

"""simple docstring"""

import random






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case ,				snake_case = 0.01 ,				snake_case = 1_000 ,				snake_case = -1 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							sample
        if len(self.sample      ) == 0:
         raise ValueError("Sample data can not be empty"      )
        lowerCAmelCase__      :							Optional[Any]    =							target
        if len(self.target      ) == 0:
         raise ValueError("Target data can not be empty"      )
        if len(self.sample      ) != len(self.target      ):
         raise ValueError("Sample data and Target data do not have matching lengths"      )
        lowerCAmelCase__      :							Tuple    =							learning_rate
        lowerCAmelCase__      :							int    =							epoch_number
        lowerCAmelCase__      :							Dict    =							bias
        lowerCAmelCase__      :							Union[str, Any]    =							len(snake_case      )
        lowerCAmelCase__      :							List[Any]    =							len(sample[0]      )  # number of columns in dataset
        lowerCAmelCase__      :							list    =							[]






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for sample in self.sample:
         sample.insert(0 ,				self.bias      )

        for _ in range(self.col_sample      ):
         self.weight.append(random.random()      )

        self.weight.insert(0 ,				self.bias      )

        lowerCAmelCase__      :							Optional[Any]    =							0

        while True:
         lowerCAmelCase__      :							Any    =							False
         for i in range(self.number_sample      ):
          lowerCAmelCase__      :							Dict    =							0
          for j in range(self.col_sample + 1      ):
           lowerCAmelCase__      :							Union[str, Any]    =							u + self.weight[j] * self.sample[i][j]
          lowerCAmelCase__      :							Dict    =							self.sign(snake_case      )
          if y != self.target[i]:
           for j in range(self.col_sample + 1      ):
            lowerCAmelCase__      :							Optional[Any]    =							(
                self.weight[j]
                + self.learning_rate
                * (self.target[i] - y)
                * self.sample[i][j]
            )
           lowerCAmelCase__      :							Tuple    =							True
            # print('Epoch: \n',epoch_count)
         lowerCAmelCase__      :							Tuple    =							epoch_count + 1
         # if you want control the epoch or just by error
         if not has_misclassified:
          print(("\nEpoch:\n", epoch_count)      )
          print("------------------------\n"      )
          # if epoch_count > self.epoch_number or not error:
          break






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        if len(self.sample      ) == 0:
         raise ValueError("Sample data can not be empty"      )
        sample.insert(0 ,				self.bias      )
        lowerCAmelCase__      :							int    =							0
        for i in range(self.col_sample + 1      ):
         lowerCAmelCase__      :							Any    =							u + self.weight[i] * sample[i]

        lowerCAmelCase__      :							Dict    =							self.sign(snake_case      )

        if y == -1:
         print(("Sample: ", sample)      )
         print("classification: P1"      )
        else:
         print(("Sample: ", sample)      )
         print("classification: P2"      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        return 1 if u >= 0 else -1




_UpperCamelCase		    =						[
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

_UpperCamelCase		    =						[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _UpperCamelCase		    =						Perceptron(
        sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
    )
    network.training()
    print("""Finished training perceptron""")
    print("""Enter values to predict or q to exit""")
    while True:
        _UpperCamelCase		    =						[]
        for i in range(len(samples[0])):
            _UpperCamelCase		    =						input("""value: """).strip()
            if user_input == "q":
                break
            _UpperCamelCase		    =						float(user_input)
            sample.insert(i, observation)
        network.sort(sample)









"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 			SCREAMING_SNAKE_CASE       (   ) ->	Any:
 def filter_log(lowercase__      ):
  return False if "This IS expected if you are initializing" in record.msg else True

 lowerCAmelCase__      :							Tuple    =							datasets.utils.logging.get_logger("transformers.modeling_utils"      )
 logger.addFilter(lowercase__      )
 try:
  yield
 finally:
  logger.removeFilter(lowercase__      )


_UpperCamelCase		    =						"""\
@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}
}
"""

_UpperCamelCase		    =						"""\
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.
"""

_UpperCamelCase		    =						"""
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  __a				(	datasets.Metric					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=64 ,				snake_case=4 ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=False ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							bert_score.utils.get_hash
        lowerCAmelCase__      :							List[Any]    =							bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
         lowerCAmelCase__      :							str    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
         lowerCAmelCase__      :							Tuple    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
        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"
         lowerCAmelCase__      :							Dict    =							bert_score.utils.langamodel[lang.lower()]

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

        lowerCAmelCase__      :							Optional[int]    =							get_hash(
            model=snake_case ,				num_layers=snake_case ,				idf=snake_case ,				rescale_with_baseline=snake_case ,				use_custom_baseline=baseline_path is not None ,				)

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

        ((lowerCAmelCase__) ,	(lowerCAmelCase__) ,	(lowerCAmelCase__))      :							int    =							self.cached_bertscorer.score(
            cands=snake_case ,				refs=snake_case ,				verbose=snake_case ,				batch_size=snake_case ,				)
        lowerCAmelCase__      :							List[str]    =							{
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if references is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							[[ref] if isinstance(snake_case ,				snake_case      ) else ref for ref in references]
        super().add_batch(predictions=snake_case ,				references=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				snake_case      ):
         lowerCAmelCase__      :							str    =							[reference]
        super().add(prediction=snake_case ,				reference=snake_case ,				**snake_case      )






