





import numpy as np







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     (0, 0)
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     0
						UpperCAmelCase_								=     0
						UpperCAmelCase_								=     0





					def __eq__(						self    :  Tuple ,					__lowercase    :  List[str]		):






						'''simple docstring'''







						return self.position == cell.position





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						print(self.position		)







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Tuple ,					__lowercase    :  Optional[Any]=(5, 5)		):






						'''simple docstring'''







						UpperCAmelCase_								=     np.zeros(__lowercase		)
						UpperCAmelCase_								=     world_size[0]
						UpperCAmelCase_								=     world_size[1]





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str]		):






						'''simple docstring'''







						print(self.w		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						UpperCAmelCase_								=     cell.position[0]
						UpperCAmelCase_								=     cell.position[1]
						UpperCAmelCase_								=     []
						for n in neughbour_cord:
							UpperCAmelCase_								=     current_x + n[0]
							UpperCAmelCase_								=     current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
								UpperCAmelCase_								=     Cell()
								UpperCAmelCase_								=     (x, y)
								UpperCAmelCase_								=     cell
								neighbours.append(__lowercase		)
						return neighbours




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     []
	_open.append(A   )

	while _open:
		UpperCAmelCase_								=     np.argmin([n.f for n in _open]   )
		UpperCAmelCase_								=     _open[min_f]
		_closed.append(_open.pop(A   )   )
		if current == goal:
			break
		for n in world.get_neigbours(A   ):
			for c in _closed:
				if c == n:
					continue
			UpperCAmelCase_								=     current.g + 1
			UpperCAmelCase_						,	UpperCAmelCase_								=     n.position
			UpperCAmelCase_						,	UpperCAmelCase_								=     goal.position
			UpperCAmelCase_								=     (ya - ya) ** 2 + (xa - xa) ** 2
			UpperCAmelCase_								=     n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(A   )
	UpperCAmelCase_								=     []
	while current.parent is not None:
		path.append(current.position   )
		UpperCAmelCase_								=     current.parent
	path.append(current.position   )
	return path[::-1]


if __name__ == "__main__":
				UpperCamelCase__					  = Gridworld()
				# Start position and goal
				UpperCamelCase__					  = Cell()
				UpperCamelCase__					  = (0, 0)
				UpperCamelCase__					  = Cell()
				UpperCamelCase__					  = (4, 4)
				print(f"path from {start.position} to {goal.position}")
				UpperCamelCase__					  = astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCamelCase__					  = 1
				print(world.w)









import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)

UpperCamelCase__   : int					  = """▁"""

UpperCamelCase__   : Dict					  = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

UpperCamelCase__   : Dict					  = {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

UpperCamelCase__   : Optional[int]					  = {"""vinai/bartpho-syllable""": 10_24}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     str			    =       VOCAB_FILES_NAMES
					lowerCamelCase     :     Optional[int]			    =       PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase     :     List[str]			    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase     :     Optional[Any]			    =       ['input_ids', 'attention_mask']





					def __init__(						self    :  List[str] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any] ,					__lowercase    :  List[str]="<s>" ,					__lowercase    :  str="</s>" ,					__lowercase    :  Optional[Any]="</s>" ,					__lowercase    :  Any="<s>" ,					__lowercase    :  Dict="<unk>" ,					__lowercase    :  int="<pad>" ,					__lowercase    :  str="<mask>" ,					__lowercase    :  Optional[Dict[str, Any]] = None ,					**__lowercase    :  Optional[Any] ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     AddedToken(__lowercase ,					lstrip=__lowercase ,					rstrip=__lowercase		) if isinstance(__lowercase ,					__lowercase		) else mask_token

						UpperCAmelCase_								=     {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=__lowercase ,					eos_token=__lowercase ,					unk_token=__lowercase ,					sep_token=__lowercase ,					cls_token=__lowercase ,					pad_token=__lowercase ,					mask_token=__lowercase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__lowercase ,					)

						UpperCAmelCase_								=     vocab_file
						UpperCAmelCase_								=     monolingual_vocab_file
						UpperCAmelCase_								=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(str(__lowercase		)		)

						# Load the reduced vocab

						# Keep order of special tokens for backward compatibility
						UpperCAmelCase_								=     {}
						UpperCAmelCase_								=     0
						for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
							if str(__lowercase		) not in self.fairseq_tokens_to_ids:
								UpperCAmelCase_								=     cnt
								cnt += 1
						with open(__lowercase ,					"""r""" ,					encoding="""utf-8"""		) as f:
							for line in f.readlines():
								UpperCAmelCase_								=     line.strip().split()[0]
								UpperCAmelCase_								=     len(self.fairseq_tokens_to_ids		)
						if str(__lowercase		) not in self.fairseq_tokens_to_ids:
							UpperCAmelCase_								=     len(self.fairseq_tokens_to_ids		)

						UpperCAmelCase_								=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}





					def __getstate__(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.__dict__.copy()
						UpperCAmelCase_								=     None
						UpperCAmelCase_								=     self.sp_model.serialized_model_proto()
						return state





					def __setstate__(						self    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     d

						# for backward compatibility
						if not hasattr(self ,					"""sp_model_kwargs"""		):
							UpperCAmelCase_								=     {}

						UpperCAmelCase_								=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						UpperCAmelCase_								=     [self.cls_token_id]
						UpperCAmelCase_								=     [self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None ,					__lowercase    :  bool = False		):






						'''simple docstring'''







						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowercase ,					token_ids_a=__lowercase ,					already_has_special_tokens=__lowercase		)

						if token_ids_a is None:
							return [1] + ([0] * len(__lowercase		)) + [1]
						return [1] + ([0] * len(__lowercase		)) + [1, 1] + ([0] * len(__lowercase		)) + [1]





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self.sep_token_id]
						UpperCAmelCase_								=     [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						return len(self.fairseq_ids_to_tokens		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     {self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str		):






						'''simple docstring'''







						return self.sp_model.encode(__lowercase ,					out_type=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Tuple		):






						'''simple docstring'''







						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						else:
							return self.unk_token_id





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return self.fairseq_ids_to_tokens[index]





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     """""".join(__lowercase		).replace(__lowercase ,					""" """		).strip()
						return out_string





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str ,					__lowercase    :  Optional[str] = None		):






						'''simple docstring'''







						if not os.path.isdir(__lowercase		):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
						UpperCAmelCase_								=     os.path.join(
						    __lowercase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
						UpperCAmelCase_								=     os.path.join(
						    __lowercase ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] ,					)

						if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowercase		) and os.path.isfile(self.vocab_file		):
							copyfile(self.vocab_file ,					__lowercase		)
						elif not os.path.isfile(self.vocab_file		):
							with open(__lowercase ,					"""wb"""		) as fi:
								UpperCAmelCase_								=     self.sp_model.serialized_model_proto()
								fi.write(__lowercase		)

						if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
						    __lowercase		) and os.path.isfile(self.monolingual_vocab_file		):
							copyfile(self.monolingual_vocab_file ,					__lowercase		)
						elif not os.path.isfile(self.monolingual_vocab_file		):
							with open(__lowercase ,					"""w""" ,					encoding="""utf-8"""		) as fp:
								for token in self.fairseq_tokens_to_ids:
									if token not in self.all_special_tokens:
										fp.write(F"""{str(__lowercase		)} \n"""		)

						return out_vocab_file, out_monolingual_vocab_file


