




"""simple docstring"""



from __future__ import annotations

import time

SCREAMING_SNAKE_CASE    =	list[tuple[int, int]]

SCREAMING_SNAKE_CASE    =	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

SCREAMING_SNAKE_CASE    =	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right




class      UpperCAmelCase_  :




				def __init__(   self						:	int     ,			snake_case_						:	int     ,			snake_case_						:	int     ,			snake_case_						:	int     ,			snake_case_						:	int     ,			snake_case_						:	Node | None				)				->							int:




					'''simple docstring'''

					A__	=    pos_x
					A__	=    pos_y
					A__	=    (pos_y, pos_x)
					A__	=    goal_x
					A__	=    goal_y
					A__	=    parent






class      UpperCAmelCase_  :




				def __init__(   self						:	Optional[int]     ,			snake_case_						:	tuple[int, int]     ,			snake_case_						:	tuple[int, int]				)				->							Any:




					'''simple docstring'''

					A__	=    Node(start[1]     ,			start[0]     ,			goal[1]     ,			goal[0]     ,			snake_case_				)
					A__	=    Node(goal[1]     ,			goal[0]     ,			goal[1]     ,			goal[0]     ,			snake_case_				)

					A__	=    [self.start]
					A__	=    False




				def 							__magic_name__							(   self						:	Tuple				)				->							Path | None:




					'''simple docstring'''

					while self.node_queue:
						A__	=    self.node_queue.pop(0				)

						if current_node.pos == self.target.pos:
							A__	=    True
							return self.retrace_path(snake_case_				)

						A__	=    self.get_successors(snake_case_				)

						for node in successors:
							self.node_queue.append(snake_case_				)

					if not self.reached:
						return [self.start.pos]
					return None




				def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Node				)				->							list[Node]:




					'''simple docstring'''

					A__	=    []
					for action in delta:
						A__	=    parent.pos_x + action[1]
						A__	=    parent.pos_y + action[0]
						if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(snake_case_				) - 1):
							continue

						if grid[pos_y][pos_x] != 0:
							continue

						successors.append(
						    Node(snake_case_     ,			snake_case_     ,			self.target.pos_y     ,			self.target.pos_x     ,			snake_case_				)				)
					return successors




				def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Node | None				)				->							Path:




					'''simple docstring'''

					A__	=    node
					A__	=    []
					while current_node is not None:
						path.append((current_node.pos_y, current_node.pos_x)				)
						A__	=    current_node.parent
					path.reverse()
					return path






class      UpperCAmelCase_  :




				def __init__(   self						:	List[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	int				)				->							Union[str, Any]:




					'''simple docstring'''

					A__	=    BreadthFirstSearch(snake_case_     ,			snake_case_				)
					A__	=    BreadthFirstSearch(snake_case_     ,			snake_case_				)
					A__	=    False




				def 							__magic_name__							(   self						:	str				)				->							Path | None:




					'''simple docstring'''

					while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
						A__	=    self.fwd_bfs.node_queue.pop(0				)
						A__	=    self.bwd_bfs.node_queue.pop(0				)

						if current_bwd_node.pos == current_fwd_node.pos:
							A__	=    True
							return self.retrace_bidirectional_path(
							    snake_case_     ,			snake_case_				)

						A__	=    current_bwd_node
						A__	=    current_fwd_node

						A__	=    {
						    self.fwd_bfs: self.fwd_bfs.get_successors(snake_case_				),
						    self.bwd_bfs: self.bwd_bfs.get_successors(snake_case_				),
						}

						for bfs in [self.fwd_bfs, self.bwd_bfs]:
							for node in successors[bfs]:
								bfs.node_queue.append(snake_case_				)

					if not self.reached:
						return [self.fwd_bfs.start.pos]
					return None




				def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Node     ,			snake_case_						:	Node				)				->							Path:




					'''simple docstring'''

					A__	=    self.fwd_bfs.retrace_path(snake_case_				)
					A__	=    self.bwd_bfs.retrace_path(snake_case_				)
					bwd_path.pop()
					bwd_path.reverse()
					A__	=    fwd_path + bwd_path
					return path


if __name__ == "__main__":
						# all coordinates are given in format [y,x]
						import doctest

						doctest.testmod()
						SCREAMING_SNAKE_CASE    =	(0, 0)
						SCREAMING_SNAKE_CASE    =	(len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						SCREAMING_SNAKE_CASE    =	time.time()
						SCREAMING_SNAKE_CASE    =	BreadthFirstSearch(init, goal)
						SCREAMING_SNAKE_CASE    =	bfs.search()
						SCREAMING_SNAKE_CASE    =	time.time() - start_bfs_time

						print("Unidirectional BFS computation time : ", bfs_time)

						SCREAMING_SNAKE_CASE    =	time.time()
						SCREAMING_SNAKE_CASE    =	BidirectionalBreadthFirstSearch(init, goal)
						SCREAMING_SNAKE_CASE    =	bd_bfs.search()
						SCREAMING_SNAKE_CASE    =	time.time() - start_bd_bfs_time

						print("Bidirectional BFS computation time : ", bd_bfs_time)











"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}."""						)

 if tokenizer_name is None:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

 logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""						)

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [checkpoint_name]

  logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""						)

  for checkpoint in checkpoint_names:
   logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""						)

   # Load tokenizer
   A__	=    tokenizer_class.from_pretrained(lowercase_      ,			force_download=lowercase_						)

   # Save fast tokenizer
   logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""						)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    dump_path

   logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

    logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   A__	=    tokenizer.save_pretrained(
       lowercase_      ,			legacy_format=lowercase_      ,			filename_prefix=lowercase_						)
   logger.info(f"""=> File names {file_names}"""						)

   for file_name in file_names:
    if not file_name.endswith("tokenizer.json"						):
     os.remove(lowercase_						)
     logger.info(f"""=> removing {file_name}"""						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
      )
      parser.add_argument(
          "--tokenizer_name",
          default=None,
          type=str,
          help=(
              f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              "download and convert all the checkpoints from AWS."
          ),
      )
      parser.add_argument(
          "--checkpoint_name",
          default=None,
          type=str,
          help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
      )
      parser.add_argument(
          "--force_download",
          action="store_true",
          help="Re-download checkpoints.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





