"""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      :		Union[str, Any]           =   logging.get_logger(__name__)


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

_a      :		Optional[int]           =   {
    '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      :		Tuple           =   {'facebook/blenderbot-3B': 128}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Union[str, Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[str]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Union[str, Any]							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			Optional[int]							=		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__				,     ):
       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						:       int  			=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
       if pre_tok_state.get("""add_prefix_space"""				,     a__  ) != add_prefix_space:
         _lowerCAmelCase						:       int  			=     getattr(a__				,     pre_tok_state.pop("""type"""  )  )
         _lowerCAmelCase						:       Optional[Any]  			=     add_prefix_space
         _lowerCAmelCase						:       List[Any]  			=     pre_tok_class(**a__  )

       _lowerCAmelCase						:       List[str]  			=     add_prefix_space

       _lowerCAmelCase						:       Dict  			=     """post_processor"""
       _lowerCAmelCase						:       List[Any]  			=     getattr(self.backend_tokenizer				,     a__				,     a__  )
       if tokenizer_component_instance:
         _lowerCAmelCase						:       Any  			=     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						:       List[Any]  			=     tuple(state["""sep"""]  )
         if "cls" in state:
           _lowerCAmelCase						:       Tuple  			=     tuple(state["""cls"""]  )

         _lowerCAmelCase						:       Union[str, Any]  			=     False

         if state.get("""add_prefix_space"""				,     a__  ) != add_prefix_space:
           _lowerCAmelCase						:       Any  			=     add_prefix_space
           _lowerCAmelCase						:       Optional[Any]  			=     True

         if state.get("""trim_offsets"""				,     a__  ) != trim_offsets:
           _lowerCAmelCase						:       Any  			=     trim_offsets
           _lowerCAmelCase						:       List[str]  			=     True

         if changes_to_apply:
           _lowerCAmelCase						:       Union[str, Any]  			=     getattr(a__				,     state.pop("""type"""  )  )
           _lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		self  ):
       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     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else value
       _lowerCAmelCase						:       List[str]  			=     value






     def     __A  (		self				,     *a__				,     **a__  ):
       _lowerCAmelCase						:       int  			=     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     __A  (		self				,     *a__				,     **a__  ):
       _lowerCAmelCase						:       str  			=     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     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self._tokenizer.model.save(a__				,     name=a__  )
       return tuple(a__  )






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     [self.sep_token_id]
       _lowerCAmelCase						:       Optional[int]  			=     [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     __A  (		self				,     a__				,     a__ = None  ):
       return token_ids_a + [self.eos_token_id]






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       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						:       Union[str, Any]  			=     """  """.join(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.encode(a__  )
       if len(a__  ) > self.model_max_length:
         _lowerCAmelCase						:       List[Any]  			=     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 ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




