

'''simple docstring'''







from sklearn.metrics import recall_score

import datasets


__lowerCamelCase		       =    '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''


__lowerCamelCase		       =    '''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {\'recall\': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {\'recall\': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {\'recall\': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'recall\': array([1., 0., 0.])}
'''


__lowerCamelCase		       =    '''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''


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



    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           return datasets.MetricInfo(
               description=_DESCRIPTION		,				citation=_CITATION		,				inputs_description=_KWARGS_DESCRIPTION		,				features=datasets.Features(
                   {
                       """predictions""": datasets.Sequence(datasets.Value("""int32"""       )       ),
                       """references""": datasets.Sequence(datasets.Value("""int32"""       )       ),
                   }
                   if self.config_name == """multilabel"""
                   else {
                       """predictions""": datasets.Value("""int32"""       ),
                       """references""": datasets.Value("""int32"""       ),
                   }       )		,				reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]		,				)







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__=1		,				UpperCamelCase__="binary"		,				UpperCamelCase__=None		,				UpperCamelCase__="warn"		,				)						->      Optional[int]:





           '''simple docstring'''
           A_						=      recall_score(
               UpperCamelCase__		,				UpperCamelCase__		,				labels=UpperCamelCase__		,				pos_label=UpperCamelCase__		,				average=UpperCamelCase__		,				sample_weight=UpperCamelCase__		,				zero_division=UpperCamelCase__		,				)
           return {"recall": float(UpperCamelCase__       ) if score.size == 1 else score}



'''simple docstring'''







import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Load configuration defined in the metadata file
       with open(UpperCAmelCase__				) as metadata_file:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      LukeConfig(use_entity_aware_attention=UpperCAmelCase__,       **metadata["""model_config"""]				)

       # Load in the weights from the checkpoint_path
       A_						=      torch.load(UpperCAmelCase__,       map_location="""cpu"""				)["""module"""]

       # Load the entity vocab file
       A_						=      load_original_entity_vocab(UpperCAmelCase__				)
       # add an entry for [MASK2]
       A_						=      max(entity_vocab.values()				) + 1
       config.entity_vocab_size += 1

       A_						=      XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]				)

       # Add special tokens to the token vocabulary for downstream tasks
       A_						=      AddedToken("""<ent>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       A_						=      AddedToken("""<ent2>""",       lstrip=UpperCAmelCase__,       rstrip=UpperCAmelCase__				)
       tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}				)
       config.vocab_size += 2

       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''				)
       tokenizer.save_pretrained(UpperCAmelCase__				)
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """r"""				) as f:
              A_						=      json.load(UpperCAmelCase__				)
       A_						=      """MLukeTokenizer"""
       with open(os.path.join(UpperCAmelCase__,       """tokenizer_config.json"""				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       with open(os.path.join(UpperCAmelCase__,       MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]				),       """w"""				) as f:
              json.dump(UpperCAmelCase__,       UpperCAmelCase__				)

       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)

       # Initialize the embeddings of the special tokens
       A_						=      tokenizer.convert_tokens_to_ids(["""@"""]				)[0]
       A_						=      tokenizer.convert_tokens_to_ids(["""#"""]				)[0]

       A_						=      state_dict["""embeddings.word_embeddings.weight"""]
       A_						=      word_emb[ent_init_index].unsqueeze(0				)
       A_						=      word_emb[enta_init_index].unsqueeze(0				)
       A_						=      torch.cat([word_emb, ent_emb, enta_emb]				)
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              A_						=      state_dict[bias_name]
              A_						=      decoder_bias[ent_init_index].unsqueeze(0				)
              A_						=      decoder_bias[enta_init_index].unsqueeze(0				)
              A_						=      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]				)

       # Initialize the query layers of the entity-aware self-attention mechanism
       for layer_index in range(config.num_hidden_layers				):
              for matrix_name in ["query.weight", "query.bias"]:
                     A_						=      F'''encoder.layer.{layer_index}.attention.self.'''
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]
                     A_						=      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       A_						=      state_dict["""entity_embeddings.entity_embeddings.weight"""]
       A_						=      entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_emb, entity_mask_emb]				)
       # add [MASK2] for 'entity_predictions.bias'
       A_						=      state_dict["""entity_predictions.bias"""]
       A_						=      entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0				)
       A_						=      torch.cat([entity_prediction_bias, entity_mask_bias]				)

       A_						=      LukeForMaskedLM(config=UpperCAmelCase__				).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.weight"""				)
       state_dict.pop("""lm_head.decoder.bias"""				)
       A_						=      OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""				) or key.startswith("""entity_predictions"""				)):
                     A_						=      state_dict[key]
              else:
                     A_						=      state_dict[key]

       A_					,			A_						=      model.load_state_dict(UpperCAmelCase__,       strict=UpperCAmelCase__				)

       if set(UpperCAmelCase__				) != {"luke.embeddings.position_ids"}:
              raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''				)
       if set(UpperCAmelCase__				) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(F'''Unexpected missing_keys: {missing_keys}'''				)

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__,       task="""entity_classification"""				)

       A_						=      """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       A_						=      (0, 9)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 33, 7_68)				)
              A_						=      torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

       if not (outputs.last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''				)
       if not torch.allclose(outputs.last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              A_						=      torch.Size((1, 1, 7_68)				)
              A_						=      torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

       if not (outputs.entity_last_hidden_state.shape == expected_shape):
              raise ValueError(
                  F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
                  F''' {expected_shape}'''				)
       if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3],       UpperCAmelCase__,       atol=1e-4				):
              raise ValueError

       # Verify masked word/entity prediction
       A_						=      MLukeTokenizer.from_pretrained(UpperCAmelCase__				)
       A_						=      """Tokyo is the capital of <mask>."""
       A_						=      (24, 30)
       A_						=      tokenizer(UpperCAmelCase__,       entity_spans=[span],       return_tensors="""pt"""				)

       A_						=      model(**UpperCAmelCase__				)

       A_						=      encoding["""input_ids"""][0].tolist()
       A_						=      input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""				)				)
       A_						=      outputs.logits[0][mask_position_id].argmax(dim=-1				)
       assert "Japan" == tokenizer.decode(UpperCAmelCase__				)

       A_						=      outputs.entity_logits[0][0].argmax().item()
       A_						=      [
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""				)][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(UpperCAmelCase__				)				)
       model.save_pretrained(UpperCAmelCase__				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      ["""[MASK]""", """[PAD]""", """[UNK]"""]

       A_						=      [json.loads(UpperCAmelCase__				) for line in open(UpperCAmelCase__				)]

       A_						=      {}
       for entry in data:
              A_						=      entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            A_						=      entity_id
                            break
                     A_						=      F'''{language}:{entity_name}'''
                     A_						=      entity_id
       return new_mapping


if __name__ == "__main__":
      __lowerCamelCase		       =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
      parser.add_argument(
          '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
      )
      parser.add_argument(
          '''--entity_vocab_path''',
          default=None,
          type=str,
          help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
      )
      parser.add_argument(
          '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
      )
      __lowerCamelCase		       =    parser.parse_args()
      convert_luke_checkpoint(
          args.checkpoint_path,
          args.metadata_path,
          args.entity_vocab_path,
          args.pytorch_dump_folder_path,
          args.model_size,
      )
