


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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_lowercase       :      Union[str, Any]	=	logging.get_logger(__name__)

_lowercase       :      Union[str, Any]	=	{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase       :      List[Any]	=	{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowercase       :      Union[str, Any]	=	{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES
				lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase 		= ['input_ids', 'attention_mask']
				lowerCAmelCase 		= GPTaTokenizer

				def __init__(						self				,   a__=None				,   a__=None				,   a__=None				,   a__="<|endoftext|>"				,   a__="<|endoftext|>"				,   a__="<|endoftext|>"				,   a__=False				,   **a__				,   )	->				List[str]:
					super().__init__(
					    a__				,   a__				,   tokenizer_file=a__				,   unk_token=a__				,   bos_token=a__				,   eos_token=a__				,   add_prefix_space=a__				,   **a__				,   )

					A								=				kwargs.pop("""add_bos_token"""				,   a__   )

					A								=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get("""add_prefix_space"""				,   a__   ) != add_prefix_space:
						A								=				getattr(a__				,   pre_tok_state.pop("""type"""   )   )
						A								=				add_prefix_space
						A								=				pre_tok_class(**a__   )

					A								=				add_prefix_space

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				BatchEncoding:
					A								=				kwargs.get("""is_split_into_words"""				,   a__   )
					assert self.add_prefix_space or not is_split_into_words, (
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    "to use it with pretokenized inputs."
					)

					return super()._batch_encode_plus(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   *a__				,   **a__   )	->				BatchEncoding:
					A								=				kwargs.get("""is_split_into_words"""				,   a__   )

					assert self.add_prefix_space or not is_split_into_words, (
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    "to use it with pretokenized inputs."
					)

					return super()._encode_plus(*a__				,   **a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					A								=				self._tokenizer.model.save(a__				,   name=a__   )
					return tuple(a__   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[int]:
					A								=				[]
					for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(a__				,   add_special_tokens=a__   ) + [self.eos_token_id]   )

					if len(a__   ) > self.model_max_length:
						A								=				input_ids[-self.model_max_length :]
					return input_ids







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


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



