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)


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:

  '''simple docstring'''




  a__     		=     UNetaDModel(**lowerCamelCase__					)
  a__     		=     checkpoint["all_modules.0.W"].data
  a__     		=     checkpoint["all_modules.0.W"].data
  a__     		=     checkpoint["all_modules.1.weight"].data
  a__     		=     checkpoint["all_modules.1.bias"].data

  a__     		=     checkpoint["all_modules.2.weight"].data
  a__     		=     checkpoint["all_modules.2.bias"].data

  a__     		=     checkpoint["all_modules.3.weight"].data
  a__     		=     checkpoint["all_modules.3.bias"].data

  a__     		=     checkpoint[list(checkpoint.keys()					)[-4]].data
  a__     		=     checkpoint[list(checkpoint.keys()					)[-3]].data
  a__     		=     checkpoint[list(checkpoint.keys()					)[-2]].data
  a__     		=     checkpoint[list(checkpoint.keys()					)[-1]].data

  a__     		=     4

  def set_attention_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
    a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

  def set_resnet_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

    a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
    a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

    if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

  for i, block in enumerate(new_model_architecture.downsample_blocks					):
    a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
    for j in range(len(block.resnets					)					):
      set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1

    if hasattr(lowerCamelCase__ ,				"downsamplers"					) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1
      a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
      module_index += 1

  set_resnet_weights(new_model_architecture.mid_block.resnets[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1] ,				lowerCamelCase__ ,				lowerCamelCase__					)
  module_index += 1

  for i, block in enumerate(new_model_architecture.up_blocks					):
    a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
    for j in range(len(block.resnets					)					):
      set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1
    if has_attentions:
      set_attention_weights(
          block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)  # why can there only be a single attention layer for up?
      module_index += 1

    if hasattr(lowerCamelCase__ ,				"resnet_up"					) and block.resnet_up is not None:
      a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
      a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
      module_index += 1
      set_resnet_weights(block.resnet_up ,				lowerCamelCase__ ,				lowerCamelCase__					)
      module_index += 1

  a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
  a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
  module_index += 1
  a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
  a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()

     parser.add_argument(
         """--checkpoint_path""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
         type=str,
         required=False,
         help="""Path to the checkpoint to convert.""",
     )

     parser.add_argument(
         """--config_file""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
         type=str,
         required=False,
         help="""The config json file corresponding to the architecture.""",
     )

     parser.add_argument(
         """--dump_path""",
         default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
         type=str,
         required=False,
         help="""Path to the output model.""",
     )

     _SCREAMING_SNAKE_CASE   						=    parser.parse_args()

     _SCREAMING_SNAKE_CASE   						=    torch.load(args.checkpoint_path, map_location="""cpu""")

     with open(args.config_file) as f:
          _SCREAMING_SNAKE_CASE   						=    json.loads(f.read())

     _SCREAMING_SNAKE_CASE   						=    convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

     if "sde" in config:
          del config["sde"]

     _SCREAMING_SNAKE_CASE   						=    UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          _SCREAMING_SNAKE_CASE   						=    ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

          _SCREAMING_SNAKE_CASE   						=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)

