

'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}




class 	A_			(    UpperCamelCase_							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  """camembert"""


     def __init__(		self			:					Tuple					,      lowercase_			:					Dict=30_522					,      lowercase_			:					Any=768					,      lowercase_			:					Dict=12					,      lowercase_			:					Tuple=12					,      lowercase_			:					Union[str, Any]=3_072					,      lowercase_			:					Dict="gelu"					,      lowercase_			:					Tuple=0.1					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					Tuple=512					,      lowercase_			:					int=2					,      lowercase_			:					List[Any]=0.02					,      lowercase_			:					List[Any]=1E-12					,      lowercase_			:					Dict=1					,      lowercase_			:					Tuple=0					,      lowercase_			:					Any=2					,      lowercase_			:					str="absolute"					,      lowercase_			:					Tuple=True					,      lowercase_			:					List[Any]=None					,      **lowercase_			:					int					,      )						->     Tuple:
      super().__init__(pad_token_id=_a					,      bos_token_id=_a					,      eos_token_id=_a					,      **_a	)

      UpperCAmelCase	:     Tuple						 =			vocab_size
      UpperCAmelCase	:     Union[str, Any]						 =			hidden_size
      UpperCAmelCase	:     List[Any]						 =			num_hidden_layers
      UpperCAmelCase	:     List[str]						 =			num_attention_heads
      UpperCAmelCase	:     int						 =			hidden_act
      UpperCAmelCase	:     Optional[int]						 =			intermediate_size
      UpperCAmelCase	:     List[str]						 =			hidden_dropout_prob
      UpperCAmelCase	:     List[str]						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			max_position_embeddings
      UpperCAmelCase	:     str						 =			type_vocab_size
      UpperCAmelCase	:     int						 =			initializer_range
      UpperCAmelCase	:     List[Any]						 =			layer_norm_eps
      UpperCAmelCase	:     Tuple						 =			position_embedding_type
      UpperCAmelCase	:     Optional[int]						 =			use_cache
      UpperCAmelCase	:     Dict						 =			classifier_dropout




class 	A_			(    UpperCamelCase_							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Mapping[str, Mapping[int, str]]:


      if self.task == "multiple-choice":
       UpperCAmelCase	:     Union[str, Any]						 =			{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
       UpperCAmelCase	:     Any						 =			{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)







'''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,
 )




