






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    (      a :Any	,							a :List[str]	,							a :Tuple )							->			int:
 hf_model.apply_weight_norm()

 a		  =      checkpoint['''input_conv.weight_g''']
 a		  =      checkpoint['''input_conv.weight_v''']
 a		  =      checkpoint['''input_conv.bias''']

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

   a		  =      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   a		  =      checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   a		  =      checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 a		  =      checkpoint['''output_conv.1.weight_g''']
 a		  =      checkpoint['''output_conv.1.weight_v''']
 a		  =      checkpoint['''output_conv.1.bias''']

 hf_model.remove_weight_norm()
@torch.no_grad()
def 						_a    (      a :Dict	,							a :int	,							a :Tuple	,							a :Dict=None	,							a :List[Any]=None	,							)							->			Optional[Any]:
 if config_path is not None:
  a		  =      SpeechTaHifiGanConfig.from_pretrained(a )
 else:
  a		  =      SpeechTaHifiGanConfig()

 a		  =      SpeechTaHifiGan(a )

 a		  =      torch.load(a )
 load_weights(orig_checkpoint['''model''']['''generator''']	,							a	,							a )

 a		  =      np.load(a )
 a		  =      stats[0].reshape(-1 )
 a		  =      stats[1].reshape(-1 )
 a		  =      torch.from_numpy(a ).float()
 a		  =      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__":
     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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCAmelCase__				   =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				   =		{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": 512,
    "google/electra-base-generator": 512,
    "google/electra-large-generator": 512,
    "google/electra-small-discriminator": 512,
    "google/electra-base-discriminator": 512,
    "google/electra-large-discriminator": 512,
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  VOCAB_FILES_NAMES
      __snake_case       =  PRETRAINED_VOCAB_FILES_MAP
      __snake_case       =  PRETRAINED_INIT_CONFIGURATION
      __snake_case       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case       =  ElectraTokenizer







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						str="[UNK]" ,					__UpperCAmelCase      :						Any="[SEP]" ,					__UpperCAmelCase      :						str="[PAD]" ,					__UpperCAmelCase      :						Optional[Any]="[CLS]" ,					__UpperCAmelCase      :						Union[str, Any]="[MASK]" ,					__UpperCAmelCase      :						List[str]=True ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->str:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase ,					tokenizer_file=__UpperCAmelCase ,					do_lower_case=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					tokenize_chinese_chars=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

       a		  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get('''lowercase''' ,					__UpperCAmelCase  ) != do_lower_case
           or normalizer_state.get('''strip_accents''' ,					__UpperCAmelCase  ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars''' ,					__UpperCAmelCase  ) != tokenize_chinese_chars
       ):
        a		  =      getattr(__UpperCAmelCase ,					normalizer_state.pop('''type'''  )  )
        a		  =      do_lower_case
        a		  =      strip_accents
        a		  =      tokenize_chinese_chars
        a		  =      normalizer_class(**__UpperCAmelCase  )

       a		  =      do_lower_case







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Tuple=None  )    ->str:




       """simple docstring"""


       a		  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[int] ,					__UpperCAmelCase      :						Optional[List[int]] = None  )    ->List[int]:




       """simple docstring"""


       a		  =      [self.sep_token_id]
       a		  =      [self.cls_token_id]
       if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       a		  =      self._tokenizer.model.save(__UpperCAmelCase ,					name=__UpperCAmelCase  )
       return tuple(__UpperCAmelCase  )


