





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      (KDPMaDiscreteScheduler,)
 __SCREAMING_SNAKE_CASE							=      10



 def 							UpperCamelCase     (				self,**__lowerCamelCase					):
        A__		   =		{
            '''num_train_timesteps''': 1100,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
        }

        config.update(**__lowerCamelCase					)
        return config



 def 							UpperCamelCase     (				self					):
        for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],[0.0002, 0.002, 0.02]					):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        A__		   =		self.scheduler_classes[0]
        A__		   =		self.get_scheduler_config(prediction_type='''v_prediction'''					)
        A__		   =		scheduler_class(**__lowerCamelCase					)

        scheduler.set_timesteps(self.num_inference_steps					)

        A__		   =		self.dummy_model()
        A__		   =		self.dummy_sample_deter * scheduler.init_noise_sigma
        A__		   =		sample.to(__lowerCamelCase					)

        for i, t in enumerate(scheduler.timesteps					):
               A__		   =		scheduler.scale_model_input(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		model(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		output.prev_sample

        A__		   =		torch.sum(torch.abs(__lowerCamelCase					)					)
        A__		   =		torch.mean(torch.abs(__lowerCamelCase					)					)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.69_34E-07					) < 1E-2
               assert abs(result_mean.item() - 6.11_12E-10					) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07					) < 1E-2
               assert abs(result_mean.item() - 0.0002					) < 1E-3



 def 							UpperCamelCase     (				self					):
        if torch_device == "mps":
               return
        A__		   =		self.scheduler_classes[0]
        A__		   =		self.get_scheduler_config()
        A__		   =		scheduler_class(**__lowerCamelCase					)

        scheduler.set_timesteps(self.num_inference_steps					)

        A__		   =		self.dummy_model()
        A__		   =		self.dummy_sample_deter * scheduler.init_noise_sigma
        A__		   =		sample.to(__lowerCamelCase					)

        for i, t in enumerate(scheduler.timesteps					):
               A__		   =		scheduler.scale_model_input(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		model(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		output.prev_sample

        A__		   =		torch.sum(torch.abs(__lowerCamelCase					)					)
        A__		   =		torch.mean(torch.abs(__lowerCamelCase					)					)

        if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 20.4125					) < 1E-2
               assert abs(result_mean.item() - 0.0266					) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 20.4125					) < 1E-2
               assert abs(result_mean.item() - 0.0266					) < 1E-3




 def 							UpperCamelCase     (				self					):
        if torch_device == "mps":
               return
        A__		   =		self.scheduler_classes[0]
        A__		   =		self.get_scheduler_config()
        A__		   =		scheduler_class(**__lowerCamelCase					)

        scheduler.set_timesteps(self.num_inference_steps,device=__lowerCamelCase					)

        A__		   =		self.dummy_model()
        A__		   =		self.dummy_sample_deter.to(__lowerCamelCase					) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
               A__		   =		scheduler.scale_model_input(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		model(__lowerCamelCase,__lowerCamelCase					)

               A__		   =		scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					)
               A__		   =		output.prev_sample

        A__		   =		torch.sum(torch.abs(__lowerCamelCase					)					)
        A__		   =		torch.mean(torch.abs(__lowerCamelCase					)					)

        if str(__lowerCamelCase					).startswith('''cpu'''					):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 20.4125					) < 1E-2
               assert abs(result_mean.item() - 0.0266					) < 1E-3
        else:
               # CUDA
               assert abs(result_sum.item() - 20.4125					) < 1E-2
               assert abs(result_mean.item() - 0.0266					) < 1E-3












import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__: Optional[Any]							   =						logging.get_logger(__name__)

a__: Any							   =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(				self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
        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(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		backbone_config.get('''model_type'''					)
                      A__		   =		CONFIG_MAPPING[backbone_model_type]
                      A__		   =		config_class.from_dict(__lowerCamelCase					)

        A__		   =		use_timm_backbone
        A__		   =		backbone_config
        A__		   =		num_channels
        A__		   =		num_queries
        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__		   =		decoder_layerdrop
        A__		   =		encoder_layers
        A__		   =		auxiliary_loss
        A__		   =		position_embedding_type
        A__		   =		backbone
        A__		   =		use_pretrained_backbone
        A__		   =		dilation
        # Hungarian matcher
        A__		   =		class_cost
        A__		   =		bbox_cost
        A__		   =		giou_cost
        # Loss coefficients
        A__		   =		mask_loss_coefficient
        A__		   =		dice_loss_coefficient
        A__		   =		cls_loss_coefficient
        A__		   =		bbox_loss_coefficient
        A__		   =		giou_loss_coefficient
        A__		   =		focal_alpha
        super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase					)



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



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




 def 							UpperCamelCase     (				self					):
        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




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



 @property
 def 							UpperCamelCase     (				self					):
        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ('''pixel_mask''', {0: '''batch'''}),
            ]					)



 @property
 def 							UpperCamelCase     (				self					):
        return 1E-5




 @property
 def 							UpperCamelCase     (				self					):
        return 12





