


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_SCREAMING_SNAKE_CASE     :			List[Any]        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			List[Any]        =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE     :			int        =							{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

_SCREAMING_SNAKE_CASE     :			Tuple        =							{
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


_SCREAMING_SNAKE_CASE     :			Any        =							{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class        A     (		_a	):







     '''simple docstring'''







     lowerCamelCase :						Tuple            =   VOCAB_FILES_NAMES
     lowerCamelCase :						Union[str, Any]            =   PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase :						Any            =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase :						Dict            =   PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase :						Optional[Any]            =   RoFormerTokenizer

     def __init__(				self    :							Union[str, Any]			,				_UpperCamelCase    :							List[str]=None			,				_UpperCamelCase    :							Any=None			,				_UpperCamelCase    :							int=True			,				_UpperCamelCase    :							Union[str, Any]="[UNK]"			,				_UpperCamelCase    :							Tuple="[SEP]"			,				_UpperCamelCase    :							Tuple="[PAD]"			,				_UpperCamelCase    :							str="[CLS]"			,				_UpperCamelCase    :							Tuple="[MASK]"			,				_UpperCamelCase    :							str=True			,				_UpperCamelCase    :							Optional[int]=None			,				**_UpperCamelCase    :							List[str]			,				):
      super().__init__(
          A_			,				tokenizer_file=A_			,				do_lower_case=A_			,				unk_token=A_			,				sep_token=A_			,				pad_token=A_			,				cls_token=A_			,				mask_token=A_			,				tokenize_chinese_chars=A_			,				strip_accents=A_			,				**A_			,				)

      _lowercase:       Any						=				json.loads(self.backend_tokenizer.normalizer.__getstate__())
      if (
          pre_tok_state.get("lowercase"			,				A_) != do_lower_case
          or pre_tok_state.get("strip_accents"			,				A_) != strip_accents
      ):
       _lowercase:       Optional[Any]						=				getattr(A_			,				pre_tok_state.pop("type"))
       _lowercase:       Dict						=				do_lower_case
       _lowercase:       str						=				strip_accents
       _lowercase:       List[str]						=				pre_tok_class(**A_)

      _lowercase:       Union[str, Any]						=				do_lower_case

     def __getstate__(				self    :							Dict):
      _lowercase:       str						=				self.__dict__.copy()
      _lowercase:       List[Any]						=				BertPreTokenizer()
      return state

     def __setstate__(				self    :							Union[str, Any]			,				_UpperCamelCase    :							str):
      _lowercase:       Any						=				d
      _lowercase:       List[str]						=				self.__dict__["_tokenizer"].get_vocab()
      _lowercase:       Tuple						=				PreTokenizer.custom(JiebaPreTokenizer(A_))

     def 					UpperCAmelCase__       (				self    :							Union[str, Any]			,				_UpperCamelCase    :							Optional[Any]			,				_UpperCamelCase    :							Dict=None):
      _lowercase:       Dict						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output

     def 					UpperCAmelCase__       (				self    :							int			,				_UpperCamelCase    :							List[int]			,				_UpperCamelCase    :							Optional[List[int]] = None):
      _lowercase:       int						=				[self.sep_token_id]
      _lowercase:       List[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) * [0] + len(token_ids_a + sep) * [1]

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Optional[str] = None):
      _lowercase:       List[str]						=				self._tokenizer.model.save(A_			,				name=A_)
      return tuple(A_)






     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							Any			,				_UpperCamelCase    :							List[Any]=None			,				_UpperCamelCase    :							Dict=None			,				_UpperCamelCase    :							Optional[Any]=False			,				**_UpperCamelCase    :							str			,				):
      _lowercase:       Optional[Any]						=				BertPreTokenizer()
      return super().save_pretrained(A_			,				A_			,				A_			,				A_			,				**A_)





"""simple docstring"""

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 lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						BertAbsConfig(
     temp_dir="""."""					, finetune_bert=UpperCamelCase__					, large=UpperCamelCase__					, share_emb=UpperCamelCase__					, use_bert_emb=UpperCamelCase__					, 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=2048					, dec_dropout=0.2					, )
 __lowercase						=						torch.load(UpperCamelCase__					, lambda UpperCamelCase__					, UpperCamelCase__			: storage			)
 __lowercase						=						AbsSummarizer(UpperCamelCase__					, torch.device("""cpu"""			)					, UpperCamelCase__			)
 original.eval()

 __lowercase						=						BertAbsSummarizer(UpperCamelCase__					, 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"""			)
 __lowercase						=						BertTokenizer.from_pretrained("""bert-base-uncased"""			)

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

 __lowercase						=						new_model(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						new_model.generator(UpperCamelCase__			)

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

 __lowercase						=						torch.allclose(UpperCamelCase__					, UpperCamelCase__					, 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,
       )
