

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_big_bird import BigBirdTokenizer
else:
      a_    :						Any    		=      None

a_    :						List[str]    		=      logging.get_logger(__name__)
a_    :						Union[str, Any]    		=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_    :						List[str]    		=      {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

a_    :						List[Any]    		=      {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


a_    :						Dict    		=      "▁"




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =BigBirdTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]
    __UpperCamelCase         =[]

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Dict=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      Union[str, Any]="<unk>"							,    snake_case__ :      int="<s>"							,    snake_case__ :      Tuple="</s>"							,    snake_case__ :      Tuple="<pad>"							,    snake_case__ :      Tuple="[SEP]"							,    snake_case__ :      Union[str, Any]="[MASK]"							,    snake_case__ :      Dict="[CLS]"							,    **snake_case__ :      Dict							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else bos_token
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else eos_token
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else unk_token
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else pad_token
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else cls_token
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else sep_token

     # Mask token behave like a normal word, i.e. include the space before it
     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else mask_token

     super().__init__(
         snake_case__							,    tokenizer_file=snake_case__							,    bos_token=snake_case__							,    eos_token=snake_case__							,    unk_token=snake_case__							,    sep_token=snake_case__							,    pad_token=snake_case__							,    cls_token=snake_case__							,    mask_token=snake_case__							,    **snake_case__							,    )

     SCREAMING_SNAKE_CASE								=			vocab_file
     SCREAMING_SNAKE_CASE								=			False if not self.vocab_file else True

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[self.cls_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'    )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is None:
      return [1] + ([0] * len(snake_case__    )) + [1]
     return [1] + ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1]

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     if not self.can_save_slow_tokenizer:
      raise ValueError(
          'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
          'tokenizer.'    )

     if not os.path.isdir(snake_case__    ):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
      return
     SCREAMING_SNAKE_CASE								=			os.path.join(
         snake_case__							,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

     if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case__    ):
      copyfile(self.vocab_file							,    snake_case__    )

     return (out_vocab_file,)




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
a_    :						List[Any]    		=      logging.get_logger("transformers.models.speecht5")


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any	)		->							Dict:
 '''simple docstring'''


 hf_model.apply_weight_norm()

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

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

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

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

 hf_model.remove_weight_norm()


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  Any=None		,							_UpperCamelCase		:  List[str]=None		,							)		->							Tuple:
 '''simple docstring'''


 if config_path is not None:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig.from_pretrained(_UpperCamelCase	)
 else:
  SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig()

 SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(_UpperCamelCase	)

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

 SCREAMING_SNAKE_CASE								=			np.load(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			stats[0].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			stats[1].reshape(-1	)
 SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	).float()
 SCREAMING_SNAKE_CASE								=			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__":
      a_    :						Dict    		=      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."
      )

      a_    :						Tuple    		=      parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )

