code: import torch
  
  from diffusers import LMSDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							a__    ):
      UpperCAmelCase_			:       List[Any]						 =							(LMSDiscreteScheduler,)
      UpperCAmelCase_			:       int						 =							10
  
  
  
  
      def 						__lowerCamelCase (					self  :   str      ,      **lowerCAmelCase  :   Tuple   )	-> Any:
        a__     		=     {
            "num_train_timesteps": 1100,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }
  
        config.update(**__A   )
        return config
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Tuple:
  
  
  
  
  
  
  
        for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=__A   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Tuple:
  
  
  
  
  
  
  
        for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,      [0.0_002, 0.002, 0.02]   ):
          self.check_over_configs(beta_start=__A      ,      beta_end=__A   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> str:
  
  
  
  
  
  
  
        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=__A   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int   )	-> List[Any]:
  
  
  
  
  
  
  
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=__A   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> List[Any]:
  
  
  
  
  
  
  
        for t in [0, 500, 800]:
          self.check_over_forward(time_step=__A   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Any:
        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
  
        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   )   )
  
        assert abs(result_sum.item() - 1_006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Any:
        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
  
        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   )   )
  
        assert abs(result_sum.item() - 0.0_017   ) < 1e-2
        assert abs(result_mean.item() - 2.2_6_7_6e-0_6   ) < 1e-3
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> int:
        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 * scheduler.init_noise_sigma.cpu()
        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   )   )
  
        assert abs(result_sum.item() - 1_006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Tuple:
        a__     		=     self.scheduler_classes[0]
        a__     		=     self.get_scheduler_config()
        a__     		=     scheduler_class(**__A      ,      use_karras_sigmas=__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
        a__     		=     sample.to(__A   )
  
        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   )   )
  
        assert abs(result_sum.item() - 3_812.9_927   ) < 2e-2
        assert abs(result_mean.item() - 4.9_648   ) < 1e-3
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  