



'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				hf_model.apply_weight_norm()

				SCREAMING_SNAKE_CASE  :						List[Any]      					= checkpoint['''input_conv.weight_g''']
				SCREAMING_SNAKE_CASE  :						Any      					= checkpoint['''input_conv.weight_v''']
				SCREAMING_SNAKE_CASE  :						int      					= checkpoint['''input_conv.bias''']

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

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

				SCREAMING_SNAKE_CASE  :						Any      					= checkpoint['''output_conv.1.weight_g''']
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= checkpoint['''output_conv.1.weight_v''']
				SCREAMING_SNAKE_CASE  :						Any      					= checkpoint['''output_conv.1.bias''']

				hf_model.remove_weight_norm()






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Tuple=None    ,	_SCREAMING_SNAKE_CASE      :str=None    ,	):
				if config_path is not None:
								SCREAMING_SNAKE_CASE  :						str      					= SpeechTaHifiGanConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
				else:
								SCREAMING_SNAKE_CASE  :						str      					= SpeechTaHifiGanConfig()

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SpeechTaHifiGan(_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						int      					= torch.load(_SCREAMING_SNAKE_CASE       )
				load_weights(orig_checkpoint['''model''']['''generator''']    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Dict      					= np.load(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Dict      					= stats[0].reshape(-1       )
				SCREAMING_SNAKE_CASE  :						Any      					= stats[1].reshape(-1       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.from_numpy(_SCREAMING_SNAKE_CASE       ).float()
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.from_numpy(_SCREAMING_SNAKE_CASE       ).float()

				model.save_pretrained(_SCREAMING_SNAKE_CASE       )

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


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 typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




