





'''simple docstring'''




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__					:			Optional[int]          =     logging.get_logger(__name__)

a__					:			List[Any]          =     '''▁'''

a__					:			Optional[Any]          =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

a__					:			int          =     {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

a__					:			Dict          =     {
    '''facebook/xglm-564M''': 2048,
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  VOCAB_FILES_NAMES
	__lowerCAmelCase       						=  PRETRAINED_VOCAB_FILES_MAP
	__lowerCAmelCase       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__lowerCAmelCase       						=  ['''input_ids''', '''attention_mask''']





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_="<s>"      ,       UpperCamelCase_="</s>"      ,       UpperCamelCase_="</s>"      ,       UpperCamelCase_="<s>"      ,       UpperCamelCase_="<unk>"      ,       UpperCamelCase_="<pad>"      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		None:
		snake_case__	       =   {} if sp_model_kwargs is None else sp_model_kwargs

		# Compatibility with the original tokenizer
		snake_case__	       =   7
		snake_case__	       =   [F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

		snake_case__	       =   kwargs.get('additional_special_tokens'      ,       []		)
		kwargs["additional_special_tokens"] += [
		    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
		]

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

		snake_case__	       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
		self.sp_model.Load(str(UpperCamelCase_		)		)
		snake_case__	       =   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'

		# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
		snake_case__	       =   1

		# Mimic fairseq token-to-id alignment for the first 4 token
		snake_case__	       =   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

		snake_case__	       =   len(self.sp_model		)
		snake_case__	       =   {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
		self.fairseq_tokens_to_ids.update(UpperCamelCase_		)

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





	def __getstate__(		self		)      ->		List[Any]:
		snake_case__	       =   self.__dict__.copy()
		snake_case__	       =   None
		snake_case__	       =   self.sp_model.serialized_model_proto()
		return state





	def __setstate__(		self      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   d

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		List[int]:

		if token_ids_a is None:
			return [self.sep_token_id] + token_ids_a
		snake_case__	       =   [self.sep_token_id]
		return sep + token_ids_a + sep + sep + token_ids_a





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = False		)      ->		List[int]:

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		List[int]:
		snake_case__	       =   [self.sep_token_id]

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





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   {self.convert_ids_to_tokens(UpperCamelCase_		): i for i in range(self.vocab_size		)}
		vocab.update(self.added_tokens_encoder		)
		return vocab





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		List[str]:
		return self.sp_model.encode(UpperCamelCase_      ,       out_type=UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		if token in self.fairseq_tokens_to_ids:
			return self.fairseq_tokens_to_ids[token]
		snake_case__	       =   self.sp_model.PieceToId(UpperCamelCase_		)

		# 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      ,       UpperCamelCase_		)      ->		Tuple:
		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      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   ''.join(UpperCamelCase_		).replace(UpperCamelCase_      ,       ' '		).strip()
		return out_string





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		Tuple[str]:
		if not os.path.isdir(UpperCamelCase_		):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
			return
		snake_case__	       =   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_		) and os.path.isfile(self.vocab_file		):
			copyfile(self.vocab_file      ,       UpperCamelCase_		)
		elif not os.path.isfile(self.vocab_file		):
			with open(UpperCamelCase_      ,       'wb'		) as fi:
				snake_case__	       =   self.sp_model.serialized_model_proto()
				fi.write(UpperCamelCase_		)

		return (out_vocab_file,)










'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   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() = }""")



