
"""simple docstring"""




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Any   							=			[
					    'Constraint',
					    'ConstraintListState',
					    'DisjunctiveConstraint',
					    'PhrasalConstraint',
					]
					UpperCAmelCase__				:   Optional[int]   							=			[
					    'BeamHypotheses',
					    'BeamScorer',
					    'BeamSearchScorer',
					    'ConstrainedBeamSearchScorer',
					]
					UpperCAmelCase__				:   Any   							=			[
					    'EpsilonLogitsWarper',
					    'EtaLogitsWarper',
					    'ForcedBOSTokenLogitsProcessor',
					    'ForcedEOSTokenLogitsProcessor',
					    'HammingDiversityLogitsProcessor',
					    'InfNanRemoveLogitsProcessor',
					    'LogitsProcessor',
					    'LogitsProcessorList',
					    'LogitsWarper',
					    'MinLengthLogitsProcessor',
					    'MinNewTokensLengthLogitsProcessor',
					    'NoBadWordsLogitsProcessor',
					    'NoRepeatNGramLogitsProcessor',
					    'PrefixConstrainedLogitsProcessor',
					    'RepetitionPenaltyLogitsProcessor',
					    'SequenceBiasLogitsProcessor',
					    'EncoderRepetitionPenaltyLogitsProcessor',
					    'TemperatureLogitsWarper',
					    'TopKLogitsWarper',
					    'TopPLogitsWarper',
					    'TypicalLogitsWarper',
					    'EncoderNoRepeatNGramLogitsProcessor',
					    'ExponentialDecayLengthPenalty',
					    'LogitNormalization',
					]
					UpperCAmelCase__				:   Optional[int]   							=			[
					    'MaxNewTokensCriteria',
					    'MaxLengthCriteria',
					    'MaxTimeCriteria',
					    'StoppingCriteria',
					    'StoppingCriteriaList',
					    'validate_stopping_criteria',
					]
					UpperCAmelCase__				:   Optional[int]   							=			[
					    'GenerationMixin',
					    'top_k_top_p_filtering',
					    'GreedySearchEncoderDecoderOutput',
					    'GreedySearchDecoderOnlyOutput',
					    'SampleEncoderDecoderOutput',
					    'SampleDecoderOnlyOutput',
					    'BeamSearchEncoderDecoderOutput',
					    'BeamSearchDecoderOnlyOutput',
					    'BeamSampleEncoderDecoderOutput',
					    'BeamSampleDecoderOnlyOutput',
					    'ContrastiveSearchEncoderDecoderOutput',
					    'ContrastiveSearchDecoderOnlyOutput',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   List[Any]   							=			[
					    'TFForcedBOSTokenLogitsProcessor',
					    'TFForcedEOSTokenLogitsProcessor',
					    'TFLogitsProcessor',
					    'TFLogitsProcessorList',
					    'TFLogitsWarper',
					    'TFMinLengthLogitsProcessor',
					    'TFNoBadWordsLogitsProcessor',
					    'TFNoRepeatNGramLogitsProcessor',
					    'TFRepetitionPenaltyLogitsProcessor',
					    'TFTemperatureLogitsWarper',
					    'TFTopKLogitsWarper',
					    'TFTopPLogitsWarper',
					    'TFForceTokensLogitsProcessor',
					    'TFSuppressTokensAtBeginLogitsProcessor',
					    'TFSuppressTokensLogitsProcessor',
					]
					UpperCAmelCase__				:   Optional[Any]   							=			[
					    'TFGenerationMixin',
					    'tf_top_k_top_p_filtering',
					    'TFGreedySearchDecoderOnlyOutput',
					    'TFGreedySearchEncoderDecoderOutput',
					    'TFSampleEncoderDecoderOutput',
					    'TFSampleDecoderOnlyOutput',
					    'TFBeamSearchEncoderDecoderOutput',
					    'TFBeamSearchDecoderOnlyOutput',
					    'TFBeamSampleEncoderDecoderOutput',
					    'TFBeamSampleDecoderOnlyOutput',
					    'TFContrastiveSearchEncoderDecoderOutput',
					    'TFContrastiveSearchDecoderOnlyOutput',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Union[str, Any]   							=			[
					    'FlaxForcedBOSTokenLogitsProcessor',
					    'FlaxForcedEOSTokenLogitsProcessor',
					    'FlaxLogitsProcessor',
					    'FlaxLogitsProcessorList',
					    'FlaxLogitsWarper',
					    'FlaxMinLengthLogitsProcessor',
					    'FlaxTemperatureLogitsWarper',
					    'FlaxTopKLogitsWarper',
					    'FlaxTopPLogitsWarper',
					]
					UpperCAmelCase__				:   Tuple   							=			[
					    'FlaxGenerationMixin',
					    'FlaxGreedySearchOutput',
					    'FlaxSampleOutput',
					    'FlaxBeamSearchOutput',
					]

if TYPE_CHECKING:
					from .configuration_utils import GenerationConfig
					from .streamers import TextIteratorStreamer, TextStreamer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
										from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
										from .logits_process import (
										    EncoderNoRepeatNGramLogitsProcessor,
										    EncoderRepetitionPenaltyLogitsProcessor,
										    EpsilonLogitsWarper,
										    EtaLogitsWarper,
										    ExponentialDecayLengthPenalty,
										    ForcedBOSTokenLogitsProcessor,
										    ForcedEOSTokenLogitsProcessor,
										    HammingDiversityLogitsProcessor,
										    InfNanRemoveLogitsProcessor,
										    LogitNormalization,
										    LogitsProcessor,
										    LogitsProcessorList,
										    LogitsWarper,
										    MinLengthLogitsProcessor,
										    MinNewTokensLengthLogitsProcessor,
										    NoBadWordsLogitsProcessor,
										    NoRepeatNGramLogitsProcessor,
										    PrefixConstrainedLogitsProcessor,
										    RepetitionPenaltyLogitsProcessor,
										    SequenceBiasLogitsProcessor,
										    TemperatureLogitsWarper,
										    TopKLogitsWarper,
										    TopPLogitsWarper,
										    TypicalLogitsWarper,
										)
										from .stopping_criteria import (
										    MaxLengthCriteria,
										    MaxNewTokensCriteria,
										    MaxTimeCriteria,
										    StoppingCriteria,
										    StoppingCriteriaList,
										    validate_stopping_criteria,
										)
										from .utils import (
										    BeamSampleDecoderOnlyOutput,
										    BeamSampleEncoderDecoderOutput,
										    BeamSearchDecoderOnlyOutput,
										    BeamSearchEncoderDecoderOutput,
										    ContrastiveSearchDecoderOnlyOutput,
										    ContrastiveSearchEncoderDecoderOutput,
										    GenerationMixin,
										    GreedySearchDecoderOnlyOutput,
										    GreedySearchEncoderDecoderOutput,
										    SampleDecoderOnlyOutput,
										    SampleEncoderDecoderOutput,
										    top_k_top_p_filtering,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tf_logits_process import (
										    TFForcedBOSTokenLogitsProcessor,
										    TFForcedEOSTokenLogitsProcessor,
										    TFForceTokensLogitsProcessor,
										    TFLogitsProcessor,
										    TFLogitsProcessorList,
										    TFLogitsWarper,
										    TFMinLengthLogitsProcessor,
										    TFNoBadWordsLogitsProcessor,
										    TFNoRepeatNGramLogitsProcessor,
										    TFRepetitionPenaltyLogitsProcessor,
										    TFSuppressTokensAtBeginLogitsProcessor,
										    TFSuppressTokensLogitsProcessor,
										    TFTemperatureLogitsWarper,
										    TFTopKLogitsWarper,
										    TFTopPLogitsWarper,
										)
										from .tf_utils import (
										    TFBeamSampleDecoderOnlyOutput,
										    TFBeamSampleEncoderDecoderOutput,
										    TFBeamSearchDecoderOnlyOutput,
										    TFBeamSearchEncoderDecoderOutput,
										    TFContrastiveSearchDecoderOnlyOutput,
										    TFContrastiveSearchEncoderDecoderOutput,
										    TFGenerationMixin,
										    TFGreedySearchDecoderOnlyOutput,
										    TFGreedySearchEncoderDecoderOutput,
										    TFSampleDecoderOnlyOutput,
										    TFSampleEncoderDecoderOutput,
										    tf_top_k_top_p_filtering,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .flax_logits_process import (
										    FlaxForcedBOSTokenLogitsProcessor,
										    FlaxForcedEOSTokenLogitsProcessor,
										    FlaxLogitsProcessor,
										    FlaxLogitsProcessorList,
										    FlaxLogitsWarper,
										    FlaxMinLengthLogitsProcessor,
										    FlaxTemperatureLogitsWarper,
										    FlaxTopKLogitsWarper,
										    FlaxTopPLogitsWarper,
										)
										from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
					import sys

					UpperCAmelCase__				:   Any   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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


UpperCAmelCase__				:   Optional[int]   							=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCAmelCase__				:   Optional[Any]   							=			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

UpperCAmelCase__				:   Dict   							=			{
    'RUCAIBox/mvp': 1_0_2_4,
}

@lru_cache()
def lowercase_  (       ):
							SCREAMING_SNAKE_CASE__						:	Dict									=       (
							    list(range(ord("""!"""						)       ,ord("""~"""						) + 1						)						) + list(range(ord("""¡"""						)       ,ord("""¬"""						) + 1						)						) + list(range(ord("""®"""						)       ,ord("""ÿ"""						) + 1						)						)
							)
							SCREAMING_SNAKE_CASE__						:	str									=       bs[:]
							SCREAMING_SNAKE_CASE__						:	Any									=       0
							for b in range(2**8						):
														if b not in bs:
																					bs.append(_snake_case						)
																					cs.append(2**8 + n						)
																					n += 1
							SCREAMING_SNAKE_CASE__						:	List[str]									=       [chr(_snake_case						) for n in cs]
							return dict(zip(_snake_case       ,_snake_case						)						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Tuple									=       set()
							SCREAMING_SNAKE_CASE__						:	Any									=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)						)
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       char
							return pairs



class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						int 	=       VOCAB_FILES_NAMES
				__UpperCamelCase				:						str 	=       PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase				:						str 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase				:						List[Any] 	=       ['''input_ids''', '''attention_mask''']



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__="replace"       ,	SCREAMING_SNAKE_CASE__="<s>"       ,	SCREAMING_SNAKE_CASE__="</s>"       ,	SCREAMING_SNAKE_CASE__="</s>"       ,	SCREAMING_SNAKE_CASE__="<s>"       ,	SCREAMING_SNAKE_CASE__="<unk>"       ,	SCREAMING_SNAKE_CASE__="<pad>"       ,	SCREAMING_SNAKE_CASE__="<mask>"       ,	SCREAMING_SNAKE_CASE__=False       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else bos_token
											SCREAMING_SNAKE_CASE__						:	str									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else eos_token
											SCREAMING_SNAKE_CASE__						:	List[str]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else sep_token
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else cls_token
											SCREAMING_SNAKE_CASE__						:	Tuple									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else unk_token
											SCREAMING_SNAKE_CASE__						:	Any									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											SCREAMING_SNAKE_CASE__						:	List[str]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else mask_token

											super().__init__(
											    errors=SCREAMING_SNAKE_CASE__       ,	bos_token=SCREAMING_SNAKE_CASE__       ,	eos_token=SCREAMING_SNAKE_CASE__       ,	unk_token=SCREAMING_SNAKE_CASE__       ,	sep_token=SCREAMING_SNAKE_CASE__       ,	cls_token=SCREAMING_SNAKE_CASE__       ,	pad_token=SCREAMING_SNAKE_CASE__       ,	mask_token=SCREAMING_SNAKE_CASE__       ,	add_prefix_space=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)
											with open(SCREAMING_SNAKE_CASE__       ,	encoding="""utf-8"""   ) as vocab_handle:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       json.load(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {v: k for k, v in self.encoder.items()}
											SCREAMING_SNAKE_CASE__						:	Any									=       errors  # how to handle errors in decoding
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       bytes_to_unicode()
											SCREAMING_SNAKE_CASE__						:	Tuple									=       {v: k for k, v in self.byte_encoder.items()}
											with open(SCREAMING_SNAKE_CASE__       ,	encoding="""utf-8"""   ) as merges_handle:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       merges_handle.read().split("""\n"""   )[1:-1]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [tuple(merge.split()   ) for merge in bpe_merges]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       dict(zip(SCREAMING_SNAKE_CASE__       ,	range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {}
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											return len(self.encoder   )



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



											"""simple docstring"""




											return dict(self.encoder       ,	**self.added_tokens_encoder   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											if token in self.cache:
																		return self.cache[token]
											SCREAMING_SNAKE_CASE__						:	Dict									=       tuple(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	str									=       get_pairs(SCREAMING_SNAKE_CASE__   )

											if not pairs:
																		return token

											while True:
																		SCREAMING_SNAKE_CASE__						:	Any									=       min(SCREAMING_SNAKE_CASE__       ,	key=lambda SCREAMING_SNAKE_CASE__   : self.bpe_ranks.get(SCREAMING_SNAKE_CASE__       ,	float("""inf"""   )   )   )
																		if bigram not in self.bpe_ranks:
																									break
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	int									=       bigram
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
																		SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       0
																		while i < len(SCREAMING_SNAKE_CASE__   ):
																									try:
																																SCREAMING_SNAKE_CASE__						:	str									=       word.index(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																									except ValueError:
																																new_word.extend(word[i:]   )
																																break
																									else:
																																new_word.extend(word[i:j]   )
																																SCREAMING_SNAKE_CASE__						:	Tuple									=       j

																									if word[i] == first and i < len(SCREAMING_SNAKE_CASE__   ) - 1 and word[i + 1] == second:
																																new_word.append(first + second   )
																																i += 2
																									else:
																																new_word.append(word[i]   )
																																i += 1
																		SCREAMING_SNAKE_CASE__						:	int									=       tuple(SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       new_word
																		if len(SCREAMING_SNAKE_CASE__   ) == 1:
																									break
																		else:
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       get_pairs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       """ """.join(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       word
											return word



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
											for token in re.findall(self.pat       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Dict									=       """""".join(
																		    self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__   ).split(""" """   )   )
											return bpe_tokens



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




											return self.encoder.get(SCREAMING_SNAKE_CASE__       ,	self.encoder.get(self.unk_token   )   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Union[str, Any]:



											"""simple docstring"""




											return self.decoder.get(SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       """""".join(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""       ,	errors=self.errors   )
											return text



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		Tuple[str]:



											"""simple docstring"""




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

											with open(SCREAMING_SNAKE_CASE__       ,	"""w"""       ,	encoding="""utf-8"""   ) as f:
																		f.write(json.dumps(self.encoder       ,	indent=2       ,	sort_keys=SCREAMING_SNAKE_CASE__       ,	ensure_ascii=SCREAMING_SNAKE_CASE__   ) + """\n"""   )

											SCREAMING_SNAKE_CASE__						:	Dict									=       0
											with open(SCREAMING_SNAKE_CASE__       ,	"""w"""       ,	encoding="""utf-8"""   ) as writer:
																		writer.write("""#version: 0.2\n"""   )
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,	key=lambda SCREAMING_SNAKE_CASE__   : kv[1]   ):
																									if index != token_index:
																																logger.warning(
																																    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																    """ Please check that the tokenizer is not corrupted!"""   )
																																SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       token_index
																									writer.write(""" """.join(SCREAMING_SNAKE_CASE__   ) + """\n"""   )
																									index += 1

											return vocab_file, merge_file



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [self.cls_token_id]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = False   ) ->		List[int]:



											"""simple docstring"""




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

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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [self.cls_token_id]

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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=False       ,	**SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Any									=       kwargs.pop("""add_prefix_space"""       ,	self.add_prefix_space   )
											if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__   ) > 0 and not text[0].isspace()):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       """ """ + text
											return (text, kwargs)




