


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'''



# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_lowercase    :    Union[str, Any]			     =  """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)





