'''simple docstring'''

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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

lowercase_     		= logging.get_logger(__name__)


lowercase_     		= {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_     		= {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowercase_     		= {"facebook/blenderbot-3B": 128}
class   SCREAMING_SNAKE_CASE			(						_UpperCAmelCase):







		"""simple docstring"""
		lowercase   :      Optional[Any]       =							VOCAB_FILES_NAMES
		lowercase   :      int       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Tuple       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      int       =							['''input_ids''', '''attention_mask''']
		lowercase   :      str       =							BlenderbotTokenizer






		def __init__(   self		,						__A=None		,						__A=None		,						__A=None		,						__A="replace"		,						__A="<s>"		,						__A="</s>"		,						__A="</s>"		,						__A="<s>"		,						__A="<unk>"		,						__A="<pad>"		,						__A="<mask>"		,						__A=False		,						__A=True		,						**__A		,						) ->     int:
								super().__init__(
								    A_		,						A_		,						tokenizer_file=A_		,						errors=A_		,						bos_token=A_		,						eos_token=A_		,						sep_token=A_		,						cls_token=A_		,						unk_token=A_		,						pad_token=A_		,						mask_token=A_		,						add_prefix_space=A_		,						trim_offsets=A_		,						**A_		,						)

								_lowerCAmelCase										=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
								if pre_tok_state.get('add_prefix_space'		,						A_				) != add_prefix_space:
														_lowerCAmelCase										=getattr(A_		,						pre_tok_state.pop('type'				)				)
														_lowerCAmelCase										=add_prefix_space
														_lowerCAmelCase										=pre_tok_class(**A_				)

								_lowerCAmelCase										=add_prefix_space

								_lowerCAmelCase										='post_processor'
								_lowerCAmelCase										=getattr(self.backend_tokenizer		,						A_		,						A_				)
								if tokenizer_component_instance:
														_lowerCAmelCase										=json.loads(tokenizer_component_instance.__getstate__()				)

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																				_lowerCAmelCase										=tuple(state['sep']				)
														if "cls" in state:
																				_lowerCAmelCase										=tuple(state['cls']				)

														_lowerCAmelCase										=False

														if state.get('add_prefix_space'		,						A_				) != add_prefix_space:
																				_lowerCAmelCase										=add_prefix_space
																				_lowerCAmelCase										=True

														if state.get('trim_offsets'		,						A_				) != trim_offsets:
																				_lowerCAmelCase										=trim_offsets
																				_lowerCAmelCase										=True

														if changes_to_apply:
																				_lowerCAmelCase										=getattr(A_		,						state.pop('type'				)				)
																				_lowerCAmelCase										=component_class(**A_				)
																				setattr(self.backend_tokenizer		,						A_		,						A_				)






		@property
		# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
		def 		UpperCamelCase__       (   self				) ->     str:
								if self._mask_token is None:
														if self.verbose:
																				logger.error('Using mask_token, but it is not set yet.'				)
														return None
								return str(self._mask_token				)






		@mask_token.setter
		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=AddedToken(A_		,						lstrip=A_		,						rstrip=A_				) if isinstance(A_		,						A_				) else value
								_lowerCAmelCase										=value






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     BatchEncoding:
								_lowerCAmelCase										=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:
								_lowerCAmelCase										=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]:
								_lowerCAmelCase										=self._tokenizer.model.save(A_		,						name=A_				)
								return tuple(A_				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     List[int]:
								_lowerCAmelCase										=[self.sep_token_id]
								_lowerCAmelCase										=[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 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     Any:
								return token_ids_a + [self.eos_token_id]






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[int]:
								_lowerCAmelCase										=[]
								for is_user, text in conversation.iter_texts():
														if is_user:
																				# We need to space prefix as it's being done within blenderbot
																				inputs.append(' ' + text				)
														else:
																				# Generated responses should contain them already.
																				inputs.append(A_				)

								_lowerCAmelCase										='  '.join(A_				)
								_lowerCAmelCase										=self.encode(A_				)
								if len(A_				) > self.model_max_length:
														_lowerCAmelCase										=input_ids[-self.model_max_length :]
														logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''				)
								return input_ids






'''simple docstring'''

from __future__ import annotations



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





