




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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__			:       Any         =							logging.get_logger(__name__)

lowerCamelCase__			:       int         =							{
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}





class 	lowerCamelCase_							(				SCREAMING_SNAKE_CASE__	,       SCREAMING_SNAKE_CASE__ ):


 '''simple docstring'''







 lowercase_       =   '''resnet'''
 lowercase_       =   ['''basic''', '''bottleneck''']



 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=64    ,    _lowerCAmelCase  :		List[str]=[256, 512, 1_024, 2_048]    ,    _lowerCAmelCase  :		str=[3, 4, 6, 3]    ,    _lowerCAmelCase  :		Union[str, Any]="bottleneck"    ,    _lowerCAmelCase  :		str="relu"    ,    _lowerCAmelCase  :		Optional[Any]=False    ,    _lowerCAmelCase  :		Optional[int]=None    ,    _lowerCAmelCase  :		int=None    ,    **_lowerCAmelCase  :		Optional[Any]    ,    ):
       super().__init__(**A__						)
       if layer_type not in self.layer_types:
             raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types						)}"						)
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      embedding_size
       SCREAMING_SNAKE_CASE_     						=      hidden_sizes
       SCREAMING_SNAKE_CASE_     						=      depths
       SCREAMING_SNAKE_CASE_     						=      layer_type
       SCREAMING_SNAKE_CASE_     						=      hidden_act
       SCREAMING_SNAKE_CASE_     						=      downsample_in_first_stage
       SCREAMING_SNAKE_CASE_     						=      ['stem'] + [F"stage{idx}" for idx in range(1    ,    len(A__						) + 1						)]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      get_aligned_output_features_output_indices(
           out_features=A__    ,    out_indices=A__    ,    stage_names=self.stage_names						)





class 	lowerCamelCase_							(				SCREAMING_SNAKE_CASE__ ):


 '''simple docstring'''







 lowercase_       =   version.parse("1.11" )



 @property
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]						)




 @property
 def 				lowerCAmelCase_  (							self  :		str						):
       return 1E-3





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

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


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.LayerNorm(config.hidden_size						)
       SCREAMING_SNAKE_CASE_     						=      nn.Linear(config.hidden_size    ,    self.proj_size						)

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




 def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       super().__init__()
       SCREAMING_SNAKE_CASE_     						=      (config.num_hidden_layers + 1) // 5
       SCREAMING_SNAKE_CASE_     						=      config.hidden_size
       SCREAMING_SNAKE_CASE_     						=      1
       SCREAMING_SNAKE_CASE_     						=      nn.ModuleList(
           [
               BasicTransformerBlock(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states