



'''simple docstring'''






import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger("transformers.models.speecht5")


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   List[Any]:
							hf_model.apply_weight_norm()

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

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

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

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

							hf_model.remove_weight_norm()


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Union[str, Any]=None   ,      _lowerCAmelCase  :						List[str]=None   ,      )      ->   List[Any]:
							if config_path is not None:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       Optional[Any] 							=		SpeechTaHifiGanConfig()

							UpperCAmelCase :       Dict 							=		SpeechTaHifiGan(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		torch.load(_lowerCAmelCase  )
							load_weights(orig_checkpoint['''model''']['''generator''']   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Any 							=		np.load(_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		stats[0].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		stats[1].reshape(-1  )
							UpperCAmelCase :       Tuple 							=		torch.from_numpy(_lowerCAmelCase  ).float()
							UpperCAmelCase :       List[str] 							=		torch.from_numpy(_lowerCAmelCase  ).float()

							model.save_pretrained(_lowerCAmelCase  )

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


if __name__ == "__main__":
						UpperCamelCase__:   List[Any]								=			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."
						)

						UpperCamelCase__:   Optional[int]								=			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 itertools import product




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      list[int]:
							lowercase_					:			List[Any]										=       sides_number
							lowercase_					:			Dict										=       max_face_number * dice_number
							lowercase_					:			List[str]										=       [0] * (max_total + 1)

							lowercase_					:			Union[str, Any]										=       1
							lowercase_					:			Dict										=       range(UpperCAmelCase__      ,      max_face_number + 1 )
							for dice_numbers in product(UpperCAmelCase__      ,      repeat=UpperCAmelCase__ ):
														lowercase_					:			Any										=       sum(UpperCAmelCase__ )
														totals_frequencies[total] += 1

							return totals_frequencies




def 							lowerCamelCase	(      )			->      float:
							lowercase_					:			Optional[Any]										=       total_frequency_distribution(
							    sides_number=4      ,      dice_number=9 )
							lowercase_					:			List[str]										=       total_frequency_distribution(
							    sides_number=6      ,      dice_number=6 )

							lowercase_					:			Union[str, Any]										=       0
							lowercase_					:			Tuple										=       9
							lowercase_					:			Optional[int]										=       4 * 9
							lowercase_					:			List[Any]										=       6
							for peter_total in range(UpperCAmelCase__      ,      max_peter_total + 1 ):
														peter_wins_count += peter_totals_frequencies[peter_total] * sum(
														    colin_totals_frequencies[min_colin_total:peter_total] )

							lowercase_					:			str										=       (4**9) * (6**6)
							lowercase_					:			List[Any]										=       peter_wins_count / total_games_number

							lowercase_					:			Dict										=       round(UpperCAmelCase__      ,      ndigits=7 )

							return rounded_peter_win_probability


if __name__ == "__main__":
						print(f"""{solution() = }""")





