






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__lowerCamelCase	:		Optional[int]										=   """Usage of script: script_name <size_of_canvas:int>"""

__lowerCamelCase	:		int										=   [0] * 1_00 + [1] * 10
random.shuffle(choice)







def 			A__							(      _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =[[False for i in range(lowerCamelCase_	)] for j in range(lowerCamelCase_	)]
 return canvas







def 			A__							(      _a  :					list[list[bool]]	):





 '''simple docstring'''







 for i, row in enumerate(lowerCamelCase_	):
  for j, _ in enumerate(lowerCamelCase_	):
   snake_case__  :    Optional[int]							       =bool(random.getrandbits(1	)	)







def 			A__							(      _a  :					list[list[bool]]	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =np.array(lowerCamelCase_	)
 snake_case__  :    List[Any]							       =np.array(create_canvas(current_canvas.shape[0]	)	)
 for r, row in enumerate(lowerCamelCase_	):
  for c, pt in enumerate(lowerCamelCase_	):
   snake_case__  :    int							       =__judge_point(
       lowerCamelCase_		,  current_canvas[r - 1 : r + 2, c - 1 : c + 2]	)

 snake_case__  :    Optional[int]							       =next_gen_canvas
 del next_gen_canvas  # cleaning memory as we move on.
 snake_case__  :    Optional[int]							       =current_canvas.tolist()
 return return_canvas







def 			A__							(      _a  :					bool		,  _a  :					list[list[bool]]	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =0
 snake_case__  :    str							       =0
 # finding dead or alive neighbours count.
 for i in neighbours:
  for status in i:
   if status:
    alive += 1
   else:
    dead += 1

    # handling duplicate entry for focus pt.
 if pt:
  alive -= 1
 else:
  dead -= 1

 # running the rules of game here.
 snake_case__  :    Optional[Any]							       =pt
 if pt:
  if alive < 2:
   snake_case__  :    List[Any]							       =False
  elif alive == 2 or alive == 3:
   snake_case__  :    Tuple							       =True
  elif alive > 3:
   snake_case__  :    Any							       =False
 else:
  if alive == 3:
   snake_case__  :    Optional[int]							       =True

 return state


if __name__ == "__main__":
       if len(sys.argv) != 2:
              raise Exception(usage_doc)

       __lowerCamelCase	:		str										=   int(sys.argv[1])
       # main working structure of this module.
       __lowerCamelCase	:		Union[str, Any]										=   create_canvas(canvas_size)
       seed(c)
       __lowerCamelCase			,				__lowerCamelCase	:		Optional[Any]										=   plt.subplots()
       fig.show()
       __lowerCamelCase	:		Dict										=   ListedColormap(["""w""", """k"""])
       try:
              while True:
                     __lowerCamelCase	:		Union[str, Any]										=   run(c)
                     ax.matshow(c, cmap=cmap)
                     fig.canvas.draw()
                     ax.cla()
       except KeyboardInterrupt:
              # do nothing.
              pass













from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__lowerCamelCase	:		Any										=   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__lowerCamelCase	:		str										=   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__lowerCamelCase	:		str										=   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 return float((preds == labels).mean()	)







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
                   """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
               }   )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)



      def 						lowercase__					(     self				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       else:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )





