



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

UpperCamelCase__   : Dict					  = logging.get_logger(__name__)


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




def    A_(			A				,      A				,      A				,      A   ):
	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:
		UpperCAmelCase_								=     TOKENIZER_CLASSES
	else:
		UpperCAmelCase_								=     {tokenizer_name: getattr(A				,      tokenizer_name + """Fast"""   )}

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

	for tokenizer_name in tokenizer_names:
		UpperCAmelCase_								=     TOKENIZER_CLASSES[tokenizer_name]

		UpperCAmelCase_								=     True
		if checkpoint_name is None:
			UpperCAmelCase_								=     list(tokenizer_class.max_model_input_sizes.keys()   )
		else:
			UpperCAmelCase_								=     [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
			UpperCAmelCase_								=     tokenizer_class.from_pretrained(A				,      force_download=A   )

			# 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:
				UpperCAmelCase_						,	UpperCAmelCase_								=     checkpoint.split("""/"""   )
				UpperCAmelCase_								=     os.path.join(A				,      A   )
			elif add_prefix:
				UpperCAmelCase_								=     checkpoint
				UpperCAmelCase_								=     dump_path
			else:
				UpperCAmelCase_								=     None
				UpperCAmelCase_								=     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]:
				UpperCAmelCase_								=     list(tokenizer.pretrained_vocab_files_map.values()   )[0][checkpoint]
				UpperCAmelCase_								=     file_path.split(A   )[-1][0]
				if next_char == "/":
					UpperCAmelCase_								=     os.path.join(A				,      A   )
					UpperCAmelCase_								=     None

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

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

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


if __name__ == "__main__":
				UpperCamelCase__   : List[Any]					  = 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.""",
				)
				UpperCamelCase__   : Union[str, Any]					  = parser.parse_args()

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









from __future__ import annotations

UpperCamelCase__   : str					  = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the action grid

	UpperCAmelCase_								=     init[0]
	UpperCAmelCase_								=     init[1]
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     g + heuristic[x][y]  # cost from starting cell to destination cell
	UpperCAmelCase_								=     [[f, g, x, y]]

	UpperCAmelCase_								=     False  # flag that is set when search is complete
	UpperCAmelCase_								=     False  # flag set if we can't find expand

	while not found and not resign:
		if len(A   ) == 0:
			raise ValueError("""Algorithm is unable to find solution"""   )
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			UpperCAmelCase_								=     cell.pop()
			UpperCAmelCase_								=     next_cell[2]
			UpperCAmelCase_								=     next_cell[3]
			UpperCAmelCase_								=     next_cell[1]

			if x == goal[0] and y == goal[1]:
				UpperCAmelCase_								=     True
			else:
				for i in range(len(A   )   ):  # to try out different valid actions
					UpperCAmelCase_								=     x + DIRECTIONS[i][0]
					UpperCAmelCase_								=     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(A   ) and ya >= 0 and ya < len(grid[0]   ):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							UpperCAmelCase_								=     g + cost
							UpperCAmelCase_								=     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]   )
							UpperCAmelCase_								=     1
							UpperCAmelCase_								=     i
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     goal[0]
	UpperCAmelCase_								=     goal[1]
	invpath.append([x, y]   )  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		UpperCAmelCase_								=     x - DIRECTIONS[action[x][y]][0]
		UpperCAmelCase_								=     y - DIRECTIONS[action[x][y]][1]
		UpperCAmelCase_								=     xa
		UpperCAmelCase_								=     ya
		invpath.append([x, y]   )

	UpperCAmelCase_								=     []
	for i in range(len(A   )   ):
		path.append(invpath[len(A   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				UpperCamelCase__   : Dict					  = [0, 0]
				# all coordinates are given in format [y,x]
				UpperCamelCase__   : Optional[int]					  = [len(grid) - 1, len(grid[0]) - 1]
				UpperCamelCase__   : Optional[Any]					  = 1

				# the cost map which pushes the path closer to the goal
				UpperCamelCase__   : Any					  = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												UpperCamelCase__   : Any					  = abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																UpperCamelCase__   : Dict					  = 99

				UpperCamelCase__  ,			UpperCamelCase__   : Dict					  = search(grid, init, goal, cost, heuristic)

				print("""ACTION MAP""")
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])


