





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case__					:							Tuple													=     logging.get_logger(__name__)

snake_case__					:							Optional[int]													=     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """deformable_detr"""
   lowerCAmelCase__        = {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }




   def __init__(self	:int    ,   _UpperCamelCase	:List[Any]=True    ,   _UpperCamelCase	:Tuple=None    ,   _UpperCamelCase	:Tuple=3    ,   _UpperCamelCase	:Dict=300    ,   _UpperCamelCase	:Union[str, Any]=1024    ,   _UpperCamelCase	:Any=6    ,   _UpperCamelCase	:int=1024    ,   _UpperCamelCase	:Optional[int]=8    ,   _UpperCamelCase	:Dict=6    ,   _UpperCamelCase	:Any=1024    ,   _UpperCamelCase	:List[str]=8    ,   _UpperCamelCase	:Union[str, Any]=0.0    ,   _UpperCamelCase	:Union[str, Any]=True    ,   _UpperCamelCase	:Optional[int]="relu"    ,   _UpperCamelCase	:List[Any]=256    ,   _UpperCamelCase	:List[Any]=0.1    ,   _UpperCamelCase	:List[str]=0.0    ,   _UpperCamelCase	:List[str]=0.0    ,   _UpperCamelCase	:Dict=0.0_2    ,   _UpperCamelCase	:List[str]=1.0    ,   _UpperCamelCase	:Optional[Any]=True    ,   _UpperCamelCase	:List[Any]=False    ,   _UpperCamelCase	:Tuple="sine"    ,   _UpperCamelCase	:Any="resnet50"    ,   _UpperCamelCase	:Optional[Any]=True    ,   _UpperCamelCase	:int=False    ,   _UpperCamelCase	:Union[str, Any]=4    ,   _UpperCamelCase	:Optional[int]=4    ,   _UpperCamelCase	:Optional[int]=4    ,   _UpperCamelCase	:int=False    ,   _UpperCamelCase	:Any=300    ,   _UpperCamelCase	:Any=False    ,   _UpperCamelCase	:List[str]=1    ,   _UpperCamelCase	:Dict=5    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:str=1    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:List[Any]=5    ,   _UpperCamelCase	:Tuple=2    ,   _UpperCamelCase	:Dict=0.1    ,   _UpperCamelCase	:Any=0.2_5    ,   _UpperCamelCase	:Any=False    ,   **_UpperCamelCase	:List[str]    ,   )->					Dict:
         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
                     __A          =				CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
               elif isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
                     __A          =				backbone_config.get('''model_type''' )
                     __A          =				CONFIG_MAPPING[backbone_model_type]
                     __A          =				config_class.from_dict(_UpperCamelCase )
         __A          =				use_timm_backbone
         __A          =				backbone_config
         __A          =				num_channels
         __A          =				num_queries
         __A          =				max_position_embeddings
         __A          =				d_model
         __A          =				encoder_ffn_dim
         __A          =				encoder_layers
         __A          =				encoder_attention_heads
         __A          =				decoder_ffn_dim
         __A          =				decoder_layers
         __A          =				decoder_attention_heads
         __A          =				dropout
         __A          =				attention_dropout
         __A          =				activation_dropout
         __A          =				activation_function
         __A          =				init_std
         __A          =				init_xavier_std
         __A          =				encoder_layerdrop
         __A          =				auxiliary_loss
         __A          =				position_embedding_type
         __A          =				backbone
         __A          =				use_pretrained_backbone
         __A          =				dilation
         # deformable attributes
         __A          =				num_feature_levels
         __A          =				encoder_n_points
         __A          =				decoder_n_points
         __A          =				two_stage
         __A          =				two_stage_num_proposals
         __A          =				with_box_refine
         if two_stage is True and with_box_refine is False:
               raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
         # Hungarian matcher
         __A          =				class_cost
         __A          =				bbox_cost
         __A          =				giou_cost
         # Loss coefficients
         __A          =				mask_loss_coefficient
         __A          =				dice_loss_coefficient
         __A          =				bbox_loss_coefficient
         __A          =				giou_loss_coefficient
         __A          =				eos_coefficient
         __A          =				focal_alpha
         __A          =				disable_custom_kernels
         super().__init__(is_encoder_decoder=_UpperCamelCase    ,   **_UpperCamelCase )




   @property
   def _lowerCAmelCase      (self	:Union[str, Any] )->					int:
         return self.encoder_attention_heads




   @property
   def _lowerCAmelCase      (self	:List[str] )->					int:
         return self.d_model






   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A          =				copy.deepcopy(self.__dict__ )
         if self.backbone_config is not None:
               __A          =				self.backbone_config.to_dict()
         __A          =				self.__class__.model_type
         return output












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





