


"""simple docstring"""



import copy

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


__snake_case             =   logging.get_logger(__name__)

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







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Optional[int]							  =  '''deta'''
   __UpperCAmelCase			:	List[str]							  =  {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=900						,     UpperCamelCase__=2048						,     UpperCamelCase__=6						,     UpperCamelCase__=2048						,     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__=5						,     UpperCamelCase__=4						,     UpperCamelCase__=4						,     UpperCamelCase__=True						,     UpperCamelCase__=300						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=1						,     UpperCamelCase__=5						,     UpperCamelCase__=2						,     UpperCamelCase__=1						,     UpperCamelCase__=1						,     UpperCamelCase__=5						,     UpperCamelCase__=2						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.25						,     **UpperCamelCase__						,     )     ->		Dict:

       '''simple docstring'''




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

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







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return self.encoder_attention_heads







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return self.d_model







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




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









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   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 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 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 _lowerCAmelCase    (				unittest.TestCase     ):







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

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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





