



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

import sentencepiece as spm

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


lowerCAmelCase__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  '''▁'''

lowerCAmelCase__					  =  {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase__					  =  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase__					  =  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
lowerCAmelCase__					  =  ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		VOCAB_FILES_NAMES
					lowercase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=		PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=		["""input_ids""", """attention_mask"""]

					lowercase_								=		[]
					lowercase_								=		[]







					def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     int=None				,       SCREAMING_SNAKE_CASE :     str=None				,       SCREAMING_SNAKE_CASE :     List[str]="</s>"				,       SCREAMING_SNAKE_CASE :     List[str]="</s>"				,       SCREAMING_SNAKE_CASE :     Tuple="<s>"				,       SCREAMING_SNAKE_CASE :     Optional[Any]="<unk>"				,       SCREAMING_SNAKE_CASE :     Tuple="<pad>"				,       SCREAMING_SNAKE_CASE :     int="<mask>"				,       SCREAMING_SNAKE_CASE :     Optional[Dict[str, Any]] = None				,       **SCREAMING_SNAKE_CASE :     Tuple				,       ):
									# Mask token behave like a normal word, i.e. include the space before it
									lowercase__					:   str													=					AddedToken(SCREAMING_SNAKE_CASE				,       lstrip=SCREAMING_SNAKE_CASE				,       rstrip=SCREAMING_SNAKE_CASE					) if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) else mask_token

									lowercase__					:   Optional[int]													=					{} if sp_model_kwargs is None else sp_model_kwargs

									lowercase__					:   Optional[int]													=					kwargs.get("additional_special_tokens"				,       []					)
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=SCREAMING_SNAKE_CASE				,       tgt_lang=SCREAMING_SNAKE_CASE				,       eos_token=SCREAMING_SNAKE_CASE				,       unk_token=SCREAMING_SNAKE_CASE				,       sep_token=SCREAMING_SNAKE_CASE				,       cls_token=SCREAMING_SNAKE_CASE				,       pad_token=SCREAMING_SNAKE_CASE				,       mask_token=SCREAMING_SNAKE_CASE				,       sp_model_kwargs=self.sp_model_kwargs				,       **SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   Tuple													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE					)					)
									lowercase__					:   Tuple													=					vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									lowercase__					:   Optional[int]													=					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									lowercase__					:   List[Any]													=					1

									lowercase__					:   Tuple													=					len(self.sp_model					)
									lowercase__					:   str													=					{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE					)
									}
									lowercase__					:   List[Any]													=					{v: k for k, v in self.lang_code_to_id.items()}
									lowercase__					:   int													=					len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
									lowercase__					:   str													=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}

									lowercase__					:   Optional[int]													=					src_lang if src_lang is not None else "en_XX"
									lowercase__					:   Optional[int]													=					self.lang_code_to_id[self._src_lang]
									lowercase__					:   Optional[Any]													=					tgt_lang
									self.set_src_lang_special_tokens(self._src_lang					)







					@property
					def 			snake_case					(							self :     int					):
									return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token







					@property
					def 			snake_case					(							self :     Tuple					):
									return self._src_lang







					@src_lang.setter
					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   int													=					new_src_lang
									self.set_src_lang_special_tokens(self._src_lang					)







					def __getstate__(							self :     int					):
									lowercase__					:   Tuple													=					self.__dict__.copy()
									lowercase__					:   List[Any]													=					None
									return state







					def __setstate__(							self :     str				,       SCREAMING_SNAKE_CASE :     Dict					):
									lowercase__					:   Optional[Any]													=					d

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

									lowercase__					:   Dict													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Optional[int]													=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     str					):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE				,       out_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str					):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									lowercase__					:   str													=					self.sp_model.PieceToId(SCREAMING_SNAKE_CASE					)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     int					):
									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   int													=					[]
									lowercase__					:   str													=					""
									lowercase__					:   str													=					False
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	if not prev_is_special:
																					out_string += " "
																	out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE					) + token
																	lowercase__					:   Optional[Any]													=					True
																	lowercase__					:   Optional[int]													=					[]
													else:
																	current_sub_tokens.append(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Tuple													=					False
									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE					)
									return out_string.strip()







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str] = None					):
									if not os.path.isdir(SCREAMING_SNAKE_CASE					):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
									lowercase__					:   Union[str, Any]													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

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

									return (out_vocab_file,)







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

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

									lowercase__					:   Any													=					[1] * len(self.prefix_tokens					)
									lowercase__					:   Optional[Any]													=					[1] * len(self.suffix_tokens					)
									if token_ids_a is None:
													return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE					)) + suffix_ones
									return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE					)) + ([0] * len(SCREAMING_SNAKE_CASE					)) + suffix_ones







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str]				,       SCREAMING_SNAKE_CASE :     Optional[str]				,       **SCREAMING_SNAKE_CASE :     Any					):
									if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"					)
									lowercase__					:   int													=					src_lang
									lowercase__					:   Optional[Any]													=					self(SCREAMING_SNAKE_CASE				,       add_special_tokens=SCREAMING_SNAKE_CASE				,       return_tensors=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					tgt_lang_id
									return inputs







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str = "en_XX"				,       SCREAMING_SNAKE_CASE :     Optional[List[str]] = None				,       SCREAMING_SNAKE_CASE :     str = "ro_RO"				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
									lowercase__					:   Optional[Any]													=					src_lang
									lowercase__					:   int													=					tgt_lang
									return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									return self.set_src_lang_special_tokens(self.src_lang					)







					def 			snake_case					(							self :     str					):
									return self.set_tgt_lang_special_tokens(self.tgt_lang					)







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   str													=					self.lang_code_to_id[src_lang]
									lowercase__					:   Any													=					[self.cur_lang_code_id]
									lowercase__					:   Optional[Any]													=					[self.eos_token_id]







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     str					):
									lowercase__					:   Optional[Any]													=					self.lang_code_to_id[tgt_lang]
									lowercase__					:   Union[str, Any]													=					[self.cur_lang_code_id]
									lowercase__					:   Any													=					[self.eos_token_id]









from __future__ import annotations

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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the reference grid
				lowercase__					:   List[Any]													=					1
				lowercase__					:   Tuple													=					[
				    [0 for col in range(len(grid[0]						)						)] for row in range(len(lowerCamelCase__						)						)
				]  # the action grid

				lowercase__					:   Union[str, Any]													=					init[0]
				lowercase__					:   List[str]													=					init[1]
				lowercase__					:   Optional[Any]													=					0
				lowercase__					:   Optional[int]													=					g + heuristic[x][y]  # cost from starting cell to destination cell
				lowercase__					:   Tuple													=					[[f, g, x, y]]

				lowercase__					:   Union[str, Any]													=					False  # flag that is set when search is complete
				lowercase__					:   Any													=					False  # flag set if we can't find expand

				while not found and not resign:
								if len(lowerCamelCase__						) == 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()
												lowercase__					:   Tuple													=					cell.pop()
												lowercase__					:   Optional[Any]													=					next_cell[2]
												lowercase__					:   int													=					next_cell[3]
												lowercase__					:   Union[str, Any]													=					next_cell[1]

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

				lowercase__					:   List[str]													=					[]
				for i in range(len(lowerCamelCase__						)						):
								path.append(invpath[len(lowerCamelCase__						) - 1 - i]						)
				return path, action


if __name__ == "__main__":
				lowerCAmelCase__					  =  [
				    [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],
				]

				lowerCAmelCase__					  =  [0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__					  =  [len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__					  =  1

				# the cost map which pushes the path closer to the goal
				lowerCAmelCase__					  =  [[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])):
												lowerCAmelCase__					  =  abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																lowerCAmelCase__					  =  9_9

				lowerCAmelCase__  ,						lowerCAmelCase__					  =  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])




