






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)
UpperCAmelCase__							  =						logging.getLogger(__name__)


UpperCAmelCase__							  =						"Hello world! cécé herlolip"


UpperCAmelCase__							  =						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 		_a       ( a					:Union[str, Any]      ,				a					:Optional[int]					)				->     Union[str, Any]:
 a						    = BertAbsConfig(
     temp_dir='''.'''      ,				finetune_bert=a      ,				large=a      ,				share_emb=a      ,				use_bert_emb=a      ,				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      ,				)
 a						    = torch.load(a      ,				lambda a      ,				a					: storage					)
 a						    = AbsSummarizer(a      ,				torch.device('''cpu'''					)      ,				a					)
 original.eval()

 a						    = BertAbsSummarizer(a      ,				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'''					)
 a						    = BertTokenizer.from_pretrained('''bert-base-uncased'''					)

 # prepare the model inputs
 a						    = tokenizer.encode('''This is sample éàalj\'-.'''					)
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a					))					)
 a						    = torch.tensor(a					).unsqueeze(0					)
 a						    = tokenizer.encode('''This is sample 3 éàalj\'-.'''					)
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a					))					)
 a						    = torch.tensor(a					).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
 a						    = encoder_input_ids
 a						    = decoder_input_ids
 a						    = a						    = None
 a						    = None
 a						    = a						    = None
 a						    = a						    = None
 a						    = 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
 a						    = original(a      ,				a      ,				a      ,				a      ,				a      ,				a      ,				a					)[0]
 a						    = original.generator(a					)

 a						    = new_model(
     a      ,				a      ,				a      ,				a      ,				a					)[0]
 a						    = new_model.generator(a					)

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

 a						    = torch.allclose(a      ,				a      ,				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__":
     UpperCAmelCase__							  =						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.",
     )
     UpperCAmelCase__							  =						parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )










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

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


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     UpperCAmelCase__							  =						None


UpperCAmelCase__							  =						logging.get_logger(__name__)


UpperCAmelCase__							  =						"▁"

UpperCAmelCase__							  =						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__							  =						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase__							  =						{
    "google/pegasus-xsum": 512,
}






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    VOCAB_FILES_NAMES
      __snake_case	    =    PRETRAINED_VOCAB_FILES_MAP
      __snake_case	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case	    =    PegasusTokenizer
      __snake_case	    =    ['''input_ids''', '''attention_mask''']







      def __init__( self :		List[Any]					,   __UpperCAmelCase :		str=None					,   __UpperCAmelCase :		List[Any]=None					,   __UpperCAmelCase :		Any="<pad>"					,   __UpperCAmelCase :		str="</s>"					,   __UpperCAmelCase :		List[str]="<unk>"					,   __UpperCAmelCase :		List[str]="<mask_2>"					,   __UpperCAmelCase :		Tuple="<mask_1>"					,   __UpperCAmelCase :		int=None					,   __UpperCAmelCase :		str=103					,   **__UpperCAmelCase :		Optional[int]					,   )       ->Dict:




       """simple docstring"""


       a						    = offset

       if additional_special_tokens is not None:
        if not isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         raise TypeError(
             F"""additional_special_tokens should be of type {type(__UpperCAmelCase    )}, but is"""
             F""" {type(__UpperCAmelCase    )}"""    )

        a						    = (
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            F"""<unk_{i}>""" for i in range(len(__UpperCAmelCase    )					,   self.offset - 1    )
        ]

        if len(set(__UpperCAmelCase    )    ) != len(__UpperCAmelCase    ):
         raise ValueError(
             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
             F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
        a						    = additional_special_tokens_extended
       else:
        a						    = [mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [F"""<unk_{i}>""" for i in range(2					,   self.offset    )]

       super().__init__(
           __UpperCAmelCase					,   tokenizer_file=__UpperCAmelCase					,   pad_token=__UpperCAmelCase					,   eos_token=__UpperCAmelCase					,   unk_token=__UpperCAmelCase					,   mask_token=__UpperCAmelCase					,   mask_token_sent=__UpperCAmelCase					,   offset=__UpperCAmelCase					,   additional_special_tokens=__UpperCAmelCase					,   **__UpperCAmelCase					,   )
       a						    = vocab_file
       a						    = False if not self.vocab_file else True







      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		int    )       ->int:




       """simple docstring"""


       a						    = set(self.all_special_ids    )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
        raise ValueError(
            '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
            F""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

       return [1 if x in all_special_ids else 0 for x in seq]







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List					,   __UpperCAmelCase :		Optional[List] = None					,   __UpperCAmelCase :		bool = False    )       ->List[int]:




       """simple docstring"""


       if already_has_special_tokens:
        return self._special_token_mask(__UpperCAmelCase    )
       elif token_ids_a is None:
        return self._special_token_mask(__UpperCAmelCase    ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]







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




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[str] = None    )       ->Tuple[str]:




       """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(__UpperCAmelCase    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
       a						    = 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    ):
        copyfile(self.vocab_file					,   __UpperCAmelCase    )

       return (out_vocab_file,)


