




'''simple docstring'''





from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
  import pyximport

  pyximport.install(setup_args={'''include_dirs''': np.get_include()})
  from . import algos_graphormer  # noqa E402





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_ = 512						):
    _a				:							Optional[int]							       =      x.shape[1] if len(x.shape						) > 1 else 1
    _a				:							Any							       =      1 + np.arange(0   ,	feature_num * offset   ,	UpperCAmelCase_   ,	dtype=np.intaa						)
    _a				:							str							       =      x + feature_offset
    return x





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=True						):
    requires_backends(UpperCAmelCase_   ,	["""cython"""]						)

    if keep_features and "edge_attr" in item.keys():  # edge_attr
        _a				:							Optional[Any]							       =      np.asarray(item["""edge_attr"""]   ,	dtype=np.intaa						)
    else:
        _a				:							str							       =      np.ones((len(item["""edge_index"""][0]						), 1)   ,	dtype=np.intaa						)  # same embedding for all

    if keep_features and "node_feat" in item.keys():  # input_nodes
        _a				:							str							       =      np.asarray(item["""node_feat"""]   ,	dtype=np.intaa						)
    else:
        _a				:							str							       =      np.ones((item["""num_nodes"""], 1)   ,	dtype=np.intaa						)  # same embedding for all

    _a				:							Dict							       =      np.asarray(item["""edge_index"""]   ,	dtype=np.intaa						)

    _a				:							Union[str, Any]							       =      convert_to_single_emb(UpperCAmelCase_						) + 1
    _a				:							List[Any]							       =      item["""num_nodes"""]

    if len(edge_attr.shape						) == 1:
        _a				:							Any							       =      edge_attr[:, None]
    _a				:							List[Any]							       =      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]   ,	dtype=np.intaa						)
    _a				:							str							       =      convert_to_single_emb(UpperCAmelCase_						) + 1

    # node adj matrix [num_nodes, num_nodes] bool
    _a				:							Any							       =      np.zeros([num_nodes, num_nodes]   ,	dtype=UpperCAmelCase_						)
    _a				:							Dict							       =      True

    _a       , _a				:							Dict							       =      algos_graphormer.floyd_warshall(UpperCAmelCase_						)
    _a				:							Tuple							       =      np.amax(UpperCAmelCase_						)

    _a				:							Union[str, Any]							       =      algos_graphormer.gen_edge_input(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
    _a				:							Dict							       =      np.zeros([num_nodes + 1, num_nodes + 1]   ,	dtype=np.single						)  # with graph token

    # combine
    _a				:							str							       =      input_nodes + 1  # we shift all indices by one for padding
    _a				:							Union[str, Any]							       =      attn_bias
    _a				:							str							       =      attn_edge_type
    _a				:							Union[str, Any]							       =      shortest_path_result.astype(np.intaa						) + 1  # we shift all indices by one for padding
    _a				:							List[Any]							       =      np.sum(UpperCAmelCase_   ,	axis=1						).reshape(-1						) + 1  # we shift all indices by one for padding
    _a				:							Optional[int]							       =      item["""in_degree"""]  # for undirected graph
    _a				:							int							       =      input_edges + 1  # we shift all indices by one for padding
    if "labels" not in item:
        _a				:							str							       =      item["""y"""]

    return item





class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase=20		,     __lowerCAmelCase=False ):







      '''simple docstring'''


      if not is_cython_available():
          raise ImportError("""Graphormer preprocessing needs Cython (pyximport)""" )

      _a				:							str							       =      spatial_pos_max
      _a				:							List[Any]							       =      on_the_fly_processing



  def __call__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if self.on_the_fly_processing:
          _a				:							Optional[int]							       =      [preprocess_item(__lowerCAmelCase ) for i in features]

      if not isinstance(features[0]		,     __lowerCAmelCase ):
          _a				:							Union[str, Any]							       =      [vars(__lowerCAmelCase ) for f in features]
      _a				:							Optional[Any]							       =      {}

      _a				:							List[str]							       =      max(len(i["""input_nodes"""] ) for i in features )
      _a				:							Dict							       =      len(features[0]["""input_nodes"""][0] )
      _a				:							Optional[int]							       =      len(features[0]["""attn_edge_type"""][0][0] )
      _a				:							List[Any]							       =      max(len(i["""input_edges"""][0][0] ) for i in features )
      _a				:							Dict							       =      len(features[0]["""input_edges"""][0][0][0] )
      _a				:							Union[str, Any]							       =      len(__lowerCAmelCase )

      _a				:							str							       =      torch.zeros(__lowerCAmelCase		,     max_node_num + 1		,     max_node_num + 1		,     dtype=torch.float )
      _a				:							List[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Optional[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							List[Any]							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Tuple							       =      torch.zeros(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )
      _a				:							Tuple							       =      torch.zeros(
          __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dtype=torch.long )

      for ix, f in enumerate(__lowerCAmelCase ):
          for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
              _a				:							Tuple							       =      torch.tensor(f[k] )

          if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max] ) > 0:
              _a				:							List[str]							       =      float("""-inf""" )

          _a				:							List[str]							       =      f["""attn_bias"""]
          _a				:							Any							       =      f[
              """attn_edge_type"""
          ]
          _a				:							Dict							       =      f["""spatial_pos"""]
          _a				:							Dict							       =      f["""in_degree"""]
          _a				:							str							       =      f["""input_nodes"""]
          _a				:							List[Any]							       =      f["""input_edges"""]

      _a				:							Union[str, Any]							       =      batch["""in_degree"""]

      _a				:							Optional[Any]							       =      features[0]["""labels"""]
      if len(__lowerCAmelCase ) == 1:  # one task
          if isinstance(sample[0]		,     __lowerCAmelCase ):  # regression
              _a				:							List[str]							       =      torch.from_numpy(np.concatenate([i["""labels"""] for i in features] ) )
          else:  # binary classification
              _a				:							Any							       =      torch.from_numpy(np.concatenate([i["""labels"""] for i in features] ) )
      else:  # multi task classification, left to float to keep the NaNs
          _a				:							int							       =      torch.from_numpy(np.stack([i["""labels"""] for i in features]		,     axis=0 ) )

      return batch








'''simple docstring'''





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def UpperCAmelCase    (       ):
    def filter_log(UpperCAmelCase_						):
        return False if "This IS expected if you are initializing" in record.msg else True

    _a				:							Optional[Any]							       =      datasets.utils.logging.get_logger("""transformers.modeling_utils"""						)
    logger.addFilter(UpperCAmelCase_						)
    try:
        yield
    finally:
        logger.removeFilter(UpperCAmelCase_						)


a		:	Tuple       =      '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

a		:	List[str]       =      '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

a		:	Dict       =      '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''





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



  def     a_							(			self ):







      '''simple docstring'''


      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     homepage="""https://github.com/Tiiiger/bert_score"""		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""		,     id="""sequence""" ),
                  """references""": datasets.Sequence(datasets.Value("""string"""		,     id="""sequence""" )		,     id="""references""" ),
              } )		,     codebase_urls=["""https://github.com/Tiiiger/bert_score"""]		,     reference_urls=[
              """https://github.com/Tiiiger/bert_score""",
              """https://arxiv.org/abs/1904.09675""",
          ]		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=64		,     __lowerCAmelCase=4		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      bert_score.utils.get_hash
      _a				:							int							       =      bert_score.BERTScorer

      if version.parse(bert_score.__version__ ) >= version.parse("""0.3.10""" ):
          _a				:							Union[str, Any]							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
          _a				:							Dict							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
      elif use_fast_tokenizer:
          raise ImportWarning(
              """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
              """You can install it with `pip install \"bert-score>=0.3.10\"`.""" )

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          _a				:							List[str]							       =      bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          _a				:							Dict							       =      bert_score.utils.modelalayers[model_type]

      _a				:							Union[str, Any]							       =      get_hash(
          model=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     use_custom_baseline=baseline_path is not None		,     )

      with filter_logging_context():
          if not hasattr(self		,     """cached_bertscorer""" ) or self.cached_bertscorer.hash != hashcode:
              _a				:							Optional[Any]							       =      scorer(
                  model_type=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     nthreads=__lowerCAmelCase		,     all_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     device=__lowerCAmelCase		,     lang=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     baseline_path=__lowerCAmelCase		,     )

      ((_a)       , (_a)       , (_a))				:							Optional[Any]							       =      self.cached_bertscorer.score(
          cands=__lowerCAmelCase		,     refs=__lowerCAmelCase		,     verbose=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     )
      _a				:							Tuple							       =      {
          """precision""": P.tolist(),
          """recall""": R.tolist(),
          """f1""": F.tolist(),
          """hashcode""": hashcode,
      }
      return output_dict



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if references is not None:
          _a				:							Dict							       =      [[ref] if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else ref for ref in references]
      super().add_batch(predictions=__lowerCAmelCase		,     references=__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [reference]
      super().add(prediction=__lowerCAmelCase		,     reference=__lowerCAmelCase		,     **__lowerCAmelCase )


