


'''simple docstring'''



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


__A	=logging.get_logger(__name__)

__A	='▁'

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

__A	={
    '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'
        ),
    }
}

__A	={
    'facebook/mbart-large-50-one-to-many-mmt': 10_24,
}

# fmt: off
__A	=['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					(     a__	):
					lowerCAmelCase      :Dict   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :Optional[int]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :int   =					['''input_ids''', '''attention_mask''']

					lowerCAmelCase      :List[int]   =					[]
					lowerCAmelCase      :List[int]   =					[]




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

								UpperCAmelCase__  :					Any        =     {} if sp_model_kwargs is None else sp_model_kwargs

								UpperCAmelCase__  :					List[str]        =     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=_lowerCamelCase							,							tgt_lang=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**_lowerCamelCase							,							)

								UpperCAmelCase__  :					int        =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(str(_lowerCamelCase))
								UpperCAmelCase__  :					int        =     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
								UpperCAmelCase__  :					List[Any]        =     {"""<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
								UpperCAmelCase__  :					str        =     1

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

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
								UpperCAmelCase__  :					List[str]        =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}

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




					@property
					def 				snake_case__						(      self):
								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):
								return self._src_lang




					@src_lang.setter
					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     new_src_lang
								self.set_src_lang_special_tokens(self._src_lang)




					def __getstate__(      self):
								UpperCAmelCase__  :					List[str]        =     self.__dict__.copy()
								UpperCAmelCase__  :					Optional[int]        =     None
								return state




					def __setstate__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     d

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

								UpperCAmelCase__  :					List[Any]        =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return self.sp_model.encode(_lowerCamelCase							,							out_type=_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase__  :					List[str]        =     self.sp_model.PieceToId(_lowerCamelCase)

								# 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							,							_lowerCamelCase):
								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							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     []
								UpperCAmelCase__  :					int        =     """"""
								UpperCAmelCase__  :					List[Any]        =     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(_lowerCamelCase) + token
														UpperCAmelCase__  :					List[str]        =     True
														UpperCAmelCase__  :					Optional[Any]        =     []
											else:
														current_sub_tokens.append(_lowerCamelCase)
														UpperCAmelCase__  :					Optional[int]        =     False
								out_string += self.sp_model.decode(_lowerCamelCase)
								return out_string.strip()




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__  :					Optional[Any]        =     os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

								return (out_vocab_file,)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

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

								UpperCAmelCase__  :					List[Any]        =     [1] * len(self.prefix_tokens)
								UpperCAmelCase__  :					Any        =     [1] * len(self.suffix_tokens)
								if token_ids_a is None:
											return prefix_ones + ([0] * len(_lowerCamelCase)) + suffix_ones
								return prefix_ones + ([0] * len(_lowerCamelCase)) + ([0] * len(_lowerCamelCase)) + suffix_ones




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = "en_XX"							,							_lowerCamelCase = None							,							_lowerCamelCase = "ro_RO"							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     src_lang
								UpperCAmelCase__  :					List[Any]        =     tgt_lang
								return super().prepare_seqaseq_batch(_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self):
								return self.set_src_lang_special_tokens(self.src_lang)




					def 				snake_case__						(      self):
								return self.set_tgt_lang_special_tokens(self.tgt_lang)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     self.lang_code_to_id[src_lang]
								UpperCAmelCase__  :					Optional[Any]        =     [self.cur_lang_code_id]
								UpperCAmelCase__  :					Optional[Any]        =     [self.eos_token_id]




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     self.lang_code_to_id[tgt_lang]
								UpperCAmelCase__  :					int        =     [self.cur_lang_code_id]
								UpperCAmelCase__  :					Optional[int]        =     [self.eos_token_id]



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i + 1][j] + matrix[i][j]					)

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums					)


if __name__ == "__main__":
		print(f"""{solution() = }""")