



"""simple docstring"""
import warnings

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


_UpperCAmelCase     =			logging.get_logger(__name__)

_UpperCAmelCase     =			{
    """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 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Optional[int]     						=					'xlnet'
  UpperCamelCase	:				List[str]     						=					['mems']
  UpperCamelCase	:				int     						=					{
      'n_token': 'vocab_size',  # Backward compatibility
      'hidden_size': 'd_model',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }



  def __init__(						self :					int       ,		lowerCAmelCase :					Optional[int]=3_2000       ,		lowerCAmelCase :					List[Any]=1024       ,		lowerCAmelCase :					Dict=24       ,		lowerCAmelCase :					Union[str, Any]=16       ,		lowerCAmelCase :					Dict=4096       ,		lowerCAmelCase :					Optional[int]="gelu"       ,		lowerCAmelCase :					Optional[int]=True       ,		lowerCAmelCase :					Tuple="bi"       ,		lowerCAmelCase :					Tuple=0.0_2       ,		lowerCAmelCase :					List[str]=1E-12       ,		lowerCAmelCase :					Union[str, Any]=0.1       ,		lowerCAmelCase :					Union[str, Any]=512       ,		lowerCAmelCase :					Optional[Any]=None       ,		lowerCAmelCase :					int=True       ,		lowerCAmelCase :					Tuple=False       ,		lowerCAmelCase :					Tuple=False       ,		lowerCAmelCase :					List[Any]=-1       ,		lowerCAmelCase :					int=False       ,		lowerCAmelCase :					int="last"       ,		lowerCAmelCase :					int=True       ,		lowerCAmelCase :					Any="tanh"       ,		lowerCAmelCase :					Dict=0.1       ,		lowerCAmelCase :					int=5       ,		lowerCAmelCase :					int=5       ,		lowerCAmelCase :					Dict=5       ,		lowerCAmelCase :					List[str]=1       ,		lowerCAmelCase :					Union[str, Any]=2       ,		**lowerCAmelCase :					Dict       ,		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=vocab_size
     SCREAMING_SNAKE_CASE_: Tuple     			=d_model
     SCREAMING_SNAKE_CASE_: Optional[int]     			=n_layer
     SCREAMING_SNAKE_CASE_: int     			=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})'''		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=d_model // n_head
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=ff_activation
     SCREAMING_SNAKE_CASE_: Any     			=d_inner
     SCREAMING_SNAKE_CASE_: List[Any]     			=untie_r
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=attn_type

     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: Dict     			=layer_norm_eps

     SCREAMING_SNAKE_CASE_: List[Any]     			=dropout
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=mem_len
     SCREAMING_SNAKE_CASE_: Optional[int]     			=reuse_len
     SCREAMING_SNAKE_CASE_: Optional[int]     			=bi_data
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=clamp_len
     SCREAMING_SNAKE_CASE_: Tuple     			=same_length

     SCREAMING_SNAKE_CASE_: int     			=summary_type
     SCREAMING_SNAKE_CASE_: List[str]     			=summary_use_proj
     SCREAMING_SNAKE_CASE_: str     			=summary_activation
     SCREAMING_SNAKE_CASE_: Tuple     			=summary_last_dropout
     SCREAMING_SNAKE_CASE_: Optional[int]     			=start_n_top
     SCREAMING_SNAKE_CASE_: List[Any]     			=end_n_top

     SCREAMING_SNAKE_CASE_: int     			=bos_token_id
     SCREAMING_SNAKE_CASE_: Any     			=pad_token_id
     SCREAMING_SNAKE_CASE_: List[Any]     			=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       ,		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=kwargs["""use_cache"""]

     SCREAMING_SNAKE_CASE_: str     			=use_mems_eval
     SCREAMING_SNAKE_CASE_: List[str]     			=use_mems_train
     super().__init__(pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase       ,		**lowerCAmelCase		)



  @property
  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''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 :					Union[str, Any]       ,		lowerCAmelCase :					List[Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




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








"""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      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[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 ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

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

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

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

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


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,
   )



