


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


SCREAMING_SNAKE_CASE						:Optional[int]			=  [
    # 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       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]				)->str:
   '''simple docstring'''






   for pegasus_name, hf_name in PATTERNS:
      snake_case_            =       k.replace(lowerCAmelCase_		,    lowerCAmelCase_				)
   return k



def       _lowerCAmelCase    (				lowerCAmelCase_				:dict		,    lowerCAmelCase_				:dict				)->PegasusForConditionalGeneration:
   '''simple docstring'''






   snake_case_            =       DEFAULTS.copy()
   cfg_kwargs.update(lowerCAmelCase_				)
   snake_case_            =       PegasusConfig(**lowerCAmelCase_				)
   snake_case_            =       PegasusForConditionalGeneration(lowerCAmelCase_				)
   snake_case_            =       torch_model.model.state_dict()
   snake_case_            =       {}
   for k, v in tf_weights.items():
      snake_case_            =       rename_state_dict_key(lowerCAmelCase_				)
      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:
         snake_case_            =       v.T
      snake_case_            =       torch.tensor(lowerCAmelCase_		,    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
   snake_case_            =       torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]				)
   snake_case_            =       mapping["shared.weight"]
   snake_case_            =       mapping["shared.weight"]
   snake_case_            =       {k: torch.zeros_like(lowerCAmelCase_				) for k, v in sd.items() if k.endswith("bias"				) and k not in mapping}
   mapping.update(**lowerCAmelCase_				)
   snake_case_     ,     snake_case_            =       torch_model.model.load_state_dict(lowerCAmelCase_		,    strict=lowerCAmelCase_				)
   snake_case_            =       [
       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       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000"				)->Dict:
   '''simple docstring'''






   snake_case_            =       tf.train.list_variables(lowerCAmelCase_				)
   snake_case_            =       {}
   snake_case_            =       ["Adafactor", "global_step"]
   for name, shape in tqdm(lowerCAmelCase_		,    desc="converting tf checkpoint to dict"				):
      snake_case_            =       any(pat in name for pat in ignore_name				)
      if skip_key:
         continue
      snake_case_            =       tf.train.load_variable(lowerCAmelCase_		,    lowerCAmelCase_				)
      snake_case_            =       array
   return tf_weights



def       _lowerCAmelCase    (				lowerCAmelCase_				:str		,    lowerCAmelCase_				:str				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       Path(lowerCAmelCase_				).parent.name
   snake_case_            =       task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
   snake_case_            =       PegasusTokenizer.from_pretrained("sshleifer/pegasus"		,    model_max_length=lowerCAmelCase_				)
   assert tok.model_max_length == desired_max_model_length
   tok.save_pretrained(lowerCAmelCase_				)

   # convert model
   snake_case_            =       get_tf_weights_as_numpy(lowerCAmelCase_				)
   snake_case_            =       task_specific_params[F'''summarization_{dataset}''']
   if dataset == "large":
      snake_case_            =       task_specific_params
   snake_case_            =       convert_pegasus(lowerCAmelCase_		,    lowerCAmelCase_				)
   torch_model.save_pretrained(lowerCAmelCase_				)
   snake_case_            =       torch_model.state_dict()
   sd.pop("model.decoder.embed_positions.weight"				)
   sd.pop("model.encoder.embed_positions.weight"				)
   torch.save(lowerCAmelCase_		,    Path(lowerCAmelCase_				) / "pytorch_model.bin"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						: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.''')
   SCREAMING_SNAKE_CASE						:List[Any]			=  parser.parse_args()
   if args.save_dir is None:
      SCREAMING_SNAKE_CASE						:Dict			=  Path(args.tf_ckpt_path).parent.name
      SCREAMING_SNAKE_CASE						:Dict			=  os.path.join('''pegasus''', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








import json
import os
from typing import Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:Dict			=  {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE						:Optional[int]			=  {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:List[str]			=  '''</w>'''
SCREAMING_SNAKE_CASE						:int			=  '''@@ '''



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						:Tuple			=  {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}



class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']



  def __init__(  self							:							List[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Union[str, Any]="<s>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							Union[str, Any]="</s>"     ,					_lowerCAmelCase							:							str="<unk>"     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			List[str]:




     """simple docstring"""
     super().__init__(
         unk_token=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					do_lower_case=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       do_lower_case

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}

     if merges_file is None:
        logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

        snake_case_            =       None
        snake_case_            =       None
     else:
        with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
           snake_case_            =       merges_handle.read().split("\n"	)[:-1]

        snake_case_            =       [tuple(merge.split()[:2]	) for merge in merges]
        snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
        snake_case_            =       {}



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     return len(self.decoder	)



  def        lowerCAmelCase__	(  self							:							Any	) ->			Dict:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Any	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       j

           if word[i] == first and i < len(_lowerCAmelCase	) - 1 and word[i + 1] == second:
              new_word.append(first + second	)
              i += 2
           else:
              new_word.append(word[i]	)
              i += 1
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     if word == "\n  " + BPE_TOKEN_MERGES:
        snake_case_            =       "\n" + BPE_TOKEN_MERGES

     if word.endswith(_lowerCAmelCase	):
        snake_case_            =       word.replace(_lowerCAmelCase     ,					""	)

     snake_case_            =       word.replace(" "     ,					_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[str]	) ->			List[Any]:




     """simple docstring"""

     if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding."	)

     if self.do_lower_case:
        snake_case_            =       text.lower()

     snake_case_            =       text.split()

     snake_case_            =       []
     for token in text:
        if token:
           split_tokens.extend(list(self.bpe(_lowerCAmelCase	).split(" "	)	)	)

     return split_tokens



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							str	) ->			int:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     snake_case_            =       self.decoder.get(_lowerCAmelCase     ,					self.unk_token	)
     return result



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			str:




     """simple docstring"""
     snake_case_            =       " ".join(_lowerCAmelCase	)

     # make sure @@ tokens are concatenated
     snake_case_            =       "".join(string.split(_lowerCAmelCase	)	)

     return string



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




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

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as f:
        f.write(json.dumps(self.encoder     ,					indent=2     ,					sort_keys=_lowerCAmelCase     ,					ensure_ascii=_lowerCAmelCase	) + "\n"	)

     snake_case_            =       0
     if self.bpe_ranks is None:
        return (vocab_file,)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda _lowerCAmelCase	: kv[1]	):
           if index != token_index:
              logger.warning(
                  F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                  " Please check that the tokenizer is not corrupted!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return (vocab_file, merges_file)




