





'''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__							:							Optional[int] =logging.get_logger(__name__)


a__							:							List[str] ={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a__							:							Optional[Any] ={
    '''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__							:							List[str] ={'''facebook/blenderbot-3B''': 128}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Any					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Tuple					=["input_ids", "attention_mask"]
 SCREAMING_SNAKE_CASE_	:					Any					=BlenderbotTokenizer






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

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

        __UpperCamelCase       =  add_prefix_space

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

               __UpperCamelCase       =  False

               if state.get('add_prefix_space'							,	__A	) != add_prefix_space:
                      __UpperCamelCase       =  add_prefix_space
                      __UpperCamelCase       =  True

               if state.get('trim_offsets'							,	__A	) != trim_offsets:
                      __UpperCamelCase       =  trim_offsets
                      __UpperCamelCase       =  True

               if changes_to_apply:
                      __UpperCamelCase       =  getattr(__A							,	state.pop('type'	)	)
                      __UpperCamelCase       =  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        _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       :							Any							,	__A       :							Tuple	):
        __UpperCamelCase       =  AddedToken(__A							,	lstrip=__A							,	rstrip=__A	) if isinstance(__A							,	__A	) else value
        __UpperCamelCase       =  value






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	*__A       :							Optional[int]							,	**__A       :							Tuple	):
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Tuple							,	*__A       :							Dict							,	**__A       :							List[str]	):
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  self._tokenizer.model.save(__A							,	name=__A	)
        return tuple(__A	)






 def        _lowerCamelCase					(       self       :							int							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __UpperCamelCase       =  [self.sep_token_id]
        __UpperCamelCase       =  [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       :							Optional[int]							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        return token_ids_a + [self.eos_token_id]






 def        _lowerCamelCase					(       self       :							str							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        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	)

        __UpperCamelCase       =  '  '.join(__A	)
        __UpperCamelCase       =  self.encode(__A	)
        if len(__A	) > self.model_max_length:
               __UpperCamelCase       =  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 copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__							:							List[Any] =logging.get_logger(__name__)

a__							:							List[Any] ={
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_text_model"






 def __init__(       self       :							str							,	__A       :							List[Any]=2_5_0_0_0_2							,	__A       :							Any=1_0_2_4							,	__A       :							int=2_4							,	__A       :							Dict=1_6							,	__A       :							Optional[Any]=4_0_9_6							,	__A       :							Union[str, Any]="gelu"							,	__A       :							Dict=0.1							,	__A       :							Dict=0.1							,	__A       :							List[str]=5_1_4							,	__A       :							Optional[int]=1							,	__A       :							int=0.02							,	__A       :							Optional[Any]=0.02							,	__A       :							Optional[Any]=1e-05							,	__A       :							Dict=1							,	__A       :							List[Any]=0							,	__A       :							int=2							,	__A       :							Tuple="absolute"							,	__A       :							Optional[Any]=True							,	__A       :							Optional[int]=7_6_8							,	**__A       :							List[str]							,	):
        super().__init__(pad_token_id=__A							,	bos_token_id=__A							,	eos_token_id=__A							,	**__A	)

        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  type_vocab_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  position_embedding_type
        __UpperCamelCase       =  use_cache
        __UpperCamelCase       =  project_dim






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="altclip_vision_model"






 def __init__(       self       :							List[Any]							,	__A       :							Union[str, Any]=7_6_8							,	__A       :							Optional[int]=3_0_7_2							,	__A       :							Optional[Any]=5_1_2							,	__A       :							Tuple=1_2							,	__A       :							Union[str, Any]=1_2							,	__A       :							Optional[int]=3							,	__A       :							Dict=2_2_4							,	__A       :							Tuple=3_2							,	__A       :							str="quick_gelu"							,	__A       :							Dict=1e-5							,	__A       :							Optional[int]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							int=1.0							,	**__A       :							Optional[int]							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  initializer_factor
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  hidden_act






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[Any]							,	__A       :							Union[str, os.PathLike]							,	**__A       :							Optional[Any]	):
        cls._set_token_in_kwargs(__A	)

        __UpperCamelCase       ,						__UpperCamelCase       =  cls.get_config_dict(__A							,	**__A	)

        # get the vision config dict if we are loading from AltCLIPConfig
        if config_dict.get('model_type'	) == "altclip":
               __UpperCamelCase       =  config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls							,	'model_type'	) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

        return cls.from_dict(__A							,	**__A	)






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[str]					="altclip"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=True






 def __init__(       self       :							Any							,	__A       :							List[str]=None							,	__A       :							List[Any]=None							,	__A       :							List[str]=7_6_8							,	__A       :							List[str]=2.6592							,	**__A       :							Dict	):
        # If `_config_dict` exist, we use them for the backward compatibility.
        # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
        # of confusion!).
        __UpperCamelCase       =  kwargs.pop('text_config_dict'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('vision_config_dict'							,	__A	)

        super().__init__(**__A	)

        # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
        # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
        # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
        if text_config_dict is not None:
               if text_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `text_config_dict`.
               __UpperCamelCase       =  AltCLIPTextConfig(**__A	).to_dict()

               # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
               for key, value in _text_config_dict.items():
                      if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                             # If specified in `text_config_dict`
                             if key in text_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                                        f'''The value `text_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                                        f'''value `text_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
               text_config.update(_text_config_dict	)

        if vision_config_dict is not None:
               if vision_config is None:
                      __UpperCamelCase       =  {}

               # This is the complete result when using `vision_config_dict`.
               __UpperCamelCase       =  AltCLIPVisionConfig(**__A	).to_dict()
               # convert keys to string instead of integer
               if "id2label" in _vision_config_dict:
                      __UpperCamelCase       =  {
                          str(__A	): value for key, value in _vision_config_dict['id2label'].items()
                      }

               # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
               for key, value in _vision_config_dict.items():
                      if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                             # If specified in `vision_config_dict`
                             if key in vision_config_dict:
                                    __UpperCamelCase       =  (
                                        f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                                        f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
                                    )
                             # If inferred from default argument values (just to be super careful)
                             else:
                                    __UpperCamelCase       =  (
                                        f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                                        f'''The value `vision_config["{key}"]` will be overriden.'''
                                    )
                             logger.warning(__A	)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
               vision_config.update(_vision_config_dict	)

        if text_config is None:
               __UpperCamelCase       =  {}
               logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.'	)

        if vision_config is None:
               __UpperCamelCase       =  {}
               logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.'	)

        __UpperCamelCase       =  AltCLIPTextConfig(**__A	)
        __UpperCamelCase       =  AltCLIPVisionConfig(**__A	)

        __UpperCamelCase       =  projection_dim
        __UpperCamelCase       =  logit_scale_init_value
        __UpperCamelCase       =  1.0






 @classmethod
 def        _lowerCamelCase					(       cls       :							Union[str, Any]							,	__A       :							AltCLIPTextConfig							,	__A       :							AltCLIPVisionConfig							,	**__A       :							Optional[Any]	):
        return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)
        __UpperCamelCase       =  self.text_config.to_dict()
        __UpperCamelCase       =  self.vision_config.to_dict()
        __UpperCamelCase       =  self.__class__.model_type
        return output
