






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def        lowerCamelCase__   (					):
  def filter_log(__lowerCamelCase    :  Optional[int]	):
    return False if "This IS expected if you are initializing" in record.msg else True

  __UpperCAmelCase							:			List[Any]     =						datasets.utils.logging.get_logger("""transformers.modeling_utils"""	)
  logger.addFilter(__lowerCamelCase	)
  try:
    yield
  finally:
    logger.removeFilter(__lowerCamelCase	)


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

a	: List[str]      =				"\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

a	: List[str]      =				"\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"







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



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    Any      )							->   List[Any]:
        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 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    int	,   __lowercase      :    Any	,   __lowercase      :    Union[str, Any]=None	,   __lowercase      :    Any=None	,   __lowercase      :    Union[str, Any]=None	,   __lowercase      :    Any=False	,   __lowercase      :    str=False	,   __lowercase      :    Tuple=None	,   __lowercase      :    str=64	,   __lowercase      :    List[Any]=4	,   __lowercase      :    Tuple=False	,   __lowercase      :    int=False	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Dict=False	,   )							->   int:
        __UpperCAmelCase							:			Tuple     =						bert_score.utils.get_hash
        __UpperCAmelCase							:			int     =						bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("""0.3.10"""      ):
          __UpperCAmelCase							:			List[str]     =						functools.partial(__lowercase	,   use_fast_tokenizer=__lowercase      )
          __UpperCAmelCase							:			Optional[int]     =						functools.partial(__lowercase	,   use_fast_tokenizer=__lowercase      )
        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"
          __UpperCAmelCase							:			int     =						bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
          __UpperCAmelCase							:			int     =						bert_score.utils.modelalayers[model_type]

        __UpperCAmelCase							:			Optional[Any]     =						get_hash(
            model=__lowercase	,   num_layers=__lowercase	,   idf=__lowercase	,   rescale_with_baseline=__lowercase	,   use_custom_baseline=baseline_path is not None	,   )

        with filter_logging_context():
          if not hasattr(self	,   """cached_bertscorer"""      ) or self.cached_bertscorer.hash != hashcode:
            __UpperCAmelCase							:			int     =						scorer(
                model_type=__lowercase	,   num_layers=__lowercase	,   batch_size=__lowercase	,   nthreads=__lowercase	,   all_layers=__lowercase	,   idf=__lowercase	,   device=__lowercase	,   lang=__lowercase	,   rescale_with_baseline=__lowercase	,   baseline_path=__lowercase	,   )

        ((__UpperCAmelCase)   ,				(__UpperCAmelCase)   ,				(__UpperCAmelCase))							:			Tuple     =						self.cached_bertscorer.score(
            cands=__lowercase	,   refs=__lowercase	,   verbose=__lowercase	,   batch_size=__lowercase	,   )
        __UpperCAmelCase							:			Optional[int]     =						{
            """precision""": P.tolist(),
            """recall""": R.tolist(),
            """f1""": F.tolist(),
            """hashcode""": hashcode,
        }
        return output_dict
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Any=None	,   __lowercase      :    List[str]=None	,   **__lowercase      :    int      )							->   Dict:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if references is not None:
          __UpperCAmelCase							:			int     =						[[ref] if isinstance(__lowercase	,   __lowercase      ) else ref for ref in references]
        super().add_batch(predictions=__lowercase	,   references=__lowercase	,   **__lowercase      )




      def 			UpperCAmelCase   (   self      :    Dict	,   __lowercase      :    int=None	,   __lowercase      :    Optional[Any]=None	,   **__lowercase      :    str      )							->   Union[str, Any]:
        # References can be strings or lists of strings
        # Let's change strings to lists of strings with one element
        if isinstance(__lowercase	,   __lowercase      ):
          __UpperCAmelCase							:			int     =						[reference]
        super().add(prediction=__lowercase	,   reference=__lowercase	,   **__lowercase      )










class 					a					:



      """simple docstring"""


      def __init__(   self      :    int	,   __lowercase      :    int      )							->   None:
        __UpperCAmelCase							:			str     =						data
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None






def        lowerCamelCase__   (					__lowerCamelCase    :  BinaryTreeNode | None						,	__lowerCamelCase    :  int	):
  if node is None:
    __UpperCAmelCase							:			Optional[int]     =						BinaryTreeNode(__lowerCamelCase	)
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase							:			int     =						insert(node.left_child						,	__lowerCamelCase	)
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase							:			Dict     =						insert(node.right_child						,	__lowerCamelCase	)
  return node






def        lowerCamelCase__   (					__lowerCamelCase    :  None | BinaryTreeNode	):  # if node is None,return
  if node:
    __UpperCAmelCase							:			Optional[Any]     =						inorder(node.left_child	)
    __UpperCAmelCase							:			str     =						[*inorder_array, node.data]
    __UpperCAmelCase							:			Optional[Any]     =						inorder_array + inorder(node.right_child	)
  else:
    __UpperCAmelCase							:			Tuple     =						[]
  return inorder_array






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Any     =						insert(__lowerCamelCase						,	15	)
  insert(__lowerCamelCase						,	10	)
  insert(__lowerCamelCase						,	25	)
  insert(__lowerCamelCase						,	6	)
  insert(__lowerCamelCase						,	14	)
  insert(__lowerCamelCase						,	20	)
  insert(__lowerCamelCase						,	60	)
  return root






def        lowerCamelCase__   (					):
  # main function
  __UpperCAmelCase							:			int     =						make_tree()
  print("""Printing values of binary search tree in Inorder Traversal."""	)
  inorder(__lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


