


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__												=      logging.get_logger(__name__)

UpperCamelCase__												=      {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase__												=      {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase__												=      {
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

UpperCamelCase__												=      """▁"""

class     UpperCAmelCase__ (   UpperCAmelCase__		):



     '''simple docstring'''
     UpperCAmelCase_       =	VOCAB_FILES_NAMES
     UpperCAmelCase_       =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_       =	['''input_ids''', '''attention_mask''']




     def __init__(			self							:							int						,   UpperCamelCase							:							List[str]						,   UpperCamelCase							:							int="<s>"						,   UpperCamelCase							:							str="</s>"						,   UpperCamelCase							:							Dict="</s>"						,   UpperCamelCase							:							Optional[int]="<s>"						,   UpperCamelCase							:							List[str]="<unk>"						,   UpperCamelCase							:							str="<pad>"						,   UpperCamelCase							:							str="<mask>"						,   UpperCamelCase							:							Optional[Dict[str, Any]] = None						,   **UpperCamelCase							:							List[str]						,   ):





            """simple docstring"""



            _lowercase       :					Tuple     							=      AddedToken(UpperCAmelCase__						,   lstrip=UpperCAmelCase__						,   rstrip=UpperCAmelCase__		) if isinstance(UpperCAmelCase__						,   UpperCAmelCase__		) else mask_token

            _lowercase       :					List[Any]     							=      {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=UpperCAmelCase__						,   eos_token=UpperCAmelCase__						,   unk_token=UpperCAmelCase__						,   sep_token=UpperCAmelCase__						,   cls_token=UpperCAmelCase__						,   pad_token=UpperCAmelCase__						,   mask_token=UpperCAmelCase__						,   sp_model_kwargs=self.sp_model_kwargs						,   **UpperCAmelCase__						,   )

            _lowercase       :					Optional[Any]     							=      vocab_file
            _lowercase       :					Optional[Any]     							=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(UpperCAmelCase__		)		)

            _lowercase       :					Optional[int]     							=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

            _lowercase       :					Tuple     							=      len(self.sp_model		) - 1
            _lowercase       :					Optional[int]     							=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}




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





            """simple docstring"""



            if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            _lowercase       :					List[Any]     							=      [self.cls_token_id]
            _lowercase       :					List[str]     							=      [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep




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





            """simple docstring"""



            if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=UpperCAmelCase__						,   token_ids_a=UpperCAmelCase__						,   already_has_special_tokens=UpperCAmelCase__		)

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




     def 	lowerCAmelCase_ (			self							:							List[str]						,   UpperCamelCase							:							List[int]						,   UpperCamelCase							:							Optional[List[int]] = None		):





            """simple docstring"""



            _lowercase       :					int     							=      [self.sep_token_id]
            _lowercase       :					str     							=      [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 + sep + token_ids_a + sep		) * [0]




     @property
     def 	lowerCAmelCase_ (			self							:							Union[str, Any]		):





            """simple docstring"""



            return len(self.sp_model		)




     def 	lowerCAmelCase_ (			self							:							Optional[int]		):





            """simple docstring"""



            _lowercase       :					Tuple     							=      {self.convert_ids_to_tokens(UpperCAmelCase__		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab




     def 	lowerCAmelCase_ (			self							:							Optional[int]						,   UpperCamelCase							:							str		):





            """simple docstring"""



            return self.sp_model.encode(UpperCAmelCase__						,   out_type=UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							str						,   UpperCamelCase							:							Optional[int]		):





            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
            _lowercase       :					Optional[Any]     							=      self.sp_model.PieceToId(UpperCAmelCase__		)

            return spm_id if spm_id else self.unk_token_id




     def 	lowerCAmelCase_ (			self							:							Optional[Any]						,   UpperCamelCase							:							List[str]		):





            """simple docstring"""



            if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(UpperCAmelCase__		)




     def 	lowerCAmelCase_ (			self							:							Union[str, Any]						,   UpperCamelCase							:							int		):





            """simple docstring"""



            _lowercase       :					Dict     							=      []
            _lowercase       :					int     							=      ''''''
            _lowercase       :					List[Any]     							=      False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          if not prev_is_special:
                                 out_string += " "
                          out_string += self.sp_model.decode(UpperCAmelCase__		) + token
                          _lowercase       :					List[str]     							=      True
                          _lowercase       :					Union[str, Any]     							=      []
                   else:
                          current_sub_tokens.append(UpperCAmelCase__		)
                          _lowercase       :					str     							=      False
            out_string += self.sp_model.decode(UpperCAmelCase__		)
            return out_string.strip()




     def __getstate__(			self							:							Optional[Any]		):





            """simple docstring"""



            _lowercase       :					Tuple     							=      self.__dict__.copy()
            _lowercase       :					List[str]     							=      None
            return state




     def __setstate__(			self							:							List[Any]						,   UpperCamelCase							:							int		):





            """simple docstring"""



            _lowercase       :					str     							=      d

            # for backward compatibility
            if not hasattr(self						,   '''sp_model_kwargs'''		):
                   _lowercase       :					List[str]     							=      {}

            _lowercase       :					Any     							=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(self.vocab_file		)




     def 	lowerCAmelCase_ (			self							:							Tuple						,   UpperCamelCase							:							str						,   UpperCamelCase							:							Optional[str] = None		):





            """simple docstring"""



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

            if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase__		) and os.path.isfile(self.vocab_file		):
                   copyfile(self.vocab_file						,   UpperCAmelCase__		)
            elif not os.path.isfile(self.vocab_file		):
                   with open(UpperCAmelCase__						,   '''wb'''		) as fi:
                          _lowercase       :					Tuple     							=      self.sp_model.serialized_model_proto()
                          fi.write(UpperCAmelCase__		)

            return (out_vocab_file,)





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCamelCase      :						int							=  logging.get_logger("""transformers.models.speecht5""")







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

       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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None					,							)  ->			str:



       """simple docstring"""

       if config_path is not None:
              A__ 					=       SpeechTaHifiGanConfig.from_pretrained(lowercase_		)
       else:
              A__ 					=       SpeechTaHifiGanConfig()

       A__ 					=       SpeechTaHifiGan(lowercase_		)

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

       A__ 					=       np.load(lowercase_		)
       A__ 					=       stats[0].reshape(-1		)
       A__ 					=       stats[1].reshape(-1		)
       A__ 					=       torch.from_numpy(lowercase_		).float()
       A__ 					=       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__":
  _lowerCamelCase      :						Any							=  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."""
  )

  _lowerCamelCase      :						List[str]							=  parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )


