
import os
from collections import deque

import torch
from torch.utils.data import Dataset







class      A__							(     __magic_name__	):






				def __init__(						self					:      Optional[Any]    ,     a					:      Tuple=""    ,     a					:      List[str]="train"   ):






							'''simple docstring'''







							assert os.path.isdir(a   )

							lowerCAmelCase__      :      Optional[int]         =		[]
							lowerCAmelCase__      :      List[str]         =		os.listdir(a   )
							for story_filename in story_filenames_list:
										if "summary" in story_filename:
													continue
										lowerCAmelCase__      :      Optional[int]         =		os.path.join(a    ,     a   )
										if not os.path.isfile(a   ):
													continue
										self.documents.append(a   )






				def __len__(						self					:      str   ):






							'''simple docstring'''







							return len(self.documents   )






				def __getitem__(						self					:      Tuple    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.documents[idx]
							lowerCAmelCase__      :      int         =		document_path.split('/'   )[-1]
							with open(a    ,     encoding='utf-8'   ) as source:
										lowerCAmelCase__      :      Tuple         =		source.read()
										lowerCAmelCase__      ,				lowerCAmelCase__      :      List[str]         =		process_story(a   )
							return document_name, story_lines, summary_lines






def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      Any         =		list(filter(lambda SCREAMING_SNAKE_CASE_     : len(SCREAMING_SNAKE_CASE_     ) != 0       ,							[line.strip() for line in raw_story.split('\n'     )]     )     )

			# for some unknown reason some lines miss a period, add it
			lowerCAmelCase__      :      int         =		[_add_missing_period(SCREAMING_SNAKE_CASE_     ) for line in nonempty_lines]

			# gather article lines
			lowerCAmelCase__      :      Dict         =		[]
			lowerCAmelCase__      :      Tuple         =		deque(SCREAMING_SNAKE_CASE_     )
			while True:
						try:
									lowerCAmelCase__      :      Any         =		lines.popleft()
									if element.startswith('@highlight'     ):
												break
									story_lines.append(SCREAMING_SNAKE_CASE_     )
						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
			lowerCAmelCase__      :      Any         =		list(filter(lambda SCREAMING_SNAKE_CASE_     : not t.startswith('@highlight'     )       ,							SCREAMING_SNAKE_CASE_     )     )

			return story_lines, summary_lines
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      Dict         =		['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
			if line.startswith('@highlight'     ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			if len(SCREAMING_SNAKE_CASE_     ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE_     ))     )
						return sequence
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Any:
			lowerCAmelCase__      :      Any         =		torch.ones_like(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		sequence == pad_token_id
			lowerCAmelCase__      :      str         =		0
			return mask
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			lowerCAmelCase__      :      List[str]         =		[tokenizer.encode(SCREAMING_SNAKE_CASE_     ) for line in story_lines]
			lowerCAmelCase__      :      Optional[Any]         =		[token for sentence in story_lines_token_ids for token in sentence]
			lowerCAmelCase__      :      List[Any]         =		[tokenizer.encode(SCREAMING_SNAKE_CASE_     ) for line in summary_lines]
			lowerCAmelCase__      :      Tuple         =		[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids







def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[str]:
			lowerCAmelCase__      :      int         =		[]
			for sequence in batch:
						lowerCAmelCase__      :      Union[str, Any]         =		-1
						lowerCAmelCase__      :      Tuple         =		[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2     )
						batch_embeddings.append(SCREAMING_SNAKE_CASE_     )
			return torch.tensor(SCREAMING_SNAKE_CASE_     )

from __future__ import annotations

lowerCamelCase__          =		list[list[int]]

# assigning initial values to the grid
lowerCamelCase__          =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__          =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		bool:
			for i in range(9     ):
						if grid[row][i] == n or grid[i][column] == n:
									return False

			for i in range(3     ):
						for j in range(3     ):
									if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
												return False

			return True
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		tuple[int, int] | None:
			for i in range(9     ):
						for j in range(9     ):
									if grid[i][j] == 0:
												return i, j
			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Matrix | None:
			if location := find_empty_location(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		location
			else:
						# If the location is ``None``, then the grid is solved.
						return grid

			for digit in range(1       ,							10     ):
						if is_safe(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
									lowerCAmelCase__      :      Optional[Any]         =		digit

									if sudoku(SCREAMING_SNAKE_CASE_     ) is not None:
												return grid

									lowerCAmelCase__      :      List[Any]         =		0

			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			for row in grid:
						for cell in row:
									print(SCREAMING_SNAKE_CASE_       ,							end=' '     )
						print()


if __name__ == "__main__":
					# make a copy of grid so that you can compare with the unmodified grid
					for example_grid in (initial_grid, no_solution):
										print("""\nExample grid:\n""" + """=""" * 20)
										print_solution(example_grid)
										print("""\nExample grid solution:""")
										lowerCamelCase__          =		sudoku(example_grid)
										if solution is not None:
															print_solution(solution)
										else:
															print("""Cannot find a solution.""")