'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

UpperCamelCase__     :	Tuple				=			{
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}


class 				_lowercase	(      UpperCamelCase_   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Union[str, Any]      							=   '''deit'''




      def __init__(				self					,lowerCamelCase_=768					,lowerCamelCase_=12					,lowerCamelCase_=12					,lowerCamelCase_=3072					,lowerCamelCase_="gelu"					,lowerCamelCase_=0.0					,lowerCamelCase_=0.0					,lowerCamelCase_=0.02					,lowerCamelCase_=1e-12					,lowerCamelCase_=224					,lowerCamelCase_=16					,lowerCamelCase_=3					,lowerCamelCase_=True					,lowerCamelCase_=16					,**lowerCamelCase_					,)			->			Union[str, Any]:



         '''simple docstring'''



         super().__init__(**__a							)

         UpperCAmelCase__							:				Optional[int]   				=  hidden_size
         UpperCAmelCase__							:				int   				=  num_hidden_layers
         UpperCAmelCase__							:				List[Any]   				=  num_attention_heads
         UpperCAmelCase__							:				Optional[int]   				=  intermediate_size
         UpperCAmelCase__							:				Tuple   				=  hidden_act
         UpperCAmelCase__							:				Optional[int]   				=  hidden_dropout_prob
         UpperCAmelCase__							:				Tuple   				=  attention_probs_dropout_prob
         UpperCAmelCase__							:				Optional[int]   				=  initializer_range
         UpperCAmelCase__							:				List[Any]   				=  layer_norm_eps
         UpperCAmelCase__							:				List[Any]   				=  image_size
         UpperCAmelCase__							:				Optional[Any]   				=  patch_size
         UpperCAmelCase__							:				List[Any]   				=  num_channels
         UpperCAmelCase__							:				Tuple   				=  qkv_bias
         UpperCAmelCase__							:				Optional[int]   				=  encoder_stride




class 				_lowercase	(      UpperCamelCase_   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   version.parse('''1.11'''   )




      @property
      def 		lowerCAmelCase__					(				self							)			->			Mapping[str, Mapping[int, str]]:



         '''simple docstring'''



         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			float:



         '''simple docstring'''



         return 1e-4





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  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_					,)
            UpperCAmelCase__							:				str   				=  kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase__							:				Optional[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__(lowerCamelCase_					,lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Dict:



         '''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:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if text is not None and images is not None:
            UpperCAmelCase__							:				str   				=  image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**lowerCamelCase_							)					,tensor_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.batch_decode(*lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.decode(*lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''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 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



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




