






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
				from ...tokenization_utils_base import TextInput
from ...utils import logging


A        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spiece.model'}

A        =			{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A        =			{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

A        =			'▁'


class 							__a							(  __A						):





						'''simple docstring'''



						UpperCAmelCase__      :				int				    =						VOCAB_FILES_NAMES
						UpperCAmelCase__      :				str				    =						PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase__      :				List[str]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase__      :				List[Any]				    =						["""input_ids""", """attention_mask"""]


						def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__=100 ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__=True ,    **UpperCamelCase__ ,    ):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
														SCREAMING_SNAKE_CASE_   :	int													=   [F'''<extra_id_{i}>''' for i in range(UpperCamelCase__       )]
										elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra_id special tokens
														SCREAMING_SNAKE_CASE_   :	int													=   len(set(filter(lambda UpperCamelCase__       : bool('extra_id' in str(UpperCamelCase__       )       ) ,    UpperCamelCase__       )       )       )
														if extra_tokens != extra_ids:
																		raise ValueError(
																		    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																		    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																		    ' tokens'       )
										if legacy:
														logger.warning_once(
														    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
														    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'       )

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   legacy
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    extra_ids=UpperCamelCase__ ,    additional_special_tokens=UpperCamelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    legacy=UpperCamelCase__ ,    **UpperCamelCase__ ,    )

										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   vocab_file
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   extra_ids

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(UpperCamelCase__       )


						@staticmethod
						def   __snake_case			(						UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														SCREAMING_SNAKE_CASE_   :	Any													=   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																		return init_max_model_length
														elif init_max_model_length is None:
																		warnings.warn(
																		    'This tokenizer was incorrectly instantiated with a model max length of'
																		    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
																		    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																		    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																		    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
																		    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
																		    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
																		    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																		    ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,    UpperCamelCase__ ,    )

										return max_model_length


						@property
						def   __snake_case			(						self       ):
										return self.sp_model.get_piece_size() + self._extra_ids


						def   __snake_case			(						self       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {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__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

										# normal case: some special tokens
										if token_ids_a is None:
														return ([0] * len(UpperCamelCase__       )) + [1]
										return ([0] * len(UpperCamelCase__       )) + [1] + ([0] * len(UpperCamelCase__       )) + [1]


						def   __snake_case			(						self       ):
										return list(
										    set(filter(lambda UpperCamelCase__       : bool(re.search(R'<extra_id_\d+>' ,    UpperCamelCase__       )       ) is not None ,    self.additional_special_tokens       )       )       )


						def   __snake_case			(						self       ):
										return [self._convert_token_to_id(UpperCamelCase__       ) for token in self.get_sentinel_tokens()]


						def   __snake_case			(						self ,    UpperCamelCase__       ):
										if len(UpperCamelCase__       ) > 0 and token_ids[-1] == self.eos_token_id:
														warnings.warn(
														    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
														    ' eos tokens being added.'       )
														return token_ids
										else:
														return token_ids + [self.eos_token_id]


						def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   [self.eos_token_id]

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


						def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
										SCREAMING_SNAKE_CASE_   :	Dict													=   self._add_eos_if_not_present(UpperCamelCase__       )
										if token_ids_a is None:
														return token_ids_a
										else:
														SCREAMING_SNAKE_CASE_   :	Tuple													=   self._add_eos_if_not_present(UpperCamelCase__       )
														return token_ids_a + token_ids_a


						def __getstate__(						self       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   self.__dict__.copy()
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   None
										return state


						def __setstate__(						self ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   d

										# for backward compatibility
										if not hasattr(self ,    'sp_model_kwargs'       ):
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {}

										SCREAMING_SNAKE_CASE_   :	Tuple													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
										self.sp_model.Load(self.vocab_file       )


						def   __snake_case			(						self ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
										# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
										# the beginning of the text
										if not self.legacy:
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   SPIECE_UNDERLINE + text.replace(UpperCamelCase__ ,    ' '       )
										return super().tokenize(UpperCamelCase__ ,    **UpperCamelCase__       )


						def   __snake_case			(						self ,    UpperCamelCase__ ,    **UpperCamelCase__       ):
										if not self.legacy:
														SCREAMING_SNAKE_CASE_   :	int													=   text.startswith(UpperCamelCase__       )
														if is_first:
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   text[1:]

										SCREAMING_SNAKE_CASE_   :	str													=   self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )

										if not self.legacy and not is_first and not text.startswith(' '       ) and tokens[0].startswith(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Tuple													=   ([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
										return tokens


						def   __snake_case			(						self ,    UpperCamelCase__       ):
										if token.startswith('<extra_id_'       ):
														SCREAMING_SNAKE_CASE_   :	str													=   re.match(R'<extra_id_(\d+)>' ,    UpperCamelCase__       )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   int(match.group(1       )       )
														return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(UpperCamelCase__       )


						def   __snake_case			(						self ,    UpperCamelCase__       ):
										if index < self.sp_model.get_piece_size():
														SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.IdToPiece(UpperCamelCase__       )
										else:
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   F'''<extra_id_{self.vocab_size - 1 - index}>'''
										return token


						def   __snake_case			(						self ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   []
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ''
										SCREAMING_SNAKE_CASE_   :	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(UpperCamelCase__       ) + token
																		SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True
																		SCREAMING_SNAKE_CASE_   :	List[str]													=   []
														else:
																		current_sub_tokens.append(UpperCamelCase__       )
																		SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   False
										out_string += self.sp_model.decode(UpperCamelCase__       )
										return out_string.strip()






						def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
										if not os.path.isdir(UpperCamelCase__       ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
														return
										SCREAMING_SNAKE_CASE_   :	List[str]													=   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:
																		SCREAMING_SNAKE_CASE_   :	Any													=   self.sp_model.serialized_model_proto()
																		fi.write(UpperCamelCase__       )

										return (out_vocab_file,)







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()