


"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={"vocab_file": "vocab.txt"}

UpperCAmelCase__					={
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

UpperCAmelCase__					={
    "YituTech/conv-bert-base": 512,
    "YituTech/conv-bert-medium-small": 512,
    "YituTech/conv-bert-small": 512,
}


UpperCAmelCase__					={
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}





class  lowerCamelCase__    (		_a     ):
      a			:  Optional[int]           =		VOCAB_FILES_NAMES
      a			:  Any           =		PRETRAINED_VOCAB_FILES_MAP
      a			:  Union[str, Any]           =		PRETRAINED_INIT_CONFIGURATION
      a			:  Dict           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a			:  List[Any]           =		ConvBertTokenizer
      def __init__(		self						:						str   ,							A_						:						Dict=None   ,							A_						:						Dict=None   ,							A_						:						Any=True   ,							A_						:						int="[UNK]"   ,							A_						:						Optional[int]="[SEP]"   ,							A_						:						Optional[int]="[PAD]"   ,							A_						:						str="[CLS]"   ,							A_						:						str="[MASK]"   ,							A_						:						int=True   ,							A_						:						str=None   ,							**A_						:						Optional[Any]   ,							):






       '''simple docstring'''


       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						=						json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
       if (
           normalizer_state.get("""lowercase"""   ,							A_				) != do_lower_case
           or normalizer_state.get("""strip_accents"""   ,							A_				) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""   ,							A_				) != tokenize_chinese_chars
       ):
        __lowercase						=						getattr(A_   ,							normalizer_state.pop("""type"""				)				)
        __lowercase						=						do_lower_case
        __lowercase						=						strip_accents
        __lowercase						=						tokenize_chinese_chars
        __lowercase						=						normalizer_class(**A_				)

       __lowercase						=						do_lower_case
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]   ,							A_						:						Optional[int]   ,							A_						:						List[str]=None				):






       '''simple docstring'''


       __lowercase						=						[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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						List[int]   ,							A_						:						Optional[List[int]] = None				):






       '''simple docstring'''


       __lowercase						=						[self.sep_token_id]
       __lowercase						=						[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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						str   ,							A_						:						Optional[str] = None				):






       '''simple docstring'''


       __lowercase						=						self._tokenizer.model.save(A_   ,							name=A_				)
       return tuple(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,
       )
