

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowercase     : Tuple       =						logging.get_logger('''transformers.models.speecht5''')







def       lowercase					(	__A :					Dict ,    __A :					Union[str, Any] ,    __A :					int		)				->			Any:





    '''simple docstring'''




    hf_model.apply_weight_norm()

    snake_case							:		Any        =		checkpoint["""input_conv.weight_g"""]
    snake_case							:		Any        =		checkpoint["""input_conv.weight_v"""]
    snake_case							:		Optional[Any]        =		checkpoint["""input_conv.bias"""]

    for i in range(len(config.upsample_rates		)		):
        snake_case							:		Optional[Any]        =		checkpoint[f"""upsamples.{i}.1.weight_g"""]
        snake_case							:		List[str]        =		checkpoint[f"""upsamples.{i}.1.weight_v"""]
        snake_case							:		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		)		):
            snake_case							:		Any        =		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
            snake_case							:		Union[str, Any]        =		checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
            snake_case							:		str        =		checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

            snake_case							:		Any        =		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
            snake_case							:		List[Any]        =		checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
            snake_case							:		str        =		checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

    snake_case							:		Union[str, Any]        =		checkpoint["""output_conv.1.weight_g"""]
    snake_case							:		Tuple        =		checkpoint["""output_conv.1.weight_v"""]
    snake_case							:		Dict        =		checkpoint["""output_conv.1.bias"""]

    hf_model.remove_weight_norm()







@torch.no_grad()
def       lowercase					(	__A :					List[Any] ,    __A :					Optional[Any] ,    __A :					List[str] ,    __A :					Optional[Any]=None ,    __A :					List[str]=None ,    )				->			str:





    '''simple docstring'''




    if config_path is not None:
        snake_case							:		str        =		SpeechTaHifiGanConfig.from_pretrained(__A		)
    else:
        snake_case							:		Tuple        =		SpeechTaHifiGanConfig()

    snake_case							:		Optional[Any]        =		SpeechTaHifiGan(__A		)

    snake_case							:		str        =		torch.load(__A		)
    load_weights(orig_checkpoint["""model"""]["""generator"""] ,    __A ,    __A		)

    snake_case							:		List[Any]        =		np.load(__A		)
    snake_case							:		Tuple        =		stats[0].reshape(-1		)
    snake_case							:		str        =		stats[1].reshape(-1		)
    snake_case							:		Optional[int]        =		torch.from_numpy(__A		).float()
    snake_case							:		Tuple        =		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__":
     __lowercase     : str       =						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.'''
     )

     __lowercase     : 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,
     )





import math

__magic_name__  							=	10
__magic_name__  							=	7
__magic_name__  							=	BALLS_PER_COLOUR * NUM_COLOURS





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 20				):
    snake_case__    =					math.comb(__lowerCAmelCase     ,		__lowerCAmelCase				)
    snake_case__    =					math.comb(NUM_BALLS - BALLS_PER_COLOUR     ,		__lowerCAmelCase				)

    snake_case__    =					NUM_COLOURS * (1 - missing_colour / total)

    return F"""{result:.9f}"""


if __name__ == "__main__":
     print(solution(20))



