
"""simple docstring"""







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	       =      logging.get_logger("""transformers.models.speecht5""")
def   __lowercase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							hf_model.apply_weight_norm()

							__snake_case     			=       checkpoint["input_conv.weight_g"]
							__snake_case     			=       checkpoint["input_conv.weight_v"]
							__snake_case     			=       checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates ) ):
														__snake_case     			=       checkpoint[F'''upsamples.{i}.1.weight_g''']
														__snake_case     			=       checkpoint[F'''upsamples.{i}.1.weight_v''']
														__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 ) ):
																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
																					__snake_case     			=       checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

							__snake_case     			=       checkpoint["output_conv.1.weight_g"]
							__snake_case     			=       checkpoint["output_conv.1.weight_v"]
							__snake_case     			=       checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()
@torch.no_grad()
def   __lowercase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=None							,    )  ->     List[Any]:







							"""simple docstring"""


							if config_path is not None:
														__snake_case     			=       SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							else:
														__snake_case     			=       SpeechTaHifiGanConfig()

							__snake_case     			=       SpeechTaHifiGan(SCREAMING_SNAKE_CASE )

							__snake_case     			=       torch.load(SCREAMING_SNAKE_CASE )
							load_weights(orig_checkpoint["model"]["generator"]							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							__snake_case     			=       np.load(SCREAMING_SNAKE_CASE )
							__snake_case     			=       stats[0].reshape(-1 )
							__snake_case     			=       stats[1].reshape(-1 )
							__snake_case     			=       torch.from_numpy(SCREAMING_SNAKE_CASE ).float()
							__snake_case     			=       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__":
		_SCREAMING_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."""
		)

		_SCREAMING_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,
		)


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     list:







							"""simple docstring"""


							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       collection[i]
														__snake_case     			=       0
														__snake_case     			=       i - 1

														while low <= high:
																					__snake_case     			=       (low + high) // 2
																					if val < collection[mid]:
																												__snake_case     			=       mid - 1
																					else:
																												__snake_case     			=       mid + 1
														for j in range(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    -1 ):
																					__snake_case     			=       collection[j - 1]
														__snake_case     			=       val
							return collection


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      input("""Enter numbers separated by a comma:\n""").strip()
		_SCREAMING_SNAKE_CASE	       =      [int(item) for item in user_input.split(""",""")]
		print(binary_insertion_sort(unsorted))
