
"""simple docstring"""

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 				A		(							snake_case :List[Any]							,   snake_case :int						) ->      Any:
 __UpperCamelCase							    =			UNetaDModel(**snake_case						)
 __UpperCamelCase							    =			checkpoint['all_modules.0.W'].data
 __UpperCamelCase							    =			checkpoint['all_modules.0.W'].data
 __UpperCamelCase							    =			checkpoint['all_modules.1.weight'].data
 __UpperCamelCase							    =			checkpoint['all_modules.1.bias'].data

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

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

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

 __UpperCamelCase							    =			4

 def set_attention_weights(snake_case :Dict							,   snake_case :Tuple							,   snake_case :Optional[int]						):
  __UpperCamelCase							    =			old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
  __UpperCamelCase							    =			old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
  __UpperCamelCase							    =			old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

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

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

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

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

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

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

 for i, block in enumerate(new_model_architecture.downsample_blocks						):
  __UpperCamelCase							    =			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
   __UpperCamelCase							    =			checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
   __UpperCamelCase							    =			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						):
  __UpperCamelCase							    =			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:
   __UpperCamelCase							    =			checkpoint[f'all_modules.{module_index}.weight'].data
   __UpperCamelCase							    =			checkpoint[f'all_modules.{module_index}.bias'].data
   module_index += 1
   __UpperCamelCase							    =			checkpoint[f'all_modules.{module_index}.weight'].data
   __UpperCamelCase							    =			checkpoint[f'all_modules.{module_index}.bias'].data
   module_index += 1
   set_resnet_weights(block.resnet_up							,   snake_case							,   snake_case						)
   module_index += 1

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

 return new_model_architecture.state_dict()


if __name__ == "__main__":
      UpperCamelCase      :							Tuple          =					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.",
      )

      UpperCamelCase      :							Tuple          =					parser.parse_args()

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

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

      UpperCamelCase      :							Union[str, Any]          =					convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )

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

      UpperCamelCase      :							Dict          =					UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

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





"""simple docstring"""

import math
from itertools import permutations






def 				A		(							snake_case :int						) ->      bool:

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,   int(math.sqrt(snake_case						) + 1						)							,   6						):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 				A		(							snake_case :int							,   snake_case :list						) ->      bool:
 __UpperCamelCase    ,      __UpperCamelCase							    =			0, len(snake_case						) - 1
 while left <= right:
  __UpperCamelCase							    =			(left + right) // 2
  if prime_list[middle] == target:
   return True
  elif prime_list[middle] < target:
   __UpperCamelCase							    =			middle + 1
  else:
   __UpperCamelCase							    =			middle - 1

 return False






def 				A		(							) ->      int:
 __UpperCamelCase							    =			[n for n in range(1_0_0_1							,   1_0_0_0_0							,   2						) if is_prime(snake_case						)]
 __UpperCamelCase							    =			[]

 for number in prime_list:
  __UpperCamelCase							    =			[]

  for prime_member in permutations(list(str(snake_case						)						)						):
   __UpperCamelCase							    =			int(''.join(snake_case						)						)

   if prime % 2 == 0:
    continue

   if search(snake_case							,   snake_case						):
    tmp_numbers.append(snake_case						)

  tmp_numbers.sort()
  if len(snake_case						) >= 3:
   candidates.append(snake_case						)

 __UpperCamelCase							    =			[]
 for candidate in candidates:
  __UpperCamelCase							    =			len(snake_case						)
  __UpperCamelCase							    =			False

  for i in range(snake_case						):
   for j in range(i + 1							,   snake_case						):
    for k in range(j + 1							,   snake_case						):
     if (
         abs(candidate[i] - candidate[j]						)
         == abs(candidate[j] - candidate[k]						)
         and len({candidate[i], candidate[j], candidate[k]}						) == 3
     ):
      passed.append(
          sorted([candidate[i], candidate[j], candidate[k]]						)						)
      __UpperCamelCase							    =			True

     if found:
      break
    if found:
     break
   if found:
    break

 __UpperCamelCase							    =			set()
 for seq in passed:
  answer.add(''.join([str(snake_case						) for i in seq]						)						)

 return max(int(snake_case						) for x in answer						)


if __name__ == "__main__":
      print(solution())



