




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

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


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







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Optional[Any]						,							A_					:  str						,							A_					:  str=768  )   ->	Optional[Any]:

       """simple docstring"""






       super().__init__(A_  )
       lowerCamelCase_		     =    proj_size

       lowerCamelCase_		     =    CLIPVisionModel(A_  )
       lowerCamelCase_		     =    PaintByExampleMapper(A_  )
       lowerCamelCase_		     =    nn.LayerNorm(config.hidden_size  )
       lowerCamelCase_		     =    nn.Linear(config.hidden_size						,							self.proj_size  )

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





 def 				a__ (				self					:  List[str]						,							A_					:  List[str]						,							A_					:  Tuple=False  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model(pixel_values=A_  )
       lowerCamelCase_		     =    clip_output.pooler_output
       lowerCamelCase_		     =    self.mapper(latent_states[:, None]  )
       lowerCamelCase_		     =    self.final_layer_norm(A_  )
       lowerCamelCase_		     =    self.proj_out(A_  )
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states







class   A(						nn.Module   ):



 '''simple docstring'''


 def __init__(				self					:  Tuple						,							A_					:  Tuple  )   ->	Union[str, Any]:

       """simple docstring"""






       super().__init__()
       lowerCamelCase_		     =    (config.num_hidden_layers + 1) // 5
       lowerCamelCase_		     =    config.hidden_size
       lowerCamelCase_		     =    1
       lowerCamelCase_		     =    nn.ModuleList(
           [
               BasicTransformerBlock(A_						,							A_						,							A_						,							activation_fn='gelu'						,							attention_bias=A_  )
               for _ in range(A_  )
           ]  )





 def 				a__ (				self					:  str						,							A_					:  Any  )   ->	Optional[Any]:

       """simple docstring"""






       for block in self.blocks:
             lowerCamelCase_		     =    block(A_  )

       return hidden_states









import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


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







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






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



