






'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							int	,  a	:							TransformeraDModel	,  a	:							AutoencoderKL	,  a	:							KarrasDiffusionSchedulers	,  a	:							Optional[Dict[int, str]] = None	,  ):






       '''simple docstring'''






       super().__init__()
       self.register_modules(transformer=a	,  vae=a	,  scheduler=a      )

       # create a imagenet -> id dictionary for easier use
       lowercase_						:     Union[str, Any]							     =  {}
       if idalabel is not None:
          for key, value in idalabel.items():
             for label in value.split(","      ):
                lowercase_						:     int							     =  int(a      )
          lowercase_						:     Tuple							     =  dict(sorted(self.labels.items()      )      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							Union[str, List[str]]      ):






       '''simple docstring'''






       if not isinstance(a	,  a      ):
          lowercase_						:     List[str]							     =  list(a      )

       for l in label:
          if l not in self.labels:
             raise ValueError(
                 f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

       return [self.labels[l] for l in label]





    @torch.no_grad()
    def __call__(      self	:							List[Any]	,  a	:							List[int]	,  a	:							float = 4.0	,  a	:							Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  a	:							int = 5_0	,  a	:							Optional[str] = "pil"	,  a	:							bool = True	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  len(a      )
       lowercase_						:     Optional[Any]							     =  self.transformer.config.sample_size
       lowercase_						:     List[str]							     =  self.transformer.config.in_channels

       lowercase_						:     str							     =  randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)	,  generator=a	,  device=self.device	,  dtype=self.transformer.dtype	,  )
       lowercase_						:     List[Any]							     =  torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

       lowercase_						:     Dict							     =  torch.tensor(a	,  device=self.device      ).reshape(-1      )
       lowercase_						:     str							     =  torch.tensor([1_0_0_0] * batch_size	,  device=self.device      )
       lowercase_						:     str							     =  torch.cat([class_labels, class_null]	,  0      ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(a      )

       for t in self.progress_bar(self.scheduler.timesteps      ):
          if guidance_scale > 1:
             lowercase_						:     List[Any]							     =  latent_model_input[: len(a      ) // 2]
             lowercase_						:     Union[str, Any]							     =  torch.cat([half, half]	,  dim=0      )
          lowercase_						:     Any							     =  self.scheduler.scale_model_input(a	,  a      )

          lowercase_						:     Dict							     =  t
          if not torch.is_tensor(a      ):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             lowercase_						:     Optional[Any]							     =  latent_model_input.device.type == "mps"
             if isinstance(a	,  a      ):
                lowercase_						:     List[Any]							     =  torch.floataa if is_mps else torch.floataa
             else:
                lowercase_						:     List[str]							     =  torch.intaa if is_mps else torch.intaa
             lowercase_						:     Optional[int]							     =  torch.tensor([timesteps]	,  dtype=a	,  device=latent_model_input.device      )
          elif len(timesteps.shape      ) == 0:
             lowercase_						:     List[str]							     =  timesteps[None].to(latent_model_input.device      )
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          lowercase_						:     List[str]							     =  timesteps.expand(latent_model_input.shape[0]      )
          # predict noise model_output
          lowercase_						:     Optional[int]							     =  self.transformer(
              a	,  timestep=a	,  class_labels=a      ).sample

          # perform guidance
          if guidance_scale > 1:
             lowercase_     ,		lowercase_						:     Optional[Any]							     =  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             lowercase_     ,		lowercase_						:     Dict							     =  torch.split(a	,  len(a      ) // 2	,  dim=0      )

             lowercase_						:     Optional[int]							     =  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             lowercase_						:     List[str]							     =  torch.cat([half_eps, half_eps]	,  dim=0      )

             lowercase_						:     Tuple							     =  torch.cat([eps, rest]	,  dim=1      )

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
             lowercase_     ,		lowercase_						:     Optional[Any]							     =  torch.split(a	,  a	,  dim=1      )
          else:
             lowercase_						:     List[str]							     =  noise_pred

          # compute previous image: x_t -> x_t-1
          lowercase_						:     Any							     =  self.scheduler.step(a	,  a	,  a      ).prev_sample

       if guidance_scale > 1:
          lowercase_     ,		lowercase_						:     Optional[Any]							     =  latent_model_input.chunk(2	,  dim=0      )
       else:
          lowercase_						:     Dict							     =  latent_model_input

       lowercase_						:     str							     =  1 / self.vae.config.scaling_factor * latents
       lowercase_						:     List[Any]							     =  self.vae.decode(a      ).sample

       lowercase_						:     List[str]							     =  (samples / 2 + 0.5).clamp(0	,  1      )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       lowercase_						:     Dict							     =  samples.cpu().permute(0	,  2	,  3	,  1      ).float().numpy()

       if output_type == "pil":
          lowercase_						:     Tuple							     =  self.numpy_to_pil(a      )

       if not return_dict:
          return (samples,)

       return ImagePipelineOutput(images=a      )












'''simple docstring'''
import copy

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


UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Union[str, Any]									=     'deta'
    __lowerCamelCase: str									=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }




    def __init__(      self	:							List[str]	,  a	:							Union[str, Any]=None	,  a	:							Dict=9_0_0	,  a	:							str=2_0_4_8	,  a	:							Union[str, Any]=6	,  a	:							int=2_0_4_8	,  a	:							Optional[int]=8	,  a	:							Optional[Any]=6	,  a	:							Optional[Any]=1_0_2_4	,  a	:							Any=8	,  a	:							Dict=0.0	,  a	:							Optional[int]=True	,  a	:							Union[str, Any]="relu"	,  a	:							str=2_5_6	,  a	:							str=0.1	,  a	:							Any=0.0	,  a	:							Optional[Any]=0.0	,  a	:							str=0.02	,  a	:							int=1.0	,  a	:							Tuple=True	,  a	:							Optional[int]=False	,  a	:							str="sine"	,  a	:							List[Any]=5	,  a	:							str=4	,  a	:							Any=4	,  a	:							Optional[Any]=True	,  a	:							Union[str, Any]=3_0_0	,  a	:							List[Any]=True	,  a	:							Tuple=True	,  a	:							Any=1	,  a	:							int=5	,  a	:							Optional[int]=2	,  a	:							Tuple=1	,  a	:							List[str]=1	,  a	:							Tuple=5	,  a	:							Tuple=2	,  a	:							List[str]=0.1	,  a	:							Tuple=0.25	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
          lowercase_						:     Optional[int]							     =  CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
       else:
          if isinstance(a	,  a      ):
             lowercase_						:     Any							     =  backbone_config.pop("model_type"      )
             lowercase_						:     str							     =  CONFIG_MAPPING[backbone_model_type]
             lowercase_						:     Any							     =  config_class.from_dict(a      )

       lowercase_						:     Optional[int]							     =  backbone_config
       lowercase_						:     Dict							     =  num_queries
       lowercase_						:     Tuple							     =  max_position_embeddings
       lowercase_						:     Union[str, Any]							     =  d_model
       lowercase_						:     Optional[Any]							     =  encoder_ffn_dim
       lowercase_						:     Any							     =  encoder_layers
       lowercase_						:     int							     =  encoder_attention_heads
       lowercase_						:     Tuple							     =  decoder_ffn_dim
       lowercase_						:     Dict							     =  decoder_layers
       lowercase_						:     Dict							     =  decoder_attention_heads
       lowercase_						:     List[Any]							     =  dropout
       lowercase_						:     Union[str, Any]							     =  attention_dropout
       lowercase_						:     List[str]							     =  activation_dropout
       lowercase_						:     Optional[Any]							     =  activation_function
       lowercase_						:     str							     =  init_std
       lowercase_						:     Dict							     =  init_xavier_std
       lowercase_						:     List[Any]							     =  encoder_layerdrop
       lowercase_						:     Optional[int]							     =  auxiliary_loss
       lowercase_						:     str							     =  position_embedding_type
       # deformable attributes
       lowercase_						:     Optional[int]							     =  num_feature_levels
       lowercase_						:     List[Any]							     =  encoder_n_points
       lowercase_						:     Optional[Any]							     =  decoder_n_points
       lowercase_						:     List[Any]							     =  two_stage
       lowercase_						:     Any							     =  two_stage_num_proposals
       lowercase_						:     List[str]							     =  with_box_refine
       lowercase_						:     Optional[int]							     =  assign_first_stage
       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
       lowercase_						:     int							     =  class_cost
       lowercase_						:     Optional[int]							     =  bbox_cost
       lowercase_						:     str							     =  giou_cost
       # Loss coefficients
       lowercase_						:     Optional[int]							     =  mask_loss_coefficient
       lowercase_						:     Union[str, Any]							     =  dice_loss_coefficient
       lowercase_						:     Optional[Any]							     =  bbox_loss_coefficient
       lowercase_						:     Optional[Any]							     =  giou_loss_coefficient
       lowercase_						:     Any							     =  eos_coefficient
       lowercase_						:     str							     =  focal_alpha
       super().__init__(is_encoder_decoder=a	,  **a      )




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.encoder_attention_heads




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.d_model





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  copy.deepcopy(self.__dict__      )
       lowercase_						:     Union[str, Any]							     =  self.backbone_config.to_dict()
       lowercase_						:     str							     =  self.__class__.model_type
       return output




