

'''simple docstring'''

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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  """roc_bert"""


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[int]=30_522					,      lowercase_			:					int=768					,      lowercase_			:					str=12					,      lowercase_			:					Optional[Any]=12					,      lowercase_			:					List[str]=3_072					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					Optional[int]=512					,      lowercase_			:					List[Any]=2					,      lowercase_			:					Any=0.02					,      lowercase_			:					Optional[int]=1E-12					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Optional[Any]=0					,      lowercase_			:					str="absolute"					,      lowercase_			:					List[str]=None					,      lowercase_			:					List[str]=True					,      lowercase_			:					int=True					,      lowercase_			:					Dict=768					,      lowercase_			:					Any=910					,      lowercase_			:					Optional[Any]=512					,      lowercase_			:					Optional[Any]=24_858					,      lowercase_			:					Optional[int]=True					,      **lowercase_			:					str					,      )						->     str:
      UpperCAmelCase	:     Optional[int]						 =			vocab_size
      UpperCAmelCase	:     Any						 =			max_position_embeddings
      UpperCAmelCase	:     Any						 =			hidden_size
      UpperCAmelCase	:     Any						 =			num_hidden_layers
      UpperCAmelCase	:     List[str]						 =			num_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			intermediate_size
      UpperCAmelCase	:     List[Any]						 =			hidden_act
      UpperCAmelCase	:     List[Any]						 =			hidden_dropout_prob
      UpperCAmelCase	:     Union[str, Any]						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     List[Any]						 =			initializer_range
      UpperCAmelCase	:     List[str]						 =			type_vocab_size
      UpperCAmelCase	:     Optional[int]						 =			layer_norm_eps
      UpperCAmelCase	:     List[str]						 =			use_cache
      UpperCAmelCase	:     Optional[int]						 =			enable_pronunciation
      UpperCAmelCase	:     Tuple						 =			enable_shape
      UpperCAmelCase	:     List[str]						 =			pronunciation_embed_dim
      UpperCAmelCase	:     Dict						 =			pronunciation_vocab_size
      UpperCAmelCase	:     List[str]						 =			shape_embed_dim
      UpperCAmelCase	:     Union[str, Any]						 =			shape_vocab_size
      UpperCAmelCase	:     Any						 =			concat_input
      UpperCAmelCase	:     List[Any]						 =			position_embedding_type
      UpperCAmelCase	:     List[Any]						 =			classifier_dropout
      super().__init__(pad_token_id=lowercase_					,      **lowercase_	)







'''simple docstring'''

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     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     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

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

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

 model.save_pretrained(UpperCAmelCase_     )

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


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




