"""simple docstring"""






import copy

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


__A			   = logging.get_logger(__name__)

__A			   = {
    "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     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:int						    = "deformable_detr"
    _UpperCAmelCase	:Union[str, Any]						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=3    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase="resnet50"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			_UpperCAmelCase=False    ,			**_UpperCAmelCase    ,			):
       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.'''							)
             lowercase__:   Tuple					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
          elif isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.get('''model_type'''							)
             lowercase__:   Tuple					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   int					=      config_class.from_dict(_UpperCAmelCase							)
       lowercase__:   int					=      use_timm_backbone
       lowercase__:   List[Any]					=      backbone_config
       lowercase__:   Union[str, Any]					=      num_channels
       lowercase__:   Any					=      num_queries
       lowercase__:   Union[str, Any]					=      max_position_embeddings
       lowercase__:   Dict					=      d_model
       lowercase__:   Any					=      encoder_ffn_dim
       lowercase__:   str					=      encoder_layers
       lowercase__:   Optional[Any]					=      encoder_attention_heads
       lowercase__:   str					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   Any					=      decoder_attention_heads
       lowercase__:   str					=      dropout
       lowercase__:   Optional[int]					=      attention_dropout
       lowercase__:   Any					=      activation_dropout
       lowercase__:   Optional[int]					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Tuple					=      init_xavier_std
       lowercase__:   Any					=      encoder_layerdrop
       lowercase__:   int					=      auxiliary_loss
       lowercase__:   Any					=      position_embedding_type
       lowercase__:   List[str]					=      backbone
       lowercase__:   List[str]					=      use_pretrained_backbone
       lowercase__:   List[str]					=      dilation
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[int]					=      encoder_n_points
       lowercase__:   Tuple					=      decoder_n_points
       lowercase__:   List[Any]					=      two_stage
       lowercase__:   Optional[Any]					=      two_stage_num_proposals
       lowercase__:   List[Any]					=      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
       lowercase__:   List[str]					=      class_cost
       lowercase__:   Any					=      bbox_cost
       lowercase__:   Dict					=      giou_cost
       # Loss coefficients
       lowercase__:   Union[str, Any]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   Any					=      bbox_loss_coefficient
       lowercase__:   List[Any]					=      giou_loss_coefficient
       lowercase__:   Tuple					=      eos_coefficient
       lowercase__:   int					=      focal_alpha
       lowercase__:   Dict					=      disable_custom_kernels
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       if self.backbone_config is not None:
          lowercase__:   List[str]					=      self.backbone_config.to_dict()
       lowercase__:   List[Any]					=      self.__class__.model_type
       return output

"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
