






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
 import tensorflow as tf

 from ..tf_utils import stable_softmax


if is_torch_available():
 import torch


snake_case__   :						List[Any]													=   logging.get_logger(__name__)


@add_end_docstrings(
    UpperCAmelCase__    ,							r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """    ,							)
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			np.ndarray:
     if self.framework == "tf":
        UpperCamelCase_						=  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
     elif self.framework == "pt":
        UpperCamelCase_						=  torch.nonzero(input_ids == self.tokenizer.mask_token_id		,		as_tuple=_UpperCAmelCase			)
     else:
        raise ValueError('Unsupported framework'			)
     return masked_index




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			np.ndarray:
     UpperCamelCase_						=  self.get_masked_index(_UpperCAmelCase			)
     UpperCamelCase_						=  np.prod(masked_index.shape			)
     if numel < 1:
        raise PipelineException(
            'fill-mask'		,		self.model.base_model_prefix		,		f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Any:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        for model_input in model_inputs:
           self._ensure_exactly_one_mask_token(model_input['input_ids'][0]			)
     else:
        for input_ids in model_inputs["input_ids"]:
           self._ensure_exactly_one_mask_token(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			Dict[str, GenericTensor]:
     if return_tensors is None:
        UpperCamelCase_						=  self.framework
     UpperCamelCase_						=  self.tokenizer(_UpperCAmelCase		,		return_tensors=_UpperCAmelCase			)
     self.ensure_exactly_one_mask_token(_UpperCAmelCase			)
     return model_inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.model(**_UpperCAmelCase			)
     UpperCamelCase_						=  model_inputs['input_ids']
     return model_outputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=5		,		_UpperCAmelCase=None			)      ->			Any:
     # Cap top_k if there are targets
     if target_ids is not None and target_ids.shape[0] < top_k:
        UpperCamelCase_						=  target_ids.shape[0]
     UpperCamelCase_						=  model_outputs['input_ids'][0]
     UpperCamelCase_						=  model_outputs['logits']

     if self.framework == "tf":
        UpperCamelCase_						=  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()[:, 0]

        UpperCamelCase_						=  outputs.numpy()

        UpperCamelCase_						=  outputs[0, masked_index, :]
        UpperCamelCase_						=  stable_softmax(_UpperCAmelCase		,		axis=-1			)
        if target_ids is not None:
           UpperCamelCase_						=  tf.gather_nd(tf.squeeze(_UpperCAmelCase		,		0			)		,		target_ids.reshape(-1		,		1			)			)
           UpperCamelCase_						=  tf.expand_dims(_UpperCAmelCase		,		0			)

        UpperCamelCase_						=  tf.math.top_k(_UpperCAmelCase		,		k=_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  topk.values.numpy(), topk.indices.numpy()
     else:
        UpperCamelCase_						=  torch.nonzero(input_ids == self.tokenizer.mask_token_id		,		as_tuple=_UpperCAmelCase			).squeeze(-1			)
        # Fill mask pipeline supports only one ${mask_token} per sample

        UpperCamelCase_						=  outputs[0, masked_index, :]
        UpperCamelCase_						=  logits.softmax(dim=-1			)
        if target_ids is not None:
           UpperCamelCase_						=  probs[..., target_ids]

        UpperCamelCase_		,   UpperCamelCase_						=  probs.topk(_UpperCAmelCase			)

     UpperCamelCase_						=  []
     UpperCamelCase_						=  values.shape[0] == 1
     for i, (_values, _predictions) in enumerate(zip(values.tolist()		,		predictions.tolist()			)			):
        UpperCamelCase_						=  []
        for v, p in zip(_values		,		_predictions			):
           # Copy is important since we're going to modify this array in place
           UpperCamelCase_						=  input_ids.numpy().copy()
           if target_ids is not None:
              UpperCamelCase_						=  target_ids[p].tolist()

           UpperCamelCase_						=  p
           # Filter padding out:
           UpperCamelCase_						=  tokens[np.where(tokens != self.tokenizer.pad_token_id			)]
           # Originally we skip special tokens to give readable output.
           # For multi masks though, the other [MASK] would be removed otherwise
           # making the output look odd, so we add them back
           UpperCamelCase_						=  self.tokenizer.decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
           UpperCamelCase_						=  {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]			), 'sequence': sequence}
           row.append(_UpperCAmelCase			)
        result.append(_UpperCAmelCase			)
     if single_mask:
        return result[0]
     return result




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			Any:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  [targets]
     try:
        UpperCamelCase_						=  self.tokenizer.get_vocab()
     except Exception:
        UpperCamelCase_						=  {}
     UpperCamelCase_						=  []
     for target in targets:
        UpperCamelCase_						=  vocab.get(_UpperCAmelCase		,		_UpperCAmelCase			)
        if id_ is None:
           UpperCamelCase_						=  self.tokenizer(
               _UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_attention_mask=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase		,		max_length=1		,		truncation=_UpperCAmelCase		,		)['input_ids']
           if len(_UpperCAmelCase			) == 0:
              logger.warning(
                  f"""The specified target token `{target}` does not exist in the model vocabulary. """
                  'We cannot replace it with anything meaningful, ignoring it'			)
              continue
           UpperCamelCase_						=  input_ids[0]
           # XXX: If users encounter this pass
           # it becomes pretty slow, so let's make sure
           # The warning enables them to fix the input to
           # get faster performance.
           logger.warning(
               f"""The specified target token `{target}` does not exist in the model vocabulary. """
               f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_			)}`."""			)
        target_ids.append(id_			)
     UpperCamelCase_						=  list(set(_UpperCAmelCase			)			)
     if len(_UpperCAmelCase			) == 0:
        raise ValueError('At least one target must be provided when passed.'			)
     UpperCamelCase_						=  np.array(_UpperCAmelCase			)
     return target_ids




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None			)      ->			List[str]:
     UpperCamelCase_						=  {}

     if targets is not None:
        UpperCamelCase_						=  self.get_target_ids(_UpperCAmelCase		,		_UpperCAmelCase			)
        UpperCamelCase_						=  target_ids

     if top_k is not None:
        UpperCamelCase_						=  top_k

     if self.tokenizer.mask_token_id is None:
        raise PipelineException(
            'fill-mask'		,		self.model.base_model_prefix		,		'The tokenizer does not define a `mask_token`.'			)
     return {}, {}, postprocess_params




  def __call__(  self		,		_UpperCAmelCase		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase			)
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			) and len(_UpperCAmelCase			) == 1:
        return outputs[0]
     return outputs













import datasets

from .evaluate import evaluate


snake_case__   :						int													=   """\
@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}
}
"""

snake_case__   :						Union[str, Any]													=   """
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.
"""

snake_case__   :						Any													=   """
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     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     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		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     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=_UpperCAmelCase		,		predictions=_UpperCAmelCase			)
     return score





