

def        snake_case__		(    lowerCamelCase_		):
							A      :					Any      		=					generate_pascal_triangle(lowerCamelCase_		)
							for row_idx in range(lowerCamelCase_		):
														# 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        snake_case__		(    lowerCamelCase_		):

							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														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(lowerCamelCase_		):
														A      :					Tuple      		=					populate_current_row(lowerCamelCase_     ,				lowerCamelCase_		)
														triangle.append(lowerCamelCase_		)
							return triangle






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






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				):
							A      :					str      		=					triangle[current_row_idx - 1][current_col_idx - 1]
							A      :					Optional[Any]      		=					triangle[current_row_idx - 1][current_col_idx]
							A      :					str      		=					above_to_left_elt + above_to_right_elt






def        snake_case__		(    lowerCamelCase_		):

							if not isinstance(lowerCamelCase_     ,				lowerCamelCase_		):
														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     ,				lowerCamelCase_		):
														A      :					Tuple      		=					[0] + result[-1] + [0]
														A      :					List[str]      		=					row_index + 1
														# Calculate the number of distinct elements in a row
														A      :					Union[str, Any]      		=					sum(divmod(lowerCamelCase_     ,				2		)		)
														A      :					str      		=					[
														    temp_row[i - 1] + temp_row[i] for i in range(1     ,				distinct_elements + 1		)
														]
														A      :					List[Any]      		=					row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														A      :					Optional[int]      		=					row_first_half + row_second_half
														result.append(lowerCamelCase_		)

							return result






def        snake_case__		(    ):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(lowerCamelCase_     ,				lowerCamelCase_		) -> None:
														A      :					Any      		=					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(lowerCamelCase_     ,				lowerCamelCase_		)
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()







"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





