


def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	None:

       """simple docstring"""

       a_      :	Any							=						generate_pascal_triangle(__A       )
       for row_idx in range(__A       ):
              # Print left spaces
              for _ in range(num_rows - row_idx - 1       ):
                     print(end=' '       )
              # Print row values
              for col_idx in range(row_idx + 1       ):
                     if col_idx != row_idx:
                            print(triangle[row_idx][col_idx]						,					end=' '       )
                     else:
                            print(triangle[row_idx][col_idx]						,					end=''       )
              print()







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	list[list[int]]:

       """simple docstring"""

       if not isinstance(__A						,					__A       ):
              raise TypeError('The input value of \'num_rows\' should be \'int\''       )

       if num_rows == 0:
              return []
       elif num_rows < 0:
              raise ValueError(
                  'The input value of \'num_rows\' should be greater than or equal to 0'       )

       a_      :	list[list[int]]							=						[]
       for current_row_idx in range(__A       ):
              a_      :	Tuple							=						populate_current_row(__A						,					__A       )
              triangle.append(__A       )
       return triangle







def  SCREAMING_SNAKE_CASE_		(    __A				:    list[list[int]]						,					__A				:    int       )			->	list[int]:

       """simple docstring"""

       a_      :	Dict							=						[-1] * (current_row_idx + 1)
       # first and last elements of current row are equal to 1
       a_      :	Any							=						1, 1
       for current_col_idx in range(1						,					__A       ):
              calculate_current_element(
                  __A						,					__A						,					__A						,					__A       )
       return current_row







def  SCREAMING_SNAKE_CASE_		(    __A				:    list[list[int]]						,					__A				:    list[int]						,					__A				:    int						,					__A				:    int						,					)			->	None:

       """simple docstring"""

       a_      :	Optional[int]							=						triangle[current_row_idx - 1][current_col_idx - 1]
       a_      :	str							=						triangle[current_row_idx - 1][current_col_idx]
       a_      :	Optional[Any]							=						above_to_left_elt + above_to_right_elt







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	list[list[int]]:

       """simple docstring"""

       if not isinstance(__A						,					__A       ):
              raise TypeError('The input value of \'num_rows\' should be \'int\''       )

       if num_rows == 0:
              return []
       elif num_rows < 0:
              raise ValueError(
                  'The input value of \'num_rows\' should be greater than or equal to 0'       )

       a_      :	list[list[int]]							=						[[1]]

       for row_index in range(1						,					__A       ):
              a_      :	Optional[int]							=						[0] + result[-1] + [0]
              a_      :	Union[str, Any]							=						row_index + 1
              # Calculate the number of distinct elements in a row
              a_      :	Optional[int]							=						sum(divmod(__A						,					2       )       )
              a_      :	Any							=						[
                  temp_row[i - 1] + temp_row[i] for i in range(1						,					distinct_elements + 1       )
              ]
              a_      :	Optional[Any]							=						row_first_half[: (row_index + 1) // 2]
              row_second_half.reverse()
              a_      :	Optional[Any]							=						row_first_half + row_second_half
              result.append(__A       )

       return result







def  SCREAMING_SNAKE_CASE_		(    )			->	None:

       """simple docstring"""

       from collections.abc import Callable
       from timeit import timeit

       def benchmark_a_function(__A				:    Callable						,					__A				:    int       ) -> None:
              a_      :	Optional[int]							=						F"""{func.__name__}({value})"""
              a_      :	List[Any]							=						timeit(F"""__main__.{call}"""						,					setup='import __main__'       )
              # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
              print(F"""{call:38} -- {timing:.4f} seconds"""       )

       for value in range(15       ):  # (1, 7, 14):
              for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
                     benchmark_a_function(__A						,					__A       )
              print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()






from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_   :				List[Any]						      = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_   :				Optional[Any]						      = '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_   :				Optional[Any]						      = '\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 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             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.f1_score.html']  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="binary"  ,							SCREAMING_SNAKE_CASE__       : Dict=None  )	->    Any:
             a_      :	List[Any]							=						fa_score(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  ,							pos_label=SCREAMING_SNAKE_CASE__  ,							average=SCREAMING_SNAKE_CASE__  ,							sample_weight=SCREAMING_SNAKE_CASE__  )
             return {"f1": float(SCREAMING_SNAKE_CASE__  ) if score.size == 1 else score}


