



'''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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  hf_model.apply_weight_norm()

  __lowerCamelCase			:			List[str]       			=					checkpoint['input_conv.weight_g']
  __lowerCamelCase			:			Tuple       			=					checkpoint['input_conv.weight_v']
  __lowerCamelCase			:			List[Any]       			=					checkpoint['input_conv.bias']

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

      __lowerCamelCase			:			Optional[Any]       			=					checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
      __lowerCamelCase			:			Union[str, Any]       			=					checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
      __lowerCamelCase			:			List[Any]       			=					checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

  __lowerCamelCase			:			Tuple       			=					checkpoint['output_conv.1.weight_g']
  __lowerCamelCase			:			Any       			=					checkpoint['output_conv.1.weight_v']
  __lowerCamelCase			:			Tuple       			=					checkpoint['output_conv.1.bias']

  hf_model.remove_weight_norm()

@torch.no_grad()
def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=None			,_lowerCAmelCase=None			,) -> Any:
  if config_path is not None:
    __lowerCamelCase			:			Optional[int]       			=					SpeechTaHifiGanConfig.from_pretrained(__lowerCAmelCase			)
  else:
    __lowerCamelCase			:			str       			=					SpeechTaHifiGanConfig()

  __lowerCamelCase			:			str       			=					SpeechTaHifiGan(__lowerCAmelCase			)

  __lowerCamelCase			:			int       			=					torch.load(__lowerCAmelCase			)
  load_weights(orig_checkpoint['model']['generator']			,__lowerCAmelCase			,__lowerCAmelCase			)

  __lowerCamelCase			:			Tuple       			=					np.load(__lowerCAmelCase			)
  __lowerCamelCase			:			List[str]       			=					stats[0].reshape(-1			)
  __lowerCamelCase			:			Tuple       			=					stats[1].reshape(-1			)
  __lowerCamelCase			:			List[str]       			=					torch.from_numpy(__lowerCAmelCase			).float()
  __lowerCamelCase			:			int       			=					torch.from_numpy(__lowerCAmelCase			).float()

  model.save_pretrained(__lowerCAmelCase			)

  if repo_id:
    print('Pushing to the hub...'			)
    model.push_to_hub(__lowerCAmelCase			)


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 os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__  :  Tuple					=       data_utils.TransfoXLTokenizer
__magic_name__  :  int					=       data_utils.TransfoXLCorpus
__magic_name__  :  List[str]					=       data_utils
__magic_name__  :  str					=       data_utils
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(__lowerCAmelCase   , '''rb'''				) as fp:
      lowerCAmelCase__										=      pickle.load(__lowerCAmelCase   , encoding='''latin1'''				)
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
    print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""				)
    lowerCAmelCase__										=      corpus.vocab.__dict__
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

    lowerCAmelCase__										=      corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
    print(F"""Save dataset to {pytorch_dataset_dump_path}"""				)
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)

    print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase__										=      TransfoXLConfig()
    else:
      lowerCAmelCase__										=      TransfoXLConfig.from_json_file(__lowerCAmelCase				)
    print(F"""Building PyTorch model from configuration: {config}"""				)
    lowerCAmelCase__										=      TransfoXLLMHeadModel(__lowerCAmelCase				)

    lowerCAmelCase__										=      load_tf_weights_in_transfo_xl(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)
    # Save pytorch-model
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    print(F"""Save PyTorch model to {os.path.abspath(__lowerCAmelCase				)}"""				)
    torch.save(model.state_dict()   , __lowerCAmelCase				)
    print(F"""Save configuration file to {os.path.abspath(__lowerCAmelCase				)}"""				)
    with open(__lowerCAmelCase   , '''w'''   , encoding='''utf-8'''				) as f:
      f.write(config.to_json_string()				)


if __name__ == "__main__":
      __magic_name__  :  int					=       argparse.ArgumentParser()
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
      )
      parser.add_argument(
          """--tf_checkpoint_path""",
          default="""""",
          type=str,
          help="""An optional path to a TensorFlow checkpoint path to be converted.""",
      )
      parser.add_argument(
          """--transfo_xl_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--transfo_xl_dataset_file""",
          default="""""",
          type=str,
          help="""An optional dataset file to be converted in a vocabulary.""",
      )
      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(
          args.tf_checkpoint_path,
          args.transfo_xl_config_file,
          args.pytorch_dump_folder_path,
          args.transfo_xl_dataset_file,
      )

