"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		__A				(					lowerCamelCase__						):
     _UpperCamelCase				:     Tuple						   =						["image_processor", "tokenizer"]
     _UpperCamelCase				:     Optional[int]						   =						"ViltImageProcessor"
     _UpperCamelCase				:     Optional[Any]						   =						("BertTokenizer", "BertTokenizerFast")






     def __init__(						self							,	a__=None							,	a__=None							,	**a__    ):
       _lowerCAmelCase		:			str         =	None
       if "feature_extractor" in kwargs:
         warnings.warn(
             """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
             """ instead."""							,	a__							,	)
         _lowerCAmelCase		:			str         =	kwargs.pop("""feature_extractor"""    )

       _lowerCAmelCase		:			Any         =	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__(a__							,	a__    )
       _lowerCAmelCase		:			List[str]         =	self.image_processor






     def __call__(						self							,	a__							,	a__ = None							,	a__ = True							,	a__ = False							,	a__ = None							,	a__ = None							,	a__ = 0							,	a__ = None							,	a__ = None							,	a__ = None							,	a__ = False							,	a__ = False							,	a__ = False							,	a__ = False							,	a__ = True							,	a__ = None							,	**a__							,	):
       _lowerCAmelCase		:			int         =	self.tokenizer(
           text=a__							,	add_special_tokens=a__							,	padding=a__							,	truncation=a__							,	max_length=a__							,	stride=a__							,	pad_to_multiple_of=a__							,	return_token_type_ids=a__							,	return_attention_mask=a__							,	return_overflowing_tokens=a__							,	return_special_tokens_mask=a__							,	return_offsets_mapping=a__							,	return_length=a__							,	verbose=a__							,	return_tensors=a__							,	**a__							,	)
       # add pixel_values + pixel_mask
       _lowerCAmelCase		:			str         =	self.image_processor(a__							,	return_tensors=a__    )
       encoding.update(a__    )

       return encoding






     def        __A    (						self							,	*a__							,	**a__    ):
       return self.tokenizer.batch_decode(*a__							,	**a__    )






     def        __A    (						self							,	*a__							,	**a__    ):
       return self.tokenizer.decode(*a__							,	**a__    )






     @property
     def        __A    (						self    ):
       _lowerCAmelCase		:			Union[str, Any]         =	self.tokenizer.model_input_names
       _lowerCAmelCase		:			int         =	self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






     @property
     def        __A    (						self    ):
       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,	a__							,	)
       return self.image_processor_class






     @property
     def        __A    (						self    ):
       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,	a__							,	)
       return self.image_processor











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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {
    '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       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   """vit_msn"""


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



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase				:  Tuple						=		hidden_size
             _lowerCamelCase				:  str						=		num_hidden_layers
             _lowerCamelCase				:  int						=		num_attention_heads
             _lowerCamelCase				:  Optional[int]						=		intermediate_size
             _lowerCamelCase				:  Union[str, Any]						=		hidden_act
             _lowerCamelCase				:  str						=		hidden_dropout_prob
             _lowerCamelCase				:  List[Any]						=		attention_probs_dropout_prob
             _lowerCamelCase				:  Optional[int]						=		initializer_range
             _lowerCamelCase				:  Optional[Any]						=		layer_norm_eps
             _lowerCamelCase				:  Dict						=		image_size
             _lowerCamelCase				:  Tuple						=		patch_size
             _lowerCamelCase				:  List[str]						=		num_channels
             _lowerCamelCase				:  Optional[int]						=		qkv_bias

