


'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

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




	"""simple docstring"""





	lowerCAmelCase							,   lowerCAmelCase					:    Union[str, Any]      							= image.size
	lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	lowerCAmelCase					:    int      							= image.resize((w, h)					,   resample=PIL_INTERPOLATION['lanczos']				)
	lowerCAmelCase					:    List[Any]      							= np.array(UpperCamelCase__				).astype(np.floataa				) / 2_55.0
	lowerCAmelCase					:    Tuple      							= image[None].transpose(0					,   3					,   1					,   2				)
	lowerCAmelCase					:    List[str]      							= torch.from_numpy(UpperCamelCase__				)
	return 2.0 * image - 1.0






class  lowerCAmelCase				(				lowercase_							):



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					):
		super().__init__()
		self.register_modules(vqvae=snake_case__ ,					unet=snake_case__ ,					scheduler=snake_case__    )



	@torch.no_grad()
	def __call__(					self ,					snake_case__ = None ,					snake_case__ = 1 ,					snake_case__ = 100 ,					snake_case__ = 0.0 ,					snake_case__ = None ,					snake_case__ = "pil" ,					snake_case__ = True ,					):
		if isinstance(snake_case__ ,					PIL.Image.Image    ):
			lowerCAmelCase					:    List[Any]      							= 1
		elif isinstance(snake_case__ ,					torch.Tensor    ):
			lowerCAmelCase					:    Any      							= image.shape[0]
		else:
			raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case__    )}"    )

		if isinstance(snake_case__ ,					PIL.Image.Image    ):
			lowerCAmelCase					:    Any      							= preprocess(snake_case__    )

		lowerCAmelCase							,   lowerCAmelCase					:    int      							= image.shape[-2:]

		# in_channels should be 6: 3 for latents, 3 for low resolution image
		lowerCAmelCase					:    Tuple      							= (batch_size, self.unet.config.in_channels // 2, height, width)
		lowerCAmelCase					:    str      							= next(self.unet.parameters()    ).dtype

		lowerCAmelCase					:    Union[str, Any]      							= randn_tensor(snake_case__ ,					generator=snake_case__ ,					device=self.device ,					dtype=snake_case__    )

		lowerCAmelCase					:    Any      							= image.to(device=self.device ,					dtype=snake_case__    )

		# set timesteps and move to the correct device
		self.scheduler.set_timesteps(snake_case__ ,					device=self.device    )
		lowerCAmelCase					:    Tuple      							= self.scheduler.timesteps

		# scale the initial noise by the standard deviation required by the scheduler
		lowerCAmelCase					:    Tuple      							= latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		lowerCAmelCase					:    List[Any]      							= 'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
		lowerCAmelCase					:    Tuple      							= {}
		if accepts_eta:
			lowerCAmelCase					:    List[Any]      							= eta

		for t in self.progress_bar(snake_case__    ):
			# concat latents and low resolution image in the channel dimension.
			lowerCAmelCase					:    Optional[Any]      							= torch.cat([latents, image] ,					dim=1    )
			lowerCAmelCase					:    Optional[int]      							= self.scheduler.scale_model_input(snake_case__ ,					snake_case__    )
			# predict the noise residual
			lowerCAmelCase					:    Optional[int]      							= self.unet(snake_case__ ,					snake_case__    ).sample
			# compute the previous noisy sample x_t -> x_t-1
			lowerCAmelCase					:    Union[str, Any]      							= self.scheduler.step(snake_case__ ,					snake_case__ ,					snake_case__ ,					**snake_case__    ).prev_sample

		# decode the image latents with the VQVAE
		lowerCAmelCase					:    str      							= self.vqvae.decode(snake_case__    ).sample
		lowerCAmelCase					:    Optional[Any]      							= torch.clamp(snake_case__ ,					-1.0 ,					1.0    )
		lowerCAmelCase					:    int      							= image / 2 + 0.5
		lowerCAmelCase					:    Tuple      							= image.cpu().permute(0 ,					2 ,					3 ,					1    ).numpy()

		if output_type == "pil":
			lowerCAmelCase					:    Dict      							= self.numpy_to_pil(snake_case__    )

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=snake_case__    )




'''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,
				)
