





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


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

__lowerCamelCase :  Dict										=  {
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:List[Any]							=     'swinv2'

      _UpperCAmelCase			:Tuple							=     {
          '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_=0.02 ,							A_=1e-5 ,							A_=32 ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )

       UpperCamelCase			:      List[Any] 			=     image_size
       UpperCamelCase			:      List[str] 			=     patch_size
       UpperCamelCase			:      str 			=     num_channels
       UpperCamelCase			:      str 			=     embed_dim
       UpperCamelCase			:      Dict 			=     depths
       UpperCamelCase			:      List[Any] 			=     len(A_     )
       UpperCamelCase			:      Optional[Any] 			=     num_heads
       UpperCamelCase			:      List[Any] 			=     window_size
       UpperCamelCase			:      Any 			=     mlp_ratio
       UpperCamelCase			:      List[Any] 			=     qkv_bias
       UpperCamelCase			:      int 			=     hidden_dropout_prob
       UpperCamelCase			:      List[Any] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Tuple 			=     drop_path_rate
       UpperCamelCase			:      Optional[Any] 			=     hidden_act
       UpperCamelCase			:      List[Any] 			=     use_absolute_embeddings
       UpperCamelCase			:      int 			=     layer_norm_eps
       UpperCamelCase			:      Any 			=     initializer_range
       UpperCamelCase			:      Union[str, 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
       UpperCamelCase			:      Union[str, Any] 			=     int(embed_dim * 2 ** (len(A_     ) - 1)     )
       UpperCamelCase			:      Optional[Any] 			=     (0, 0, 0, 0)







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)

__lowerCamelCase :  Union[str, Any]										=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase :  Dict										=  {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase :  Tuple										=  {
    """facebook/blenderbot_small-90M""": 512,
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:List[Any]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     BlenderbotSmallTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_=False ,							A_=True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           ByteLevelBPETokenizer(
               vocab=A_ ,							merges=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_ ,							) ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							**A_ ,							)
       UpperCamelCase			:      Union[str, Any] 			=     add_prefix_space





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def  __UpperCamelCase(				self ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      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]
