





def A_	(		A__				)  ->		None:
       a__						:	Optional[int] =				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 A_	(		A__				)  ->		list[list[int]]:

       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__						:	Any =				populate_current_row(A__		,      A__				)
              triangle.append(A__				)
       return triangle



def A_	(		A__		,      A__				)  ->		list[int]:
       a__						:	Optional[int] =				[-1] * (current_row_idx + 1)
       # first and last elements of current row are equal to 1
       a__ ,							a__						:	str =				1, 1
       for current_col_idx in range(1		,      A__				):
              calculate_current_element(
                  A__		,      A__		,      A__		,      A__				)
       return current_row



def A_	(		A__		,      A__		,      A__		,      A__		,      )  ->		None:
       a__						:	Dict =				triangle[current_row_idx - 1][current_col_idx - 1]
       a__						:	List[str] =				triangle[current_row_idx - 1][current_col_idx]
       a__						:	str =				above_to_left_elt + above_to_right_elt



def A_	(		A__				)  ->		list[list[int]]:

       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__						:	str =				[0] + result[-1] + [0]
              a__						:	str =				row_index + 1
              # Calculate the number of distinct elements in a row
              a__						:	Optional[int] =				sum(divmod(A__		,      2				)				)
              a__						:	Dict =				[
                  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__						:	Any =				row_first_half + row_second_half
              result.append(A__				)

       return result



def A_	(		)  ->		None:
       from collections.abc import Callable
       from timeit import timeit

       def benchmark_a_function(A__		,      A__				) -> None:
              a__						:	List[str] =				F'{func.__name__}({value})'
              a__						:	Tuple =				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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





