






"""simple docstring"""




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_		:		Optional[int]		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			'▁'

A_		:		Optional[int]		     =			{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

A_		:		int		     =			{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

A_		:		Tuple		     =			{
    'facebook/s2t-small-librispeech-asr': 1024,
}

A_		:		str		     =			['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

A_		:		List[str]		     =			{'mustc': MUSTC_LANGS}




class     _lowerCAmelCase(	__SCREAMING_SNAKE_CASE ):





       """simple docstring"""







       a    :		Tuple									=VOCAB_FILES_NAMES
       a    :		Any									=PRETRAINED_VOCAB_FILES_MAP
       a    :		List[Any]									=MAX_MODEL_INPUT_SIZES
       a    :		str									=["input_ids", "attention_mask"]

       a    :		List[int]									=[]





       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase="<s>"  ,      _lowerCamelCase="</s>"  ,      _lowerCamelCase="<pad>"  ,      _lowerCamelCase="<unk>"  ,      _lowerCamelCase=False  ,      _lowerCamelCase=False  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__UpperCAmelCase  ,      eos_token=__UpperCAmelCase  ,      unk_token=__UpperCAmelCase  ,      pad_token=__UpperCAmelCase  ,      do_upper_case=__UpperCAmelCase  ,      do_lower_case=__UpperCAmelCase  ,      tgt_lang=__UpperCAmelCase  ,      lang_codes=__UpperCAmelCase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **__UpperCAmelCase  ,      )
         UpperCamelCase_: Dict   				=    do_upper_case
         UpperCamelCase_: List[Any]   				=    do_lower_case

         UpperCamelCase_: List[str]   				=    load_json(__UpperCAmelCase							)
         UpperCamelCase_: Optional[Any]   				=    {v: k for k, v in self.encoder.items()}
         UpperCamelCase_: int   				=    spm_file
         UpperCamelCase_: str   				=    load_spm(__UpperCAmelCase  ,      self.sp_model_kwargs							)

         if lang_codes is not None:
           UpperCamelCase_: Dict   				=    lang_codes
           UpperCamelCase_: Dict   				=    LANGUAGES[lang_codes]
           UpperCamelCase_: List[Any]   				=    [f'''<lang:{lang}>''' for lang in self.langs]
           UpperCamelCase_: Union[str, Any]   				=    {lang: self.sp_model.PieceToId(f'''<lang:{lang}>'''							) for lang in self.langs}

           UpperCamelCase_: Any   				=    self.lang_tokens
           UpperCamelCase_: int   				=    tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang							)
         else:
           UpperCamelCase_: int   				=    {}





       @property
       def       _a   ( self							):
         return len(self.encoder							)





       @property
       def       _a   ( self							):
         return self._tgt_lang





       @tgt_lang.setter
       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    new_tgt_lang
         self.set_tgt_lang_special_tokens(__UpperCAmelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: int   				=    self.lang_code_to_id[tgt_lang]
         UpperCamelCase_: str   				=    [lang_code_id]





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.sp_model.encode(__UpperCAmelCase  ,      out_type=__UpperCAmelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.encoder.get(__UpperCAmelCase  ,      self.encoder[self.unk_token]							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.decoder.get(__UpperCAmelCase  ,      self.unk_token							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    []
         UpperCamelCase_: Any   				=    """"""
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             UpperCamelCase_: int   				=    self.sp_model.decode(__UpperCAmelCase							)
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             UpperCamelCase_: Tuple   				=    []
           else:
             current_sub_tokens.append(__UpperCAmelCase							)
         UpperCamelCase_: Optional[int]   				=    self.sp_model.decode(__UpperCAmelCase							)
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=None							):
         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False							):

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

         UpperCamelCase_: Optional[Any]   				=    [1] * len(self.prefix_tokens							)
         UpperCamelCase_: Optional[int]   				=    [1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(__UpperCAmelCase							)) + suffix_ones
         return prefix_ones + ([0] * len(__UpperCAmelCase							)) + ([0] * len(__UpperCAmelCase							)) + suffix_ones





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.encoder.copy()
         vocab.update(self.added_tokens_encoder							)
         return vocab





       def __getstate__( self							):
         UpperCamelCase_: Dict   				=    self.__dict__.copy()
         UpperCamelCase_: Dict   				=    None
         return state





       def __setstate__( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Any   				=    d

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

         UpperCamelCase_: Any   				=    load_spm(self.spm_file  ,      self.sp_model_kwargs							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         UpperCamelCase_: List[str]   				=    Path(__UpperCAmelCase							)
         assert save_dir.is_dir(), f'''{save_directory} should be a directory'''
         UpperCamelCase_: List[Any]   				=    save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
         )
         UpperCamelCase_: Union[str, Any]   				=    save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
         )

         save_json(self.encoder  ,      __UpperCAmelCase							)

         if os.path.abspath(self.spm_file							) != os.path.abspath(__UpperCAmelCase							) and os.path.isfile(self.spm_file							):
           copyfile(self.spm_file  ,      __UpperCAmelCase							)
         elif not os.path.isfile(self.spm_file							):
           with open(__UpperCAmelCase  ,      'wb'							) as fi:
             UpperCamelCase_: Any   				=    self.sp_model.serialized_model_proto()
             fi.write(__UpperCAmelCase							)

         return (str(__UpperCAmelCase							), str(__UpperCAmelCase							))







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: int   				=    sentencepiece.SentencePieceProcessor(**lowerCAmelCase__  )
  spm.Load(str(lowerCAmelCase__  )  )
  return spm

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:





  with open(lowerCAmelCase__     ,   'r'  ) as f:
    return json.load(lowerCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Union[str, Any]:





  with open(lowerCAmelCase__     ,   'w'  ) as f:
    json.dump(lowerCAmelCase__     ,   lowerCAmelCase__     ,   indent=2  )







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A_		:		Tuple		     =			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]

def   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    if new_k not in sd:
      raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''  )

    if "dense" in k or "proj" in new_k:
      UpperCamelCase_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   dtype=sd[new_k].dtype  )
    assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  UpperCamelCase_: Tuple   				=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
  UpperCamelCase_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
  ]
  assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], F'''no matches found for the following tf keys {extra}'''
  return torch_model

def   snake_case (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

  # convert model
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    torch_model.state_dict()
  sd.pop('model.decoder.embed_positions.weight'  )
  sd.pop('model.encoder.embed_positions.weight'  )
  torch.save(UpperCAmelCase__     ,   Path(UpperCAmelCase__  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   A_		:		Optional[Any]		     =			parser.parse_args()
   if args.save_dir is None:
      A_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)