





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)
lowercase    : Optional[Any]    	=  logging.getLogger(__name__)


lowercase    : Any    	=  """Hello world! cécé herlolip"""


lowercase    : List[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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						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=2_048    ,       dec_dropout=0.2    ,       )
       _UpperCamelCase              =						torch.load(lowerCAmelCase    ,       lambda lowerCAmelCase    ,       lowerCAmelCase					: storage					)
       _UpperCamelCase              =						AbsSummarizer(lowerCAmelCase    ,       torch.device('''cpu'''					)    ,       lowerCAmelCase					)
       original.eval()

       _UpperCamelCase              =						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'''					)
       _UpperCamelCase              =						BertTokenizer.from_pretrained('''bert-base-uncased'''					)

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

       _UpperCamelCase              =						new_model(
           lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					)[0]
       _UpperCamelCase              =						new_model.generator(lowerCAmelCase					)

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

       _UpperCamelCase              =						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__":
       lowercase    : Optional[Any]    	=  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.""",
       )
       lowercase    : List[Any]    	=  parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )













import datasets

from .evaluate import evaluate


lowercase    : str    	=  """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

lowercase    : Tuple    	=  """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

lowercase    : List[str]    	=  """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION )
class  __A(						datasets.Metric ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return datasets.MetricInfo(
                description=_DESCRIPTION,       citation=_CITATION,       inputs_description=_KWARGS_DESCRIPTION,       features=datasets.Features(
                    {
                        '''predictions''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
                        },
                        '''references''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''answers''': datasets.features.Sequence(
                                {
                                    '''text''': datasets.Value('''string'''      ),
                                    '''answer_start''': datasets.Value('''int32'''      ),
                                }      ),
                        },
                    }      ),       codebase_urls=['''https://www.atticusprojectai.org/cuad'''],       reference_urls=['''https://www.atticusprojectai.org/cuad'''],       )



     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
            _UpperCamelCase              =						[
                {
                    '''paragraphs''': [
                        {
                            '''qas''': [
                                {
                                    '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                    '''id''': ref['''id'''],
                                }
                                for ref in references
                            ]
                        }
                    ]
                }
            ]
            _UpperCamelCase              =						evaluate(dataset=A,       predictions=A      )
            return score






