

'''simple docstring'''

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 				lowercase (					):



					'''simple docstring'''







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

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


a       :      Union[str, Any]  	=			"\\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       :      Union[str, Any]  	=			"\\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       :      Tuple  	=			"\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   UpperCamelCase__   (       datasets.Metric       ):


		"""simple docstring"""

		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   ,      snake_case   ,      snake_case   ,      snake_case=None   ,      snake_case=None   ,      snake_case=None   ,      snake_case=False   ,      snake_case=False   ,      snake_case=None   ,      snake_case=6_4   ,      snake_case=4   ,      snake_case=False   ,      snake_case=False   ,      snake_case=None   ,      snake_case=False   ,      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       bert_score.utils.get_hash
							UpperCAmelCase		:							Optional[int]        =       bert_score.BERTScorer

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

							if num_layers is None:
												UpperCAmelCase		:							Optional[Any]        =       bert_score.utils.modelalayers[model_type]

							UpperCAmelCase		:							List[Any]        =       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:
																	UpperCAmelCase		:							int        =       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   ,      )

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







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


							'''simple docstring'''

							if references is not None:
												UpperCAmelCase		:							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 				A_	(			self   ,      snake_case=None   ,      snake_case=None   ,      **snake_case      ):


							'''simple docstring'''

							if isinstance(snake_case   ,      snake_case      ):
												UpperCAmelCase		:							Optional[int]        =       [reference]
							super().add(prediction=snake_case   ,      reference=snake_case   ,      **snake_case      )



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
