




"""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

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-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'''
    },
}

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





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      VOCAB_FILES_NAMES
 UpperCAmelCase       :	List[str]     =      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase       :	Optional[Any]     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase       :	List[str]     =      ['''input_ids''', '''attention_mask''']
 UpperCAmelCase       :	Any     =      BlenderbotTokenizer

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			Optional[Any]=None       ,				_UpperCAmelCase			:			Dict=None       ,				_UpperCAmelCase			:			Optional[int]="replace"       ,				_UpperCAmelCase			:			int="<s>"       ,				_UpperCAmelCase			:			str="</s>"       ,				_UpperCAmelCase			:			List[Any]="</s>"       ,				_UpperCAmelCase			:			int="<s>"       ,				_UpperCAmelCase			:			int="<unk>"       ,				_UpperCAmelCase			:			List[str]="<pad>"       ,				_UpperCAmelCase			:			Optional[int]="<mask>"       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			str=True       ,				**_UpperCAmelCase			:			str       ,				):
       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       ,				)

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

       _A     =  add_prefix_space

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

             _A     =  False

             if state.get('add_prefix_space'       ,				_UpperCAmelCase						) != add_prefix_space:
                   _A     =  add_prefix_space
                   _A     =  True

             if state.get('trim_offsets'       ,				_UpperCAmelCase						) != trim_offsets:
                   _A     =  trim_offsets
                   _A     =  True

             if changes_to_apply:
                   _A     =  getattr(_UpperCAmelCase       ,				state.pop('type'						)						)
                   _A     =  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      lowerCAmelCase_				(					self			:			Optional[Any]						):
       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      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  AddedToken(_UpperCAmelCase       ,				lstrip=_UpperCAmelCase       ,				rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase       ,				_UpperCAmelCase						) else value
       _A     =  value

 def      lowerCAmelCase_				(					self			:			int       ,				*_UpperCAmelCase			:			int       ,				**_UpperCAmelCase			:			Optional[int]						):
       _A     =  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      lowerCAmelCase_				(					self			:			List[Any]       ,				*_UpperCAmelCase			:			Optional[int]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[str] = None						):
       _A     =  self._tokenizer.model.save(_UpperCAmelCase       ,				name=_UpperCAmelCase						)
       return tuple(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.sep_token_id]
       _A     =  [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      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       return token_ids_a + [self.eos_token_id]



 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			"Conversation"						):
       _A     =  []
       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						)

       _A     =  '  '.join(_UpperCAmelCase						)
       _A     =  self.encode(_UpperCAmelCase						)
       if len(_UpperCAmelCase						) > self.model_max_length:
             _A     =  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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      '''xmod'''

 def __init__(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]=30_522       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Dict=3_072       ,				_UpperCAmelCase			:			Union[str, Any]="gelu"       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[int]=0.02       ,				_UpperCAmelCase			:			Any=1E-1_2       ,				_UpperCAmelCase			:			Tuple=1       ,				_UpperCAmelCase			:			int=0       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			List[str]="absolute"       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			List[Any]=False       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=("en_XX",)       ,				_UpperCAmelCase			:			List[str]=None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout
       _A     =  pre_norm
       _A     =  adapter_reduction_factor
       _A     =  adapter_layer_norm
       _A     =  adapter_reuse_layer_norm
       _A     =  ln_before_adapter
       _A     =  list(_UpperCAmelCase						)
       _A     =  default_language





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)

