





def 	lowerCAmelCase_					(			__a						)	->						None:







  """simple docstring"""





  lowerCamelCase__: List[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 	lowerCAmelCase_					(			__a						)	->						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"						)

  lowerCamelCase__: list[list[int]]         =[]
  for current_row_idx in range(__a						):
    lowerCamelCase__: Any         =populate_current_row(__a      ,       __a						)
    triangle.append(__a						)
  return triangle





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						list[int]:







  """simple docstring"""





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





def 	lowerCAmelCase_					(			__a      ,       __a      ,       __a      ,       __a      ,       )	->						None:







  """simple docstring"""





  lowerCamelCase__: Dict         =triangle[current_row_idx - 1][current_col_idx - 1]
  lowerCamelCase__: List[Any]         =triangle[current_row_idx - 1][current_col_idx]
  lowerCamelCase__: int         =above_to_left_elt + above_to_right_elt





def 	lowerCAmelCase_					(			__a						)	->						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"						)

  lowerCamelCase__: list[list[int]]         =[[1]]

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

  return result





def 	lowerCAmelCase_					(			)	->						None:







  """simple docstring"""





  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__a      ,       __a						) -> None:
    lowerCamelCase__: Union[str, Any]         =F"""{func.__name__}({value})"""
    lowerCamelCase__: 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()








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



