



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase_								=     logging.get_logger("""transformers.models.speecht5""")



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			hf_model.apply_weight_norm()

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

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

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

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

			hf_model.remove_weight_norm()



@torch.no_grad()
def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_=None						,  SCREAMING_SNAKE_CASE_=None						,  ):
			if config_path is not None:
						lowercase__					  =				SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
			else:
						lowercase__					  =				SpeechTaHifiGanConfig()

			lowercase__					  =				SpeechTaHifiGan(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				torch.load(SCREAMING_SNAKE_CASE_  )
			load_weights(orig_checkpoint["model"]["generator"]						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				np.load(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				stats[0].reshape(-1  )
			lowercase__					  =				stats[1].reshape(-1  )
			lowercase__					  =				torch.from_numpy(SCREAMING_SNAKE_CASE_  ).float()
			lowercase__					  =				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__":
							lowercase_								=     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_								=     parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def __lowerCAmelCase (     ):
			lowercase__					  =				ArgumentParser("Accelerate CLI tool"						,  usage="accelerate <command> [<args>]"						,  allow_abbrev=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.add_subparsers(help="accelerate command helpers"  )

			# Register commands
			get_config_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			env_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			launch_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			tpu_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )
			test_command_parser(subparsers=SCREAMING_SNAKE_CASE_  )

			# Let's go
			lowercase__					  =				parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_						,  "func"  ):
						parser.print_help()
						exit(1  )

			# Run
			args.func(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
							main()




