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


SCREAMING_SNAKE_CASE							:int							 =				'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE							:Any							 =				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 						UpperCAmelCase			(	a_		,   a_						)		->	Dict:







				"""simple docstring"""



				__A   			= BertAbsConfig(
				    temp_dir="."		,   finetune_bert=a_		,   large=a_		,   share_emb=a_		,   use_bert_emb=a_		,   encoder="bert"		,   max_pos=5_1_2		,   enc_layers=6		,   enc_hidden_size=5_1_2		,   enc_heads=8		,   enc_ff_size=5_1_2		,   enc_dropout=0.2		,   dec_layers=6		,   dec_hidden_size=7_6_8		,   dec_heads=8		,   dec_ff_size=2_0_4_8		,   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] * (5_1_2 - len(a_						))						)
				__A   			= torch.tensor(a_						).unsqueeze(0						)
				__A   			= tokenizer.encode("This is sample 3 éàalj'-."						)
				decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - 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__":
					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							:List[str]							 =				parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)







'''simple docstring'''




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase = "▁"						,	_UpperCAmelCase = True						,	_UpperCAmelCase = "<unk>"						,	_UpperCAmelCase = "</s>"						,	_UpperCAmelCase = "<pad>"						,	):

          '''simple docstring'''






          __A						: Dict      =       {
              'pad': {'id': 0, 'token': pad_token},
              'eos': {'id': 1, 'token': eos_token},
              'unk': {'id': 2, 'token': unk_token},
          }

          __A						: List[Any]      =       [None] * len(self.special_tokens)
          for token_dict in self.special_tokens.values():
               __A						: List[str]      =       token_dict['token']

          __A						: str      =       Tokenizer(Unigram())

          __A						: Dict      =       normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(' {2,}')						,	' '),
                  normalizers.Lowercase(),
              ])
          __A						: Union[str, Any]      =       pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase),
                  pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
                  pre_tokenizers.Punctuation(),
              ])
          __A						: Any      =       decoders.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase)

          __A						: Dict      =       TemplateProcessing(
              single=F'$A {self.special_tokens["eos"]["token"]}'						,	special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]						,	)

          __A						: Any      =       {
              'model': 'SentencePieceUnigram',
              'replacement': replacement,
              'add_prefix_space': add_prefix_space,
          }

          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: str      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
               __A						: Union[str, Any]      =       [files]
          self._tokenizer.train(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: Dict      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          self._tokenizer.train_from_iterator(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       json.loads(self._tokenizer.to_str())

          __A						: Union[str, Any]      =       self.special_tokens['unk']['id']

          __A						: str      =       Tokenizer.from_str(json.dumps(_UpperCAmelCase))