"""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	:Optional[int]						    = """deformable_detr"""
    _UpperCAmelCase	:str						    = {
        """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__:   Any					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
          elif isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							):
             lowercase__:   Dict					=      backbone_config.get('''model_type'''							)
             lowercase__:   List[Any]					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Union[str, Any]					=      config_class.from_dict(SCREAMING_SNAKE_CASE__							)
       lowercase__:   List[str]					=      use_timm_backbone
       lowercase__:   Union[str, Any]					=      backbone_config
       lowercase__:   Tuple					=      num_channels
       lowercase__:   List[str]					=      num_queries
       lowercase__:   Tuple					=      max_position_embeddings
       lowercase__:   List[Any]					=      d_model
       lowercase__:   str					=      encoder_ffn_dim
       lowercase__:   List[Any]					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Optional[Any]					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   Any					=      decoder_attention_heads
       lowercase__:   Tuple					=      dropout
       lowercase__:   str					=      attention_dropout
       lowercase__:   Optional[Any]					=      activation_dropout
       lowercase__:   str					=      activation_function
       lowercase__:   Optional[Any]					=      init_std
       lowercase__:   Any					=      init_xavier_std
       lowercase__:   int					=      encoder_layerdrop
       lowercase__:   List[str]					=      auxiliary_loss
       lowercase__:   Tuple					=      position_embedding_type
       lowercase__:   List[str]					=      backbone
       lowercase__:   Optional[int]					=      use_pretrained_backbone
       lowercase__:   Optional[Any]					=      dilation
       # deformable attributes
       lowercase__:   Any					=      num_feature_levels
       lowercase__:   Any					=      encoder_n_points
       lowercase__:   Optional[int]					=      decoder_n_points
       lowercase__:   Optional[Any]					=      two_stage
       lowercase__:   str					=      two_stage_num_proposals
       lowercase__:   List[str]					=      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__:   List[str]					=      bbox_cost
       lowercase__:   Optional[Any]					=      giou_cost
       # Loss coefficients
       lowercase__:   Any					=      mask_loss_coefficient
       lowercase__:   List[Any]					=      dice_loss_coefficient
       lowercase__:   Any					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   str					=      eos_coefficient
       lowercase__:   List[Any]					=      focal_alpha
       lowercase__:   List[str]					=      disable_custom_kernels
       super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__    ,			**SCREAMING_SNAKE_CASE__							)






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






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






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

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						UNetaDModel(
      block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,       )
  return model





 def a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]
  __lowerCAmelCase											=						image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

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






