


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE						:str			=  logging.getLogger(__name__)


SCREAMING_SNAKE_CASE						:Optional[Any]			=  '''Hello world! cécé herlolip'''


SCREAMING_SNAKE_CASE						:Optional[int]			=  namedtuple(
    '''BertAbsConfig''',
    [
        '''temp_dir''',
        '''large''',
        '''use_bert_emb''',
        '''finetune_bert''',
        '''encoder''',
        '''share_emb''',
        '''max_pos''',
        '''enc_layers''',
        '''enc_hidden_size''',
        '''enc_heads''',
        '''enc_ff_size''',
        '''enc_dropout''',
        '''dec_layers''',
        '''dec_hidden_size''',
        '''dec_heads''',
        '''dec_ff_size''',
        '''dec_dropout''',
    ],
)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Any				)->List[Any]:
   '''simple docstring'''






   snake_case_            =       BertAbsConfig(
       temp_dir="."		,    finetune_bert=lowerCAmelCase_		,    large=lowerCAmelCase_		,    share_emb=lowerCAmelCase_		,    use_bert_emb=lowerCAmelCase_		,    encoder="bert"		,    max_pos=512		,    enc_layers=6		,    enc_hidden_size=512		,    enc_heads=8		,    enc_ff_size=512		,    enc_dropout=0.2		,    dec_layers=6		,    dec_hidden_size=768		,    dec_heads=8		,    dec_ff_size=2_048		,    dec_dropout=0.2		,    )
   snake_case_            =       torch.load(lowerCAmelCase_		,    lambda lowerCAmelCase_		,    lowerCAmelCase_				: storage				)
   snake_case_            =       AbsSummarizer(lowerCAmelCase_		,    torch.device("cpu"				)		,    lowerCAmelCase_				)
   original.eval()

   snake_case_            =       BertAbsSummarizer(lowerCAmelCase_		,    torch.device("cpu"				)				)
   new_model.eval()

   # -------------------
   # Convert the weights
   # -------------------

   logging.info("convert the model"				)
   new_model.bert.load_state_dict(original.bert.state_dict()				)
   new_model.decoder.load_state_dict(original.decoder.state_dict()				)
   new_model.generator.load_state_dict(original.generator.state_dict()				)

   # ----------------------------------
   # Make sure the outpus are identical
   # ----------------------------------

   logging.info("Make sure that the models' outputs are identical"				)
   snake_case_            =       BertTokenizer.from_pretrained("bert-base-uncased"				)

   # prepare the model inputs
   snake_case_            =       tokenizer.encode("This is sample éàalj'-."				)
   encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowerCAmelCase_				))				)
   snake_case_            =       torch.tensor(lowerCAmelCase_				).unsqueeze(0				)
   snake_case_            =       tokenizer.encode("This is sample 3 éàalj'-."				)
   decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowerCAmelCase_				))				)
   snake_case_            =       torch.tensor(lowerCAmelCase_				).unsqueeze(0				)

   # failsafe to make sure the weights reset does not affect the
   # loaded weights.
   assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight				)				) == 0

   # forward pass
   snake_case_            =       encoder_input_ids
   snake_case_            =       decoder_input_ids
   snake_case_            =       snake_case_            =       None
   snake_case_            =       None
   snake_case_            =       snake_case_            =       None
   snake_case_            =       snake_case_            =       None
   snake_case_            =       None

   # The original model does not apply the geneator layer immediatly but rather in
   # the beam search (where it combines softmax + linear layer). Since we already
   # apply the softmax in our generation process we only apply the linear layer here.
   # We make sure that the outputs of the full stack are identical
   snake_case_            =       original(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)[0]
   snake_case_            =       original.generator(lowerCAmelCase_				)

   snake_case_            =       new_model(
       lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)[0]
   snake_case_            =       new_model.generator(lowerCAmelCase_				)

   snake_case_            =       torch.max(torch.abs(output_converted_model - output_original_model				)				).item()
   print("Maximum absolute difference beween weights: {:.2f}".format(lowerCAmelCase_				)				)
   snake_case_            =       torch.max(torch.abs(output_converted_generator - output_original_generator				)				).item()
   print("Maximum absolute difference beween weights: {:.2f}".format(lowerCAmelCase_				)				)

   snake_case_            =       torch.allclose(lowerCAmelCase_		,    lowerCAmelCase_		,    atol=1e-3				)
   if are_identical:
      logging.info("all weights are equal up to 1e-3"				)
   else:
      raise ValueError("the weights are different. The new model is likely different from the original one."				)

   # The model has been saved with torch.save(model) and this is bound to the exact
   # directory structure. We save the state_dict instead.
   logging.info("saving the model's state dictionary"				)
   torch.save(
       new_model.state_dict()		,    "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   parser.add_argument(
       '''--bertabs_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help='''Path the official PyTorch dump.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=str,
       required=True,
       help='''Path to the output PyTorch model.''',
   )
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )








import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE						:str			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:str			=  {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


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']
  _SCREAMING_SNAKE_CASE		     =							RobertaTokenizer



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




     """simple docstring"""
     super().__init__(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					tokenizer_file=_lowerCAmelCase     ,					errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					trim_offsets=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
     if pre_tok_state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
        snake_case_            =       getattr(_lowerCAmelCase     ,					pre_tok_state.pop("type"	)	)
        snake_case_            =       add_prefix_space
        snake_case_            =       pre_tok_class(**_lowerCAmelCase	)

     snake_case_            =       add_prefix_space

     snake_case_            =       "post_processor"
     snake_case_            =       getattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     if tokenizer_component_instance:
        snake_case_            =       json.loads(tokenizer_component_instance.__getstate__()	)

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
           snake_case_            =       tuple(state["sep"]	)
        if "cls" in state:
           snake_case_            =       tuple(state["cls"]	)

        snake_case_            =       False

        if state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
           snake_case_            =       add_prefix_space
           snake_case_            =       True

        if state.get("trim_offsets"     ,					_lowerCAmelCase	) != trim_offsets:
           snake_case_            =       trim_offsets
           snake_case_            =       True

        if changes_to_apply:
           snake_case_            =       getattr(_lowerCAmelCase     ,					state.pop("type"	)	)
           snake_case_            =       component_class(**_lowerCAmelCase	)
           setattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			str:




     """simple docstring"""
     if self._mask_token is None:
        if self.verbose:
           logger.error("Using mask_token, but it is not set yet."	)
        return None
     return str(self._mask_token	)



  @mask_token.setter
  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Tuple	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else value
     snake_case_            =       value



  def        lowerCAmelCase__	(  self							:							Dict     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[int]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)
     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					*_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)

     assert self.add_prefix_space or not is_split_into_words, (
         F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



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




     """simple docstring"""
     snake_case_            =       self._tokenizer.model.save(_lowerCAmelCase     ,					name=_lowerCAmelCase	)
     return tuple(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]=None	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     snake_case_            =       [self.sep_token_id]
     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 + sep + token_ids_a + sep	) * [0]




