




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class       a__	( UpperCamelCase__				):
     a		:      Any		 =     (DDIMParallelScheduler,)
     a		:      Dict		 =     (("""eta""", 0.0), ("""num_inference_steps""", 50))







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



          '''simple docstring'''



          a     =				{
              "num_train_timesteps": 1000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**A			)
          return config







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Any:



          '''simple docstring'''



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

          a  ,				a     =				10, 0.0

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter

          scheduler.set_timesteps(A			)

          for t in scheduler.timesteps:
               a     =				model(A  ,		A			)
               a     =				scheduler.step(A  ,		A  ,		A  ,		A			).prev_sample

          return sample







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



          '''simple docstring'''



          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=A			)







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



          '''simple docstring'''



          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=A			)

          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(steps_offset=1			)
          a     =				scheduler_class(**A			)
          scheduler.set_timesteps(5			)
          assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]			)			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,		[0.0_0_2, 0.0_2, 0.2, 2]			):
               self.check_over_configs(beta_start=A  ,		beta_end=A			)







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



          '''simple docstring'''



          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=A			)







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



          '''simple docstring'''



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







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



          '''simple docstring'''



          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=A			)







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



          '''simple docstring'''



          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=A			)







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



          '''simple docstring'''



          self.check_over_configs(thresholding=A			)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=A  ,		prediction_type=A  ,		sample_max_value=A  ,		)







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



          '''simple docstring'''



          for t in [1, 10, 49]:
               self.check_over_forward(time_step=A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]			):
               self.check_over_forward(time_step=A  ,		num_inference_steps=A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]			):
               self.check_over_forward(time_step=A  ,		eta=A			)







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



          '''simple docstring'''



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

          assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0			) - 0.0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400			) - 0.1_4_7_7_1			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960			) - 0.3_2_4_6_0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0			) - 0.0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487  ,		486			) - 0.0_0_9_7_9			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998			) - 0.0_2			)			) < 1e-5







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



          '''simple docstring'''



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

          a  ,				a     =				10, 0.0
          scheduler.set_timesteps(A			)

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter
          a     =				self.dummy_sample_deter + 0.1
          a     =				self.dummy_sample_deter - 0.1

          a     =				samplea.shape[0]
          a     =				torch.stack([samplea, samplea, samplea]  ,		dim=0			)
          a     =				torch.arange(A			)[0:3, None].repeat(1  ,		A			)

          a     =				model(samples.flatten(0  ,		1			)  ,		timesteps.flatten(0  ,		1			)			)
          a     =				scheduler.batch_step_no_noise(A  ,		timesteps.flatten(0  ,		1			)  ,		samples.flatten(0  ,		1			)  ,		A			)

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

          assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1e-2
          assert abs(result_mean.item() - 0.4_9_8_2			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop()

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

          assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
          assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop(prediction_type="v_prediction"			)

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

          assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
          assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.full_loop(set_alpha_to_one=A  ,		beta_start=0.0_1			)
          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
          assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop(set_alpha_to_one=A  ,		beta_start=0.0_1			)
          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
          assert abs(result_mean.item() - 0.1_9_4_1			) < 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

