

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





     hf_model.apply_weight_norm()

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

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

               lowercase__    :   Tuple      =       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
               lowercase__    :   Dict      =       checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
               lowercase__    :   int      =       checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

     lowercase__    :   Tuple      =       checkpoint['output_conv.1.weight_g']
     lowercase__    :   Any      =       checkpoint['output_conv.1.weight_v']
     lowercase__    :   Optional[Any]      =       checkpoint['output_conv.1.bias']

     hf_model.remove_weight_norm()

@torch.no_grad()
def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    List[Any]=None	,     SCREAMING_SNAKE_CASE_		:    Optional[int]=None	,     ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__    :   Optional[int]      =       SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
     else:
          lowercase__    :   Any      =       SpeechTaHifiGanConfig()

     lowercase__    :   Optional[int]      =       SpeechTaHifiGan(SCREAMING_SNAKE_CASE_			)

     lowercase__    :   List[Any]      =       torch.load(SCREAMING_SNAKE_CASE_			)
     load_weights(orig_checkpoint['model']['generator']	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

     lowercase__    :   Dict      =       np.load(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   int      =       stats[0].reshape(-1			)
     lowercase__    :   Any      =       stats[1].reshape(-1			)
     lowercase__    :   List[Any]      =       torch.from_numpy(SCREAMING_SNAKE_CASE_			).float()
     lowercase__    :   List[str]      =       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,
       )




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

