



'''simple docstring'''

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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_nllb import NllbTokenizer
else:
	snake_case_			=     None


snake_case_			=     logging.get_logger(__name__)


snake_case_			=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case_			=     {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_			=     {
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
snake_case_			=     ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]


class 		a__     (				_lowercase	):
	__magic_name__		:    Optional[int]	    =     VOCAB_FILES_NAMES
	__magic_name__		:    int	    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__		:    Union[str, Any]	    =     PRETRAINED_VOCAB_FILES_MAP
	__magic_name__		:    int	    =     ["input_ids", "attention_mask"]
	__magic_name__		:    List[Any]	    =     NllbTokenizer

	__magic_name__		:    List[int]	    =     []
	__magic_name__		:    List[int]	    =     []


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= AddedToken(__UpperCAmelCase,					lstrip=__UpperCAmelCase,					rstrip=__UpperCAmelCase      ) if isinstance(__UpperCAmelCase,					__UpperCAmelCase      ) else mask_token
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= legacy_behaviour
					super().__init__(
					    vocab_file=__UpperCAmelCase,					tokenizer_file=__UpperCAmelCase,					bos_token=__UpperCAmelCase,					eos_token=__UpperCAmelCase,					sep_token=__UpperCAmelCase,					cls_token=__UpperCAmelCase,					unk_token=__UpperCAmelCase,					pad_token=__UpperCAmelCase,					mask_token=__UpperCAmelCase,					src_lang=__UpperCAmelCase,					tgt_lang=__UpperCAmelCase,					additional_special_tokens=__UpperCAmelCase,					legacy_behaviour=__UpperCAmelCase,					**__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						int      					= vocab_file
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= False if not self.vocab_file else True

					SCREAMING_SNAKE_CASE  :						Tuple      					= FAIRSEQ_LANGUAGE_CODES.copy()

					if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]      )

					self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {
					    lang_code: self.convert_tokens_to_ids(__UpperCAmelCase      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					SCREAMING_SNAKE_CASE  :						Tuple      					= src_lang if src_lang is not None else '''eng_Latn'''
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.convert_tokens_to_ids(self._src_lang      )
					SCREAMING_SNAKE_CASE  :						str      					= tgt_lang
					self.set_src_lang_special_tokens(self._src_lang      )


	@property
	def 				lowercase__  (self : Tuple      )     ->			str:

					"""simple docstring"""

					return self._src_lang


	@src_lang.setter
	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : str      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= new_src_lang
					self.set_src_lang_special_tokens(self._src_lang      )


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[int],					__UpperCAmelCase : Optional[List[int]] = None      )     ->			List[int]:

					"""simple docstring"""

					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 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : List[int],					__UpperCAmelCase : Optional[List[int]] = None      )     ->			List[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= [self.sep_token_id]
					SCREAMING_SNAKE_CASE  :						Tuple      					= [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]


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Dict,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[str],					__UpperCAmelCase : Optional[str],					**__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					if src_lang is None or tgt_lang is None:
									raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= src_lang
					SCREAMING_SNAKE_CASE  :						Any      					= self(__UpperCAmelCase,					add_special_tokens=__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.convert_tokens_to_ids(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= tgt_lang_id
					return inputs


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[str],					__UpperCAmelCase : str = "eng_Latn",					__UpperCAmelCase : Optional[List[str]] = None,					__UpperCAmelCase : str = "fra_Latn",					**__UpperCAmelCase : Any,					)     ->			BatchEncoding:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= src_lang
					SCREAMING_SNAKE_CASE  :						Tuple      					= tgt_lang
					return super().prepare_seqaseq_batch(__UpperCAmelCase,					__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			Optional[Any]:

					"""simple docstring"""

					return self.set_src_lang_special_tokens(self.src_lang      )


	def 				lowercase__  (self : int      )     ->			Any:

					"""simple docstring"""

					return self.set_tgt_lang_special_tokens(self.tgt_lang      )


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.convert_tokens_to_ids(__UpperCAmelCase      )

					if self.legacy_behaviour:
									SCREAMING_SNAKE_CASE  :						str      					= []
									SCREAMING_SNAKE_CASE  :						List[Any]      					= [self.eos_token_id, self.cur_lang_code]
					else:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= [self.cur_lang_code]
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= [self.eos_token_id]

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.convert_ids_to_tokens(self.prefix_tokens      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.convert_ids_to_tokens(self.suffix_tokens      )

					SCREAMING_SNAKE_CASE  :						Any      					= processors.TemplateProcessing(
					    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,					self.prefix_tokens + self.suffix_tokens      )      ),					)


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : str      )     ->			None:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.convert_tokens_to_ids(__UpperCAmelCase      )
					if self.legacy_behaviour:
									SCREAMING_SNAKE_CASE  :						Tuple      					= []
									SCREAMING_SNAKE_CASE  :						Dict      					= [self.eos_token_id, self.cur_lang_code]
					else:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [self.cur_lang_code]
									SCREAMING_SNAKE_CASE  :						Any      					= [self.eos_token_id]

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.convert_ids_to_tokens(self.prefix_tokens      )
					SCREAMING_SNAKE_CASE  :						int      					= self.convert_ids_to_tokens(self.suffix_tokens      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= processors.TemplateProcessing(
					    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,					self.prefix_tokens + self.suffix_tokens      )      ),					)






	def 				lowercase__  (self : Dict,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[str] = None      )     ->			Tuple[str]:

					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''      )

					if not os.path.isdir(__UpperCAmelCase      ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''      )
									return
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= os.path.join(
					    __UpperCAmelCase,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

					if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ):
									copyfile(self.vocab_file,					__UpperCAmelCase      )

					return (out_vocab_file,)









'''simple docstring'''

from __future__ import annotations

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

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

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

				while not found and not resign:
								if len(_SCREAMING_SNAKE_CASE       ) == 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()
												SCREAMING_SNAKE_CASE  :						List[str]      					= cell.pop()
												SCREAMING_SNAKE_CASE  :						List[str]      					= next_cell[2]
												SCREAMING_SNAKE_CASE  :						Dict      					= next_cell[3]
												SCREAMING_SNAKE_CASE  :						str      					= next_cell[1]

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

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								path.append(invpath[len(_SCREAMING_SNAKE_CASE       ) - 1 - i]       )
				return path, action


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

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

	# the cost map which pushes the path closer to the goal
	snake_case_			=     [[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])):
			snake_case_			=     abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				snake_case_			=     99

	snake_case_			,				snake_case_			=     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])




