

import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class      lowerCamelCase_ (			unittest.TestCase):




 @cached_property
 def     a       (			self							):
   return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'							)




 @cached_property
 def     a       (			self							):
   return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'							)




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.tokenizer_ab
   __UpperCAmelCase	:  str		=     ' I am a small frog.'
   __UpperCAmelCase	:  str		=     tok([src_text]  ,  padding=A_  ,  truncation=A_							)['input_ids']
   __UpperCAmelCase	:  List[Any]		=     tok.batch_decode(A_  ,  skip_special_tokens=A_  ,  clean_up_tokenization_spaces=A_							)[0]
   assert src_text == decoded




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.rust_tokenizer_ab
   __UpperCAmelCase	:  Any		=     ' I am a small frog.'
   __UpperCAmelCase	:  Optional[Any]		=     tok([src_text]  ,  padding=A_  ,  truncation=A_							)['input_ids']
   __UpperCAmelCase	:  str		=     tok.batch_decode(A_  ,  skip_special_tokens=A_  ,  clean_up_tokenization_spaces=A_							)[0]
   assert src_text == decoded




 def     a       (			self							):
   assert self.tokenizer_ab.add_prefix_space
   assert self.tokenizer_ab([' Sam', 'Sam']							).input_ids == [[5_502, 2], [5_502, 2]]




 def     a       (			self							):
   assert self.rust_tokenizer_ab.add_prefix_space
   assert self.rust_tokenizer_ab([' Sam', 'Sam']							).input_ids == [[5_502, 2], [5_502, 2]]








from sklearn.metrics import accuracy_score

import datasets


A__										=      "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


A__										=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


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







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


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[str]:
      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.accuracy_score.html"]  ,	)







  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


