

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

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


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

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					A_						,					A_=768	):







										'''simple docstring'''


										super().__init__(A_	)
										_UpperCAmelCase       :		Tuple 		=    proj_size

										_UpperCAmelCase       :		Any 		=    CLIPVisionModel(A_	)
										_UpperCAmelCase       :		Any 		=    PaintByExampleMapper(A_	)
										_UpperCAmelCase       :		List[str] 		=    nn.LayerNorm(config.hidden_size	)
										_UpperCAmelCase       :		str 		=    nn.Linear(config.hidden_size						,					self.proj_size	)

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





						def        _UpperCAmelCase				(       self						,					A_						,					A_=False	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.model(pixel_values=A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    clip_output.pooler_output
										_UpperCAmelCase       :		Optional[Any] 		=    self.mapper(latent_states[:, None]	)
										_UpperCAmelCase       :		Any 		=    self.final_layer_norm(A_	)
										_UpperCAmelCase       :		Tuple 		=    self.proj_out(A_	)
										if return_uncond_vector:
														return latent_states, self.uncond_vector

										return latent_states







class 				a  (	nn.Module				):


						def __init__(       self						,					A_	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		List[Any] 		=    (config.num_hidden_layers + 1) // 5
										_UpperCAmelCase       :		str 		=    config.hidden_size
										_UpperCAmelCase       :		Union[str, Any] 		=    1
										_UpperCAmelCase       :		str 		=    nn.ModuleList(
										    [
										        BasicTransformerBlock(A_						,					A_						,					A_						,					activation_fn="gelu"						,					attention_bias=A_	)
										        for _ in range(A_	)
										    ]	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										for block in self.blocks:
														_UpperCAmelCase       :		int 		=    block(A_	)

										return hidden_states





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


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


