


'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowercase    :    Optional[Any]			     =  logging.get_logger("""transformers.models.speecht5""")


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    int   ,     A     :    Union[str, Any]  ):




						'''simple docstring'''






						hf_model.apply_weight_norm()

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

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

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

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

						hf_model.remove_weight_norm()


@torch.no_grad()
def    lowerCamelCase__   ( A     :    str   ,     A     :    Dict   ,     A     :    int   ,     A     :    Union[str, Any]=None   ,     A     :    Optional[Any]=None   ,     ):




						'''simple docstring'''






						if config_path is not None:
												UpperCAmelCase         =			SpeechTaHifiGanConfig.from_pretrained(A  )
						else:
												UpperCAmelCase         =			SpeechTaHifiGanConfig()

						UpperCAmelCase         =			SpeechTaHifiGan(A  )

						UpperCAmelCase         =			torch.load(A  )
						load_weights(orig_checkpoint['''model''']['''generator''']   ,     A   ,     A  )

						UpperCAmelCase         =			np.load(A  )
						UpperCAmelCase         =			stats[0].reshape(-1  )
						UpperCAmelCase         =			stats[1].reshape(-1  )
						UpperCAmelCase         =			torch.from_numpy(A  ).float()
						UpperCAmelCase         =			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    :    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."""
	)

	_lowercase    :    Optional[Any]			     =  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'''



import functools


def    lowerCamelCase__   ( A     :    list[int]   ,     A     :    list[int]  ):




						'''simple docstring'''






						if not isinstance(A   ,     A  ) or not all(isinstance(A   ,     A  ) for day in days  ):
												raise ValueError('''The parameter days should be a list of integers'''  )

						if len(A  ) != 3 or not all(isinstance(A   ,     A  ) for cost in costs  ):
												raise ValueError('''The parameter costs should be a list of three integers'''  )

						if len(A  ) == 0:
												return 0

						if min(A  ) <= 0:
												raise ValueError('''All days elements should be greater than 0'''  )

						if max(A  ) >= 3_66:
												raise ValueError('''All days elements should be less than 366'''  )

						UpperCAmelCase         =			set(A  )

						@functools.cache
						def dynamic_programming(A     :    int  ) -> int:
												if index > 3_65:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1  )

												return min(
												    costs[0] + dynamic_programming(index + 1  )   ,     costs[1] + dynamic_programming(index + 7  )   ,     costs[2] + dynamic_programming(index + 30  )   ,     )

						return dynamic_programming(1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





