






'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 							__SCREAMING_SNAKE_CASE      (							_lowerCAmelCase     ):
      __a  					=["image_processor", "tokenizer"]
      __a  					="FlavaImageProcessor"
      __a  					=("BertTokenizer", "BertTokenizerFast")




      def __init__(  self ,   lowerCamelCase=None ,   lowerCamelCase=None ,   **lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    None
         if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.' ,   lowerCamelCase ,   )
            __a				     =    kwargs.pop('feature_extractor'      )

         __a				     =    image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'      )
         if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'      )

         super().__init__(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    self.image_processor




      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = False ,   lowerCamelCase = False ,   lowerCamelCase = None ,   lowerCamelCase = 0 ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = False ,   lowerCamelCase = False ,   lowerCamelCase = False ,   lowerCamelCase = False ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Any:



         '''simple docstring'''

         if text is None and images is None:
            raise ValueError('You have to specify either text or images. Both cannot be none.'      )

         if text is not None:
            __a				     =    self.tokenizer(
                text=lowerCamelCase ,   add_special_tokens=lowerCamelCase ,   padding=lowerCamelCase ,   truncation=lowerCamelCase ,   max_length=lowerCamelCase ,   stride=lowerCamelCase ,   pad_to_multiple_of=lowerCamelCase ,   return_token_type_ids=lowerCamelCase ,   return_attention_mask=lowerCamelCase ,   return_overflowing_tokens=lowerCamelCase ,   return_special_tokens_mask=lowerCamelCase ,   return_offsets_mapping=lowerCamelCase ,   return_length=lowerCamelCase ,   verbose=lowerCamelCase ,   return_tensors=lowerCamelCase ,   **lowerCamelCase ,   )
         if images is not None:
            __a				     =    self.image_processor(
                lowerCamelCase ,   return_image_mask=lowerCamelCase ,   return_codebook_pixels=lowerCamelCase ,   return_tensors=lowerCamelCase ,   **lowerCamelCase ,   )

         if text is not None and images is not None:
            encoding.update(lowerCamelCase      )
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**lowerCamelCase      ) ,   tensor_type=lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->Dict:



         '''simple docstring'''
         return self.tokenizer.batch_decode(*lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   *lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         return self.tokenizer.decode(*lowerCamelCase ,   **lowerCamelCase      )




      @property
      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.tokenizer.model_input_names
         __a				     =    self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




      @property
      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,   lowerCamelCase ,   )
         return self.image_processor_class






      @property
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,   lowerCamelCase ,   )
         return self.image_processor







'''simple docstring'''



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


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias