


"""simple docstring"""







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCAmelCase__      				=   logging.get_logger('''transformers.models.speecht5''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  hf_model.apply_weight_norm()

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

  for i in range(len(config.upsample_rates				)				):
    lowerCAmelCase     :     List[Any]								=							checkpoint[f"""upsamples.{i}.1.weight_g"""]
    lowerCAmelCase     :     List[str]								=							checkpoint[f"""upsamples.{i}.1.weight_v"""]
    lowerCAmelCase     :     str								=							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     :     str								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
      lowerCAmelCase     :     Tuple								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
      lowerCAmelCase     :     Any								=							checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

      lowerCAmelCase     :     Union[str, Any]								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
      lowerCAmelCase     :     List[str]								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
      lowerCAmelCase     :     int								=							checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

  lowerCAmelCase     :     Optional[Any]								=							checkpoint["output_conv.1.weight_g"]
  lowerCAmelCase     :     int								=							checkpoint["output_conv.1.weight_v"]
  lowerCAmelCase     :     int								=							checkpoint["output_conv.1.bias"]

  hf_model.remove_weight_norm()
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    List[str]=None				,      SCREAMING_SNAKE_CASE    :    Tuple=None				,      ):


  '''simple docstring'''







  if config_path is not None:
    lowerCAmelCase     :     Dict								=							SpeechTaHifiGanConfig.from_pretrained(__A				)
  else:
    lowerCAmelCase     :     List[str]								=							SpeechTaHifiGanConfig()

  lowerCAmelCase     :     Union[str, Any]								=							SpeechTaHifiGan(__A				)

  lowerCAmelCase     :     Tuple								=							torch.load(__A				)
  load_weights(orig_checkpoint["model"]["generator"]				,      __A				,      __A				)

  lowerCAmelCase     :     str								=							np.load(__A				)
  lowerCAmelCase     :     str								=							stats[0].reshape(-1				)
  lowerCAmelCase     :     int								=							stats[1].reshape(-1				)
  lowerCAmelCase     :     Tuple								=							torch.from_numpy(__A				).float()
  lowerCAmelCase     :     Dict								=							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__":
     lowerCAmelCase__      				=   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.'''
     )

     lowerCAmelCase__      				=   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'''







import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :							int      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xlnet"""
							__lowercase      		=       ["""mems"""]
							__lowercase      		=       {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(       self    ,   lowerCAmelCase_=3_20_00    ,   lowerCAmelCase_=10_24    ,   lowerCAmelCase_=24    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=40_96    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="bi"    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=-1    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_="last"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="tanh"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=2    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		vocab_size
								_snake_case   						=		d_model
								_snake_case   						=		n_layer
								_snake_case   						=		n_head
								if d_model % n_head != 0:
									raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'  )
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'  )
								_snake_case   						=		d_model // n_head
								_snake_case   						=		ff_activation
								_snake_case   						=		d_inner
								_snake_case   						=		untie_r
								_snake_case   						=		attn_type

								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps

								_snake_case   						=		dropout
								_snake_case   						=		mem_len
								_snake_case   						=		reuse_len
								_snake_case   						=		bi_data
								_snake_case   						=		clamp_len
								_snake_case   						=		same_length

								_snake_case   						=		summary_type
								_snake_case   						=		summary_use_proj
								_snake_case   						=		summary_activation
								_snake_case   						=		summary_last_dropout
								_snake_case   						=		start_n_top
								_snake_case   						=		end_n_top

								_snake_case   						=		bos_token_id
								_snake_case   						=		pad_token_id
								_snake_case   						=		eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'    ,   lowerCAmelCase_    ,   )
									_snake_case   						=		kwargs['use_cache']

								_snake_case   						=		use_mems_eval
								_snake_case   						=		use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )
								return -1




							@max_position_embeddings.setter
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								raise NotImplementedError(
								    F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )

