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


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

_lowerCamelCase   :				Union[str, Any]     			=						{'''vocab_file''': '''spiece.model'''}

_lowerCamelCase   :				int     			=						{
    '''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
_lowerCamelCase   :				Any     			=						{
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}

_lowerCamelCase   :				List[str]     			=						'''▁'''


class   lowercase      (       __lowerCamelCase       ):
							lowercase__  :    int									=							VOCAB_FILES_NAMES
							lowercase__  :    Union[str, Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Union[str, Any]									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Tuple		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[Any]="</s>"		,							_UpperCamelCase				:   Optional[Any]="<unk>"		,							_UpperCamelCase				:   Optional[Any]="<pad>"		,							_UpperCamelCase				:   str=100		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[Dict[str, Any]] = None		,							_UpperCamelCase				:   Optional[Any]=True		,							**_UpperCamelCase				:   List[Any]		,							)   ->			Any:



												'''simple docstring'''

												if extra_ids > 0 and additional_special_tokens is None:
																	SCREAMING_SNAKE_CASE           =    [F"<extra_id_{i}>" for i in range(__lowercase )]
												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           =    len(set(filter(lambda _UpperCamelCase : bool("extra_id" in str(__lowercase ) )		,							__lowercase ) ) )
																	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           =    legacy
												SCREAMING_SNAKE_CASE           =    {} if sp_model_kwargs is None else sp_model_kwargs

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

												SCREAMING_SNAKE_CASE           =    vocab_file
												SCREAMING_SNAKE_CASE           =    extra_ids

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(__lowercase )




							@staticmethod
							def 		__snake_case(  _UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Any )   ->			Any:



												'''simple docstring'''

												if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																	SCREAMING_SNAKE_CASE           =    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."		,							__lowercase		,							)

												return max_model_length




							@property
							def 		__snake_case(  self				:   int )   ->			Optional[int]:



												'''simple docstring'''

												return self.sp_model.get_piece_size() + self._extra_ids




							def 		__snake_case(  self				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {self.convert_ids_to_tokens(__lowercase ): i for i in range(self.vocab_size )}
												vocab.update(self.added_tokens_encoder )
												return vocab




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			int:



												'''simple docstring'''

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

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




							def 		__snake_case(  self				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												return list(
												    set(filter(lambda _UpperCamelCase : bool(re.search(R"<extra_id_\d+>"		,							__lowercase ) ) is not None		,							self.additional_special_tokens ) ) )




							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

												return [self._convert_token_to_id(__lowercase ) for token in self.get_sentinel_tokens()]




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   List[int] )   ->			Optional[int]:



												'''simple docstring'''

												if len(__lowercase ) > 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				:   Tuple		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [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				:   List[str]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._add_eos_if_not_present(__lowercase )
												if token_ids_a is None:
																	return token_ids_a
												else:
																	SCREAMING_SNAKE_CASE           =    self._add_eos_if_not_present(__lowercase )
																	return token_ids_a + token_ids_a




							def __getstate__(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.__dict__.copy()
												SCREAMING_SNAKE_CASE           =    None
												return state




							def __setstate__(  self				:   str		,							_UpperCamelCase				:   Union[str, Any] )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    d

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

												SCREAMING_SNAKE_CASE           =    spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(self.vocab_file )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   "TextInput"		,							**_UpperCamelCase				:   Tuple )   ->			Optional[Any]:



												'''simple docstring'''

												if not self.legacy:
																	SCREAMING_SNAKE_CASE           =    SPIECE_UNDERLINE + text.replace(__lowercase		,							" " )
												return super().tokenize(__lowercase		,							**__lowercase )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Any )   ->			Dict:



												'''simple docstring'''

												if not self.legacy:
																	SCREAMING_SNAKE_CASE           =    text.startswith(__lowercase )
																	if is_first:
																						SCREAMING_SNAKE_CASE           =    text[1:]

												SCREAMING_SNAKE_CASE           =    self.sp_model.encode(__lowercase		,							out_type=__lowercase )

												if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(__lowercase ):
																	SCREAMING_SNAKE_CASE           =    ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
												return tokens




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[Any] )   ->			Tuple:



												'''simple docstring'''

												if token.startswith("<extra_id_" ):
																	SCREAMING_SNAKE_CASE           =    re.match(R"<extra_id_(\d+)>"		,							__lowercase )
																	SCREAMING_SNAKE_CASE           =    int(match.group(1 ) )
																	return self.vocab_size - num - 1
												return self.sp_model.piece_to_id(__lowercase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Optional[Any] )   ->			Dict:



												'''simple docstring'''

												if index < self.sp_model.get_piece_size():
																	SCREAMING_SNAKE_CASE           =    self.sp_model.IdToPiece(__lowercase )
												else:
																	SCREAMING_SNAKE_CASE           =    F"<extra_id_{self.vocab_size - 1 - index}>"
												return token




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    ''''''
												SCREAMING_SNAKE_CASE           =    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(__lowercase ) + token
																						SCREAMING_SNAKE_CASE           =    True
																						SCREAMING_SNAKE_CASE           =    []
																	else:
																						current_sub_tokens.append(__lowercase )
																						SCREAMING_SNAKE_CASE           =    False
												out_string += self.sp_model.decode(__lowercase )
												return out_string.strip()





							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple:



												'''simple docstring'''

												if not os.path.isdir(__lowercase ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
																	return
												SCREAMING_SNAKE_CASE           =    os.path.join(
												    __lowercase		,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

												return (out_vocab_file,)





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




