
'''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)
__magic_name__            =  logging.getLogger(__name__)


__magic_name__            =  '''Hello world! cécé herlolip'''


__magic_name__            =  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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				BertAbsConfig(
        temp_dir="."    ,		finetune_bert=__snake_case    ,		large=__snake_case    ,		share_emb=__snake_case    ,		use_bert_emb=__snake_case    ,		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(__snake_case    ,		lambda __lowerCAmelCase    ,		__lowerCAmelCase							: storage							)
    snake_case__						=				AbsSummarizer(__snake_case    ,		torch.device("cpu"							)    ,		__snake_case							)
    original.eval()

    snake_case__						=				BertAbsSummarizer(__snake_case    ,		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(__snake_case							))							)
    snake_case__						=				torch.tensor(__snake_case							).unsqueeze(0							)
    snake_case__						=				tokenizer.encode("This is sample 3 éàalj\'-."							)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__snake_case							))							)
    snake_case__						=				torch.tensor(__snake_case							).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(__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case							)[0]
    snake_case__						=				original.generator(__snake_case							)

    snake_case__						=				new_model(
        __snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case    ,		__snake_case							)[0]
    snake_case__						=				new_model.generator(__snake_case							)

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

    snake_case__						=				torch.allclose(__snake_case    ,		__snake_case    ,		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__":
     __magic_name__            =  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.''',
     )
     __magic_name__            =  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:
     __magic_name__            =  None


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  '''▁'''

__magic_name__            =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__            =  {
    '''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'''
    },
}

__magic_name__            =  {
    '''google/pegasus-xsum''': 512,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		int    	=     VOCAB_FILES_NAMES
      _A						:		Dict    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Tuple    	=     PegasusTokenizer
      _A						:		str    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="<pad>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<mask_2>"		,	lowerCamelCase="<mask_1>"		,	lowerCamelCase=None		,	lowerCamelCase=1_03		,	**lowerCamelCase		,	):
          snake_case__						=				offset

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

              snake_case__						=				(
                  ([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(lowerCamelCase )		,	self.offset - 1 )
              ]

              if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
                  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}.""" )
              snake_case__						=				additional_special_tokens_extended
          else:
              snake_case__						=				[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__(
              lowerCamelCase		,	tokenizer_file=lowerCamelCase		,	pad_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	mask_token_sent=lowerCamelCase		,	offset=lowerCamelCase		,	additional_special_tokens=lowerCamelCase		,	**lowerCamelCase		,	)
          snake_case__						=				vocab_file
          snake_case__						=				False if not self.vocab_file else True
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):
          if already_has_special_tokens:
              return self._special_token_mask(lowerCamelCase )
          elif token_ids_a is None:
              return self._special_token_mask(lowerCamelCase ) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          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(lowerCamelCase ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



