


'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__   ) ->    Any:
			UpperCAmelCase__					:   List[str]       				=       generate_pascal_triangle(UpperCamelCase__   )
			for row_idx in range(UpperCamelCase__   ):
						# 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       _UpperCamelCase (						UpperCamelCase__   ) ->    Optional[int]:



			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						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__					:   list[list[int]]       				=       []
			for current_row_idx in range(UpperCamelCase__   ):
						UpperCAmelCase__					:   str       				=       populate_current_row(UpperCamelCase__	, UpperCamelCase__   )
						triangle.append(UpperCamelCase__   )
			return triangle







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    List[Any]:
			UpperCAmelCase__					:   List[Any]       				=       [-1] * (current_row_idx + 1)
			# first and last elements of current row are equal to 1
			UpperCAmelCase__					:   Optional[int]       				=       1, 1
			for current_col_idx in range(1	, UpperCamelCase__   ):
						calculate_current_element(
						    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			return current_row







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, ) ->    Dict:
			UpperCAmelCase__					:   Optional[Any]       				=       triangle[current_row_idx - 1][current_col_idx - 1]
			UpperCAmelCase__					:   List[Any]       				=       triangle[current_row_idx - 1][current_col_idx]
			UpperCAmelCase__					:   Tuple       				=       above_to_left_elt + above_to_right_elt







def       _UpperCamelCase (						UpperCamelCase__   ) ->    List[str]:



			if not isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						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__					:   list[list[int]]       				=       [[1]]

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

			return result







def       _UpperCamelCase (						) ->    Dict:
			from collections.abc import Callable
			from timeit import timeit

			def benchmark_a_function(UpperCamelCase__	, UpperCamelCase__   ) -> None:
						UpperCAmelCase__					:   Tuple       				=       f'''{func.__name__}({value})'''
						UpperCAmelCase__					:   Dict       				=       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(1_5   ):  # (1, 7, 14):
						for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
									benchmark_a_function(UpperCamelCase__	, UpperCamelCase__   )
						print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))