
"""simple docstring"""




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							(EulerDiscreteScheduler,)
     _A								=							10



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: Any 							=						{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**__UpperCamelCase  )
          return config



     def  lowerCAmelCase__       (      self  )->					str:
          for timesteps in [10, 50, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,			[0.0002, 0.002, 0.02]  ):
               self.check_over_configs(beta_start=__UpperCamelCase ,			beta_end=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					str:
          for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: Optional[int] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: List[Any] 							=						self.get_scheduler_config()
          UpperCAmelCase__							: str 							=						scheduler_class(**__UpperCamelCase  )

          scheduler.set_timesteps(self.num_inference_steps  )

          UpperCAmelCase__							: int 							=						torch.manual_seed(0  )

          UpperCAmelCase__							: List[Any] 							=						self.dummy_model()
          UpperCAmelCase__							: Tuple 							=						self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__							: List[Any] 							=						sample.to(__UpperCamelCase  )

          for i, t in enumerate(scheduler.timesteps  ):
               UpperCAmelCase__							: Optional[Any] 							=						scheduler.scale_model_input(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: List[Any] 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: Any 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			generator=__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						output.prev_sample

          UpperCAmelCase__							: str 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: List[Any] 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

          assert abs(result_sum.item() - 10.0807  ) < 1E-2
          assert abs(result_mean.item() - 0.0131  ) < 1E-3



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Optional[Any] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Optional[Any] 							=						self.get_scheduler_config(prediction_type="v_prediction"  )
          UpperCAmelCase__							: Union[str, Any] 							=						scheduler_class(**__UpperCamelCase  )

          scheduler.set_timesteps(self.num_inference_steps  )

          UpperCAmelCase__							: Tuple 							=						torch.manual_seed(0  )

          UpperCAmelCase__							: Optional[int] 							=						self.dummy_model()
          UpperCAmelCase__							: Any 							=						self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCAmelCase__							: Union[str, Any] 							=						sample.to(__UpperCamelCase  )

          for i, t in enumerate(scheduler.timesteps  ):
               UpperCAmelCase__							: List[str] 							=						scheduler.scale_model_input(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: str 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: Optional[int] 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			generator=__UpperCamelCase  )
               UpperCAmelCase__							: Dict 							=						output.prev_sample

          UpperCAmelCase__							: Tuple 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

          assert abs(result_sum.item() - 0.0002  ) < 1E-2
          assert abs(result_mean.item() - 2.26_76E-06  ) < 1E-3



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[int] 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: str 							=						self.get_scheduler_config()
          UpperCAmelCase__							: Optional[int] 							=						scheduler_class(**__UpperCamelCase  )

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

          UpperCAmelCase__							: Union[str, Any] 							=						torch.manual_seed(0  )

          UpperCAmelCase__							: Optional[Any] 							=						self.dummy_model()
          UpperCAmelCase__							: List[str] 							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCAmelCase__							: List[Any] 							=						sample.to(__UpperCamelCase  )

          for t in scheduler.timesteps:
               UpperCAmelCase__							: Tuple 							=						scheduler.scale_model_input(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: Tuple 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: List[Any] 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			generator=__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						output.prev_sample

          UpperCAmelCase__							: int 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: Dict 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

          assert abs(result_sum.item() - 10.0807  ) < 1E-2
          assert abs(result_mean.item() - 0.0131  ) < 1E-3



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Dict 							=						self.scheduler_classes[0]
          UpperCAmelCase__							: Any 							=						self.get_scheduler_config()
          UpperCAmelCase__							: str 							=						scheduler_class(**__UpperCamelCase ,			use_karras_sigmas=__UpperCamelCase  )

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

          UpperCAmelCase__							: Optional[Any] 							=						torch.manual_seed(0  )

          UpperCAmelCase__							: List[str] 							=						self.dummy_model()
          UpperCAmelCase__							: Dict 							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          UpperCAmelCase__							: Any 							=						sample.to(__UpperCamelCase  )

          for t in scheduler.timesteps:
               UpperCAmelCase__							: str 							=						scheduler.scale_model_input(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: List[Any] 							=						model(__UpperCamelCase ,			__UpperCamelCase  )

               UpperCAmelCase__							: str 							=						scheduler.step(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			generator=__UpperCamelCase  )
               UpperCAmelCase__							: List[str] 							=						output.prev_sample

          UpperCAmelCase__							: Optional[int] 							=						torch.sum(torch.abs(__UpperCamelCase  )  )
          UpperCAmelCase__							: int 							=						torch.mean(torch.abs(__UpperCamelCase  )  )

          assert abs(result_sum.item() - 124.52_2994_9951_1719  ) < 1E-2
          assert abs(result_mean.item() - 0.1_6213_9326_3339_9963  ) < 1E-3





"""simple docstring"""




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__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	Optional[int]          =    {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'table-transformer'
     _A								=							['past_key_values']
     _A								=							{
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }



     def __init__(      self ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=3 ,			__UpperCamelCase=1_00 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=True ,			__UpperCamelCase="relu" ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=False ,			__UpperCamelCase="sine" ,			__UpperCamelCase="resnet50" ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=0.1 ,			**__UpperCamelCase ,			)->					List[Any]:
          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."  )
                    UpperCAmelCase__							: Any 							=						CONFIG_MAPPING["resnet"](out_features=["stage4"]  )
               elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: int 							=						backbone_config.get("model_type"  )
                    UpperCAmelCase__							: Optional[Any] 							=						CONFIG_MAPPING[backbone_model_type]
                    UpperCAmelCase__							: int 							=						config_class.from_dict(__UpperCamelCase  )
               # set timm attributes to None
               UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						None, None, None

          UpperCAmelCase__							: Optional[int] 							=						use_timm_backbone
          UpperCAmelCase__							: Dict 							=						backbone_config
          UpperCAmelCase__							: List[Any] 							=						num_channels
          UpperCAmelCase__							: Any 							=						num_queries
          UpperCAmelCase__							: int 							=						d_model
          UpperCAmelCase__							: Optional[int] 							=						encoder_ffn_dim
          UpperCAmelCase__							: str 							=						encoder_layers
          UpperCAmelCase__							: Dict 							=						encoder_attention_heads
          UpperCAmelCase__							: Optional[Any] 							=						decoder_ffn_dim
          UpperCAmelCase__							: Tuple 							=						decoder_layers
          UpperCAmelCase__							: Optional[Any] 							=						decoder_attention_heads
          UpperCAmelCase__							: List[str] 							=						dropout
          UpperCAmelCase__							: Tuple 							=						attention_dropout
          UpperCAmelCase__							: List[Any] 							=						activation_dropout
          UpperCAmelCase__							: Dict 							=						activation_function
          UpperCAmelCase__							: Optional[Any] 							=						init_std
          UpperCAmelCase__							: List[str] 							=						init_xavier_std
          UpperCAmelCase__							: int 							=						encoder_layerdrop
          UpperCAmelCase__							: Tuple 							=						decoder_layerdrop
          UpperCAmelCase__							: int 							=						encoder_layers
          UpperCAmelCase__							: Dict 							=						auxiliary_loss
          UpperCAmelCase__							: Union[str, Any] 							=						position_embedding_type
          UpperCAmelCase__							: List[str] 							=						backbone
          UpperCAmelCase__							: List[Any] 							=						use_pretrained_backbone
          UpperCAmelCase__							: List[str] 							=						dilation
          # Hungarian matcher
          UpperCAmelCase__							: Dict 							=						class_cost
          UpperCAmelCase__							: Any 							=						bbox_cost
          UpperCAmelCase__							: Tuple 							=						giou_cost
          # Loss coefficients
          UpperCAmelCase__							: Any 							=						mask_loss_coefficient
          UpperCAmelCase__							: Dict 							=						dice_loss_coefficient
          UpperCAmelCase__							: Any 							=						bbox_loss_coefficient
          UpperCAmelCase__							: Tuple 							=						giou_loss_coefficient
          UpperCAmelCase__							: List[Any] 							=						eos_coefficient
          super().__init__(is_encoder_decoder=__UpperCamelCase ,			**__UpperCamelCase  )



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.encoder_attention_heads



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.d_model







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



     @property
     def  lowerCAmelCase__       (      self  )->					Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]  )



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-5



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return 12



