
def 		A_       (							snake_case							:      int       )					->			None:



						'''simple docstring'''

						__UpperCamelCase        =	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_       (							snake_case							:      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'''       )

						__UpperCamelCase        =	[]
						for current_row_idx in range(__A       ):
												__UpperCamelCase        =	populate_current_row(__A       ,					__A       )
												triangle.append(__A       )
						return triangle




def 		A_       (							snake_case							:      list[list[int]]       ,					snake_case							:      int       )					->			list[int]:



						'''simple docstring'''

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




def 		A_       (							snake_case							:      list[list[int]]       ,					snake_case							:      list[int]       ,					snake_case							:      int       ,					snake_case							:      int       ,					)					->			None:



						'''simple docstring'''

						__UpperCamelCase        =	triangle[current_row_idx - 1][current_col_idx - 1]
						__UpperCamelCase        =	triangle[current_row_idx - 1][current_col_idx]
						__UpperCamelCase        =	above_to_left_elt + above_to_right_elt




def 		A_       (							snake_case							:      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'''       )

						__UpperCamelCase        =	[[1]]

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

						return result




def 		A_       (							)					->			None:



						'''simple docstring'''

						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(snake_case							:      Callable       ,					snake_case							:      int       ) -> None:
												__UpperCamelCase        =	f"{func.__name__}({value})"
												__UpperCamelCase        =	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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ : int    		=       {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : int    		=       ["DebertaTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : List[str]    		=       [
        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaForMaskedLM",
        "DebertaForQuestionAnswering",
        "DebertaForSequenceClassification",
        "DebertaForTokenClassification",
        "DebertaModel",
        "DebertaPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Any    		=       [
        "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaForMaskedLM",
        "TFDebertaForQuestionAnswering",
        "TFDebertaForSequenceClassification",
        "TFDebertaForTokenClassification",
        "TFDebertaModel",
        "TFDebertaPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Dict    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






