



"""simple docstring"""







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A    =			logging.get_logger(__name__)


A    =			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A    =			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A    =			{'facebook/blenderbot_small-90M': 5_1_2}

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Optional[Any]:
							A         =					set()
							A         =					word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)			)
														A         =					char

							A         =					set(lowerCamelCase__			)
							return pairs






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Any 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Dict 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	Union[str, Any] 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	List[str] 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    Dict      ,							snake_case  :    Optional[Any]="__start__"      ,							snake_case  :    Optional[int]="__end__"      ,							snake_case  :    Tuple="__unk__"      ,							snake_case  :    List[Any]="__null__"      ,							**snake_case  :    str      ,							)			->						Optional[Any]:






									'''simple docstring'''

									super().__init__(unk_token=snake_case      ,							bos_token=snake_case      ,							eos_token=snake_case      ,							pad_token=snake_case      ,							**snake_case				)

									with open(snake_case      ,							encoding='utf-8'				) as vocab_handle:
																A         =					json.load(snake_case				)
									A         =					{v: k for k, v in self.encoder.items()}
									with open(snake_case      ,							encoding='utf-8'				) as merges_handle:
																A         =					merges_handle.read().split('\n'				)[1:-1]
									A         =					[tuple(merge.split()				) for merge in merges]
									A         =					dict(zip(snake_case      ,							range(len(snake_case				)				)				)				)
									A         =					{}



		@property
		def 					A_		( self  :    Tuple				)			->						int:






									'''simple docstring'''

									return len(self.encoder				)



		def 					A_		( self  :    Tuple				)			->						Dict:






									'''simple docstring'''

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



		def 					A_		( self  :    str      ,							snake_case  :    str				)			->						str:






									'''simple docstring'''

									if token in self.cache:
																return self.cache[token]
									A         =					re.sub('([.,!?()])'      ,							r' \1'      ,							snake_case				)
									A         =					re.sub('(\')'      ,							r' \1 '      ,							snake_case				)
									A         =					re.sub(r'\s{2,}'      ,							' '      ,							snake_case				)
									if "\n" in token:
																A         =					token.replace('\n'      ,							' __newln__'				)

									A         =					token.split(' '				)
									A         =					[]
									for token in tokens:
																if not len(snake_case				):
																							continue

																A         =					token.lower()
																A         =					tuple(snake_case				)
																A         =					tuple(list(word[:-1]				) + [word[-1] + '</w>']				)
																A         =					get_pairs(snake_case				)

																if not pairs:
																							words.append(snake_case				)
																							continue

																while True:
																							A         =					min(snake_case      ,							key=lambda snake_case				: self.bpe_ranks.get(snake_case      ,							float('inf'				)				)				)
																							if bigram not in self.bpe_ranks:
																														break
																							A      ,  A         =					bigram
																							A         =					[]
																							A         =					0

																							while i < len(snake_case				):
																														try:
																																					A         =					word.index(snake_case      ,							snake_case				)
																																					new_word.extend(word[i:j]				)
																																					A         =					j
																														except ValueError:
																																					new_word.extend(word[i:]				)
																																					break

																														if word[i] == first and i < len(snake_case				) - 1 and word[i + 1] == second:
																																					new_word.append(first + second				)
																																					i += 2
																														else:
																																					new_word.append(word[i]				)
																																					i += 1
																							A         =					tuple(snake_case				)
																							A         =					new_word
																							if len(snake_case				) == 1:
																														break
																							else:
																														A         =					get_pairs(snake_case				)
																A         =					'@@ '.join(snake_case				)
																A         =					word[:-4]

																A         =					word
																words.append(snake_case				)
									return " ".join(snake_case				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    str				)			->						List[str]:






									'''simple docstring'''

									A         =					[]

									A         =					re.findall(r'\S+\n?'      ,							snake_case				)

									for token in words:
																split_tokens.extend(list(self.bpe(snake_case				).split(' '				)				)				)
									return split_tokens



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    str				)			->						int:






									'''simple docstring'''

									A         =					token.lower()
									return self.encoder.get(snake_case      ,							self.encoder.get(self.unk_token				)				)



		def 					A_		( self  :    Any      ,							snake_case  :    int				)			->						str:






									'''simple docstring'''

									return self.decoder.get(snake_case      ,							self.unk_token				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    List[str]				)			->						str:






									'''simple docstring'''

									A         =					' '.join(snake_case				).replace('@@ '      ,							''				).strip()
									return out_string



		def 					A_		( self  :    List[str]      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

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

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

									A         =					0
									with open(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 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!'				)
																														A         =					token_index
																							writer.write(' '.join(snake_case				) + '\n'				)
																							index += 1

									return vocab_file, merge_file








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			['PLBartTokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



