerCAmelCase					:    Optional[int]      							= math.sqrt(
			    abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
	return vec_gaussian(_A					,   _A				)

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= np.zeros(img.shape				)
	lowerCAmelCase					:    int      							= get_gauss_kernel(_A					,   _A				)
	lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= img.shape
	for i in range(kernel_size // 2					,   size_x - kernel_size // 2				):
		for j in range(kernel_size // 2					,   size_y - kernel_size // 2				):
			lowerCAmelCase					:    int      							= get_slice(_A					,   _A					,   _A					,   _A				)
			lowerCAmelCase					:    Any      							= img_s - img_s[kernel_size // 2, kernel_size // 2]
			lowerCAmelCase					:    str      							= vec_gaussian(_A					,   _A				)
			lowerCAmelCase					:    Optional[int]      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    str      							= np.multiply(_A					,   _A				)
			lowerCAmelCase					:    Union[str, Any]      							= np.sum(_A				) / np.sum(_A				)
			lowerCAmelCase					:    Tuple      							= val
	return imga

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= float(args[2]				) if args[2:] else 1.0
	lowerCAmelCase					:    Union[str, Any]      							= float(args[3]				) if args[3:] else 1.0
	if args[4:]:
		lowerCAmelCase					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
				_lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase     ,      _lowerCAmelCase  :		Any		     =	parse_args(sys.argv)
				_lowerCAmelCase  :		str		     =	cva.imread(filename, 0)
				cva.imshow('input image', img)

				_lowerCAmelCase  :		Union[str, Any]		     =	img / 255
				_lowerCAmelCase  :		List[str]		     =	out.astype('float32')
				_lowerCAmelCase  :		Optional[int]		     =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
				_lowerCAmelCase  :		Union[str, Any]		     =	out * 255
				_lowerCAmelCase  :		Optional[Any]		     =	np.uinta(out)
				cva.imshow('output image', out)
				cva.waitKey(0)
				cva.destroyAllWindows()
