
'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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


def 		_UpperCAmelCase (     _lowerCamelCase  :				List[Any]     ,					_lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				Optional[Any]						)			->  List[Any]:
  hf_model.apply_weight_norm()

  _lowerCAmelCase				:     List[Any]       =    checkpoint["""input_conv.weight_g"""]
  _lowerCAmelCase				:     str       =    checkpoint["""input_conv.weight_v"""]
  _lowerCAmelCase				:     str       =    checkpoint["""input_conv.bias"""]

  for i in range(len(config.upsample_rates						)						):
    _lowerCAmelCase				:     List[Any]       =    checkpoint[f'upsamples.{i}.1.weight_g']
    _lowerCAmelCase				:     Tuple       =    checkpoint[f'upsamples.{i}.1.weight_v']
    _lowerCAmelCase				:     Dict       =    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						)						):
      _lowerCAmelCase				:     Tuple       =    checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
      _lowerCAmelCase				:     Optional[int]       =    checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
      _lowerCAmelCase				:     Tuple       =    checkpoint[f'blocks.{i}.convs1.{j}.1.bias']

      _lowerCAmelCase				:     str       =    checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
      _lowerCAmelCase				:     Any       =    checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
      _lowerCAmelCase				:     List[Any]       =    checkpoint[f'blocks.{i}.convs2.{j}.1.bias']

  _lowerCAmelCase				:     Any       =    checkpoint["""output_conv.1.weight_g"""]
  _lowerCAmelCase				:     List[str]       =    checkpoint["""output_conv.1.weight_v"""]
  _lowerCAmelCase				:     Optional[int]       =    checkpoint["""output_conv.1.bias"""]

  hf_model.remove_weight_norm()


@torch.no_grad()
def 		_UpperCAmelCase (     _lowerCamelCase  :				str     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				Optional[int]     ,					_lowerCamelCase  :				List[str]=None     ,					_lowerCamelCase  :				Optional[int]=None     ,					)			->  Tuple:






  if config_path is not None:
    _lowerCAmelCase				:     List[Any]       =    SpeechTaHifiGanConfig.from_pretrained(_snake_case						)
  else:
    _lowerCAmelCase				:     Optional[Any]       =    SpeechTaHifiGanConfig()

  _lowerCAmelCase				:     Optional[Any]       =    SpeechTaHifiGan(_snake_case						)

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

  _lowerCAmelCase				:     int       =    np.load(_snake_case						)
  _lowerCAmelCase				:     Any       =    stats[0].reshape(-1						)
  _lowerCAmelCase				:     List[str]       =    stats[1].reshape(-1						)
  _lowerCAmelCase				:     Optional[Any]       =    torch.from_numpy(_snake_case						).float()
  _lowerCAmelCase				:     Union[str, Any]       =    torch.from_numpy(_snake_case						).float()

  model.save_pretrained(_snake_case						)

  if repo_id:
    print("""Pushing to the hub..."""						)
    model.push_to_hub(_snake_case						)


if __name__ == "__main__":
     UpperCamelCase_   	=				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_   	=				parser.parse_args()
     convert_hifigan_checkpoint(
         args.checkpoint_path,
         args.stats_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			if openai_config_file == "":
						__a        =OpenAIGPTConfig()
			else:
						__a        =OpenAIGPTConfig.from_json_file(_snake_case	)
			__a        =OpenAIGPTModel(_snake_case	)

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(_snake_case       ,     _snake_case       ,     _snake_case	)

			# Save pytorch-model
			__a        =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			__a        =pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'Save PyTorch model to {pytorch_weights_dump_path}'	)
			torch.save(model.state_dict()       ,     _snake_case	)
			print(F'Save configuration file to {pytorch_config_dump_path}'	)
			with open(_snake_case       ,     'w'       ,     encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--openai_checkpoint_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--openai_config_file",
						    default="",
						    type=str,
						    help=(
						        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
						        "This specifies the model architecture."
						    ),
						)
						_lowerCAmelCase					:			int  				=			parser.parse_args()
						convert_openai_checkpoint_to_pytorch(
						    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
						)






