



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)
_snake_case  :   List[Any]						   =  logging.getLogger(__name__)


_snake_case  :   Union[str, Any]						   =  'Hello world! cécé herlolip'


_snake_case  :   Optional[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   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     BertAbsConfig(
       temp_dir='.',     finetune_bert=lowerCamelCase__,     large=lowerCamelCase__,     share_emb=lowerCamelCase__,     use_bert_emb=lowerCamelCase__,     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,     )
   __lowerCAmelCase							     =     torch.load(lowerCamelCase__,     lambda lowerCAmelCase_,     lowerCAmelCase_     : storage     )
   __lowerCAmelCase							     =     AbsSummarizer(lowerCamelCase__,     torch.device('cpu'     ),     lowerCamelCase__     )
   original.eval()

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

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

   __lowerCAmelCase							     =     new_model(
       lowerCamelCase__,     lowerCamelCase__,     lowerCamelCase__,     lowerCamelCase__,     lowerCamelCase__     )[0]
   __lowerCAmelCase							     =     new_model.generator(lowerCamelCase__     )

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

   __lowerCAmelCase							     =     torch.allclose(lowerCamelCase__,     lowerCamelCase__,     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__":
     _snake_case  :   Dict						   =  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.',
     )
     _snake_case  :   Any						   =  parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

