




'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_			=						logging.get_logger('transformers.models.speecht5')
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]	,					__A    :			str	,					__A    :			List[str]  )			->			Tuple:
   hf_model.apply_weight_norm()

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

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

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

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

   hf_model.remove_weight_norm()
@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			Dict	,					__A    :			Optional[Any]=None	,					__A    :			Tuple=None	,					)			->			Tuple:
   if config_path is not None:
      _SCREAMING_SNAKE_CASE	=				SpeechTaHifiGanConfig.from_pretrained(__A  )
   else:
      _SCREAMING_SNAKE_CASE	=				SpeechTaHifiGanConfig()

   _SCREAMING_SNAKE_CASE	=				SpeechTaHifiGan(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.load(__A  )
   load_weights(orig_checkpoint["model"]["generator"]	,					__A	,					__A  )

   _SCREAMING_SNAKE_CASE	=				np.load(__A  )
   _SCREAMING_SNAKE_CASE	=				stats[0].reshape(-1  )
   _SCREAMING_SNAKE_CASE	=				stats[1].reshape(-1  )
   _SCREAMING_SNAKE_CASE	=				torch.from_numpy(__A  ).float()
   _SCREAMING_SNAKE_CASE	=				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_			=						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_			=						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'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

