


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase		   =			logging.get_logger(__name__)  # pylint: disable=invalid-name




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  lowercase_		:	List[str]     ,  lowercase_		:	Optional[int]=768					)   ->		List[str]:
             """simple docstring"""




             super().__init__(lowercase_					)
             _lowerCamelCase			:						str            =proj_size

             _lowerCamelCase			:						List[Any]            =CLIPVisionModel(lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =PaintByExampleMapper(lowercase_					)
             _lowerCamelCase			:						Optional[int]            =nn.LayerNorm(config.hidden_size					)
             _lowerCamelCase			:						List[Any]            =nn.Linear(config.hidden_size     ,  self.proj_size					)

             # uncondition for scaling
             _lowerCamelCase			:						Dict            =nn.Parameter(torch.randn((1, 1, self.proj_size)					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	str=False					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.model(pixel_values=lowercase_					)
             _lowerCamelCase			:						Any            =clip_output.pooler_output
             _lowerCamelCase			:						Any            =self.mapper(latent_states[:, None]					)
             _lowerCamelCase			:						Union[str, Any]            =self.final_layer_norm(lowercase_					)
             _lowerCamelCase			:						List[str]            =self.proj_out(lowercase_					)
             if return_uncond_vector:
                    return latent_states, self.uncond_vector

             return latent_states




class A    (  nn.Module	):






      def __init__(   self		:	Tuple     ,  lowercase_		:	Any					)   ->		int:
             """simple docstring"""




             super().__init__()
             _lowerCamelCase			:						str            =(config.num_hidden_layers + 1) // 5
             _lowerCamelCase			:						int            =config.hidden_size
             _lowerCamelCase			:						Tuple            =1
             _lowerCamelCase			:						Optional[Any]            =nn.ModuleList(
                 [
                     BasicTransformerBlock(lowercase_     ,  lowercase_     ,  lowercase_     ,  activation_fn='gelu'     ,  attention_bias=lowercase_					)
                     for _ in range(lowercase_					)
                 ]					)






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	int					)   ->		List[Any]:
             """simple docstring"""




             for block in self.blocks:
                    _lowerCamelCase			:						Any            =block(lowercase_					)

             return hidden_states






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	List[Any]					)   ->		None:
             """simple docstring"""




             warnings.warn(
                 'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileViTImageProcessor instead.'     ,  lowercase_     ,  )
             super().__init__(*lowercase_     ,  **lowercase_					)


