import unittest
from timeit import timeit


def 							lowerCAmelCase__    (       a__  ,			a__      )	->int:




  '''simple docstring'''

  _UpperCamelCase				=  first_num if first_num >= second_num else second_num
  _UpperCamelCase				=  max_num
  while (common_mult % first_num > 0) or (common_mult % second_num > 0):
    common_mult += max_num
  return common_mult


def 							lowerCAmelCase__    (       a__  ,			a__      )	->int:




  '''simple docstring'''

  return b if a == 0 else greatest_common_divisor(b % a  ,			a__      )


def 							lowerCAmelCase__    (       a__  ,			a__      )	->int:




  '''simple docstring'''

  return first_num // greatest_common_divisor(a__  ,			a__      ) * second_num


def 							lowerCAmelCase__    (       )	->Dict:




  '''simple docstring'''

  _UpperCamelCase				=  (
      "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
  )
  print(
      "least_common_multiple_slow():"  ,			timeit("least_common_multiple_slow(1000, 999)"  ,			setup=a__      )  ,			)
  print(
      "least_common_multiple_fast():"  ,			timeit("least_common_multiple_fast(1000, 999)"  ,			setup=a__      )  ,			)




class  _UpperCAmelCase       (			unittest.TestCase   ):



 '''simple docstring'''




 __A           = (
     (10, 20),
     (13, 15),
     (4, 31),
     (10, 42),
     (43, 34),
     (5, 12),
     (12, 25),
     (10, 25),
     (6, 9),
 )
 __A           = (20, 195, 124, 210, 1_462, 60, 300, 50, 18)







 def 						__UpperCAmelCase							(						self							:					int)		->     List[Any]:


   """simple docstring"""







   for i, (first_num, second_num) in enumerate(self.test_inputs):
     _UpperCamelCase				=  least_common_multiple_slow(a__				,      a__)
     _UpperCamelCase				=  least_common_multiple_fast(a__				,      a__)
     with self.subTest(i=a__):
       self.assertEqual(a__				,      self.expected_results[i])
       self.assertEqual(a__				,      self.expected_results[i])


if __name__ == "__main__":
   benchmark()
   unittest.main()











"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Tuple						,  snake_case   :					List[Any]		)->		str:
       _lowerCamelCase    	=					UNetaDModel(**snake_case		)
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.0.W'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.1.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.2.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.2.bias'].data

       _lowerCamelCase    	=					checkpoint['all_modules.3.weight'].data
       _lowerCamelCase    	=					checkpoint['all_modules.3.bias'].data

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

       _lowerCamelCase    	=					4

       def set_attention_weights(snake_case   :					List[Any]						,  snake_case   :					Any						,  snake_case   :					List[Any]		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

       def set_resnet_weights(snake_case   :					Tuple						,  snake_case   :					Optional[int]						,  snake_case   :					Tuple		):
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

              _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
              _lowerCamelCase    	=					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:
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
                     _lowerCamelCase    	=					old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

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

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

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

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

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

       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data
       module_index += 1
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.weight'].data
       _lowerCamelCase    	=					checkpoint[f'all_modules.{module_index}.bias'].data

       return new_model_architecture.state_dict()


if __name__ == "__main__":
      A_     :      Any								=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.""",
      )

      A_     :      int								=parser.parse_args()

      A_     :      Dict								=torch.load(args.checkpoint_path, map_location="""cpu""")

      with open(args.config_file) as f:
            A_     :      List[str]								=json.loads(f.read())

      A_     :      str								=convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      A_     :      str								=UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

            A_     :      Optional[Any]								=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)






