
'''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

__lowerCAmelCase												=	logging.get_logger(__name__)


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

__lowerCAmelCase												=	{
    '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'
    },
}

__lowerCAmelCase												=	{'facebook/blenderbot-3B': 128}


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						VOCAB_FILES_NAMES
							lowerCAmelCase_ 						=						PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_ 						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_ 						=						["input_ids", "attention_mask"]
							lowerCAmelCase_ 						=						BlenderbotTokenizer

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

														_snake_case       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""  ,  UpperCAmelCase  ) != add_prefix_space:
																					_snake_case       =  getattr(UpperCAmelCase  ,  pre_tok_state.pop("""type"""  )  )
																					_snake_case       =  add_prefix_space
																					_snake_case       =  pre_tok_class(**UpperCAmelCase  )

														_snake_case       =  add_prefix_space

														_snake_case       =  """post_processor"""
														_snake_case       =  getattr(self.backend_tokenizer  ,  UpperCAmelCase  ,  UpperCAmelCase  )
														if tokenizer_component_instance:
																					_snake_case       =  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:
																												_snake_case       =  tuple(state["""sep"""]  )
																					if "cls" in state:
																												_snake_case       =  tuple(state["""cls"""]  )

																					_snake_case       =  False

																					if state.get("""add_prefix_space"""  ,  UpperCAmelCase  ) != add_prefix_space:
																												_snake_case       =  add_prefix_space
																												_snake_case       =  True

																					if state.get("""trim_offsets"""  ,  UpperCAmelCase  ) != trim_offsets:
																												_snake_case       =  trim_offsets
																												_snake_case       =  True

																					if changes_to_apply:
																												_snake_case       =  getattr(UpperCAmelCase  ,  state.pop("""type"""  )  )
																												_snake_case       =  component_class(**UpperCAmelCase  )
																												setattr(self.backend_tokenizer  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							@property
							# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 	lowercase							(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 	lowercase							(self  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  AddedToken(UpperCAmelCase  ,  lstrip=UpperCAmelCase  ,  rstrip=UpperCAmelCase  ) if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ) else value
														_snake_case       =  value

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       BatchEncoding:
														_snake_case       =  kwargs.get("""is_split_into_words"""  ,  UpperCAmelCase  )
														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(*UpperCAmelCase  ,  **UpperCAmelCase  )

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       BatchEncoding:
														_snake_case       =  kwargs.get("""is_split_into_words"""  ,  UpperCAmelCase  )

														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(*UpperCAmelCase  ,  **UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Tuple[str]:
														_snake_case       =  self._tokenizer.model.save(UpperCAmelCase  ,  name=UpperCAmelCase  )
														return tuple(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       List[int]:
														_snake_case       =  [self.sep_token_id]
														_snake_case       =  [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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       Dict:
														return token_ids_a + [self.eos_token_id]





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       List[int]:
														_snake_case       =  []
														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(UpperCAmelCase  )

														_snake_case       =  """  """.join(UpperCAmelCase  )
														_snake_case       =  self.encode(UpperCAmelCase  )
														if len(UpperCAmelCase  ) > self.model_max_length:
																					_snake_case       =  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'''
import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"masked_bert"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase="topK"  ,  UpperCAmelCase="constant"  ,  UpperCAmelCase=0.0  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  pruning_method
														_snake_case       =  mask_init
														_snake_case       =  mask_scale