
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							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """microsoft/swin-tiny-patch4-window7-224""": (
        """https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class   UpperCAmelCase		(						A_       ,A_ ):
   A__					:		Optional[Any]						     =     "swin"

   A__					:		str						     =     {
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=2_24    ,						snake_case__		:					List[str]=4    ,						snake_case__		:					Union[str, Any]=3    ,						snake_case__		:					Tuple=96    ,						snake_case__		:					List[Any]=[2, 2, 6, 2]    ,						snake_case__		:					Dict=[3, 6, 12, 24]    ,						snake_case__		:					Optional[Any]=7    ,						snake_case__		:					int=4.0    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					List[str]=0.0    ,						snake_case__		:					Optional[int]=0.0    ,						snake_case__		:					Tuple=0.1    ,						snake_case__		:					Dict="gelu"    ,						snake_case__		:					int=False    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[str]=1e-5    ,						snake_case__		:					Union[str, Any]=32    ,						snake_case__		:					Any=None    ,						snake_case__		:					Tuple=None    ,						**snake_case__		:					List[str]    ,						)		->	Tuple:


          '''simple docstring'''


          super().__init__(**snake_case__ )

          snake_case       :   Union[str, Any]							=      image_size
          snake_case       :   List[Any]							=      patch_size
          snake_case       :   str							=      num_channels
          snake_case       :   List[Any]							=      embed_dim
          snake_case       :   Dict							=      depths
          snake_case       :   Optional[Any]							=      len(snake_case__ )
          snake_case       :   Optional[Any]							=      num_heads
          snake_case       :   Any							=      window_size
          snake_case       :   str							=      mlp_ratio
          snake_case       :   Optional[int]							=      qkv_bias
          snake_case       :   Union[str, Any]							=      hidden_dropout_prob
          snake_case       :   Optional[Any]							=      attention_probs_dropout_prob
          snake_case       :   Optional[int]							=      drop_path_rate
          snake_case       :   Optional[Any]							=      hidden_act
          snake_case       :   Optional[int]							=      use_absolute_embeddings
          snake_case       :   List[Any]							=      layer_norm_eps
          snake_case       :   Any							=      initializer_range
          snake_case       :   Optional[Any]							=      encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case       :   Any							=      int(embed_dim * 2 ** (len(snake_case__ ) - 1) )
          snake_case       :   Union[str, Any]							=      ["stem"] + [f"""stage{idx}""" for idx in range(1    ,						len(snake_case__ ) + 1 )]
          snake_case				,					snake_case       :   Optional[Any]							=      get_aligned_output_features_output_indices(
              out_features=snake_case__    ,						out_indices=snake_case__    ,						stage_names=self.stage_names )






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     version.parse("1.11" )



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


          '''simple docstring'''


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



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	float:


          '''simple docstring'''


          return 1e-4











"""simple docstring"""






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

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


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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

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

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






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



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






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states