




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class       a__	( UpperCamelCase__				):
     a		:      Tuple		 =     (KDPMaDiscreteScheduler,)
     a		:      Optional[Any]		 =     10







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				{
              "num_train_timesteps": 1100,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**A			)
          return config







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,		[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
               self.check_over_configs(beta_start=A  ,		beta_end=A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(prediction_type="v_prediction"			)
          a     =				scheduler_class(**A			)

          scheduler.set_timesteps(self.num_inference_steps			)

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter * scheduler.init_noise_sigma
          a     =				sample.to(A			)

          for i, t in enumerate(scheduler.timesteps			):
               a     =				scheduler.scale_model_input(A  ,		A			)

               a     =				model(A  ,		A			)

               a     =				scheduler.step(A  ,		A  ,		A			)
               a     =				output.prev_sample

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1_112e-10			) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 4.693_428_650_170_972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3







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



          '''simple docstring'''



          if torch_device == "mps":
               return
          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**A			)

          scheduler.set_timesteps(self.num_inference_steps			)

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter * scheduler.init_noise_sigma
          a     =				sample.to(A			)

          for i, t in enumerate(scheduler.timesteps			):
               a     =				scheduler.scale_model_input(A  ,		A			)

               a     =				model(A  ,		A			)

               a     =				scheduler.step(A  ,		A  ,		A			)
               a     =				output.prev_sample

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
               assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
               assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          if torch_device == "mps":
               return
          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**A			)

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

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
               a     =				scheduler.scale_model_input(A  ,		A			)

               a     =				model(A  ,		A			)

               a     =				scheduler.step(A  ,		A  ,		A			)
               a     =				output.prev_sample

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          if str(A			).startswith("cpu"			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
               assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3
          else:
               # CUDA
               assert abs(result_sum.item() - 2_0.4_1_2_5			) < 1e-2
               assert abs(result_mean.item() - 0.0_2_6_6			) < 1e-3







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


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

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          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(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          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     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

