


import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a : Dict		=				logging.get_logger('''transformers.models.speecht5''')


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			hf_model.apply_weight_norm()

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

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

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

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

			hf_model.remove_weight_norm()


@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=None       ,			_UpperCamelCase=None       ,			):


			'''simple docstring'''







			if config_path is not None:
						__lowercase 					=   SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase  )
			else:
						__lowercase 					=   SpeechTaHifiGanConfig()

			__lowercase 					=   SpeechTaHifiGan(_UpperCamelCase  )

			__lowercase 					=   torch.load(_UpperCamelCase  )
			load_weights(orig_checkpoint['''model''']['''generator''']       ,			_UpperCamelCase       ,			_UpperCamelCase  )

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

			model.save_pretrained(_UpperCamelCase  )

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


if __name__ == "__main__":
		a : Dict		=				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.'''
		)

		a : List[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 dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)
@dataclass
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .pipeline_if import IFPipeline
	from .pipeline_if_imgaimg import IFImgaImgPipeline
	from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
	from .pipeline_if_inpainting import IFInpaintingPipeline
	from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
	from .pipeline_if_superresolution import IFSuperResolutionPipeline
	from .safety_checker import IFSafetyChecker
	from .watermark import IFWatermarker


