






from __future__ import annotations





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:

							# preprocessing the first row
							for i in range(1       ,    len(matrix[0]   )   ):
														matrix[0][i] += matrix[0][i - 1]

							# preprocessing the first column
							for i in range(1       ,    len(UpperCamelCase   )   ):
														matrix[i][0] += matrix[i - 1][0]

							# updating the path cost for current position
							for i in range(1       ,    len(UpperCamelCase   )   ):
														for j in range(1       ,    len(matrix[0]   )   ):
																					matrix[i][j] += min(matrix[i - 1][j]       ,    matrix[i][j - 1]   )

							return matrix[-1][-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()









'''simple docstring'''







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			str 						= []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  ) + [self.eos_token_id]  )

														if len(UpperCamelCase__  ) > self.model_max_length:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

