
'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase_							: Optional[Any] =							False
UpperCAmelCase_							: str =							True
UpperCAmelCase_							: Dict =							False


if __name__ == "__main__":
 UpperCAmelCase_							: Optional[Any] =							argparse.ArgumentParser()

 parser.add_argument(
     '--repo_path',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the architecture.',
 )

 parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

 UpperCAmelCase_							: Optional[int] =							parser.parse_args()

 UpperCAmelCase_							: str =							{
     'image_size': 'sample_size',
     'num_res_blocks': 'layers_per_block',
     'block_channels': 'block_out_channels',
     'down_blocks': 'down_block_types',
     'up_blocks': 'up_block_types',
     'downscale_freq_shift': 'freq_shift',
     'resnet_num_groups': 'norm_num_groups',
     'resnet_act_fn': 'act_fn',
     'resnet_eps': 'norm_eps',
     'num_head_channels': 'attention_head_dim',
 }

 UpperCAmelCase_							: List[Any] =							{
     'time_steps': 'time_proj',
     'mid': 'mid_block',
     'downsample_blocks': 'down_blocks',
     'upsample_blocks': 'up_blocks',
 }

 UpperCAmelCase_							: Optional[int] =							'' if has_file(args.repo_path, 'config.json') else 'unet'

 with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
  UpperCAmelCase_							: List[str] =							reader.read()
  UpperCAmelCase_							: List[str] =							json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, 'config.json'):
  UpperCAmelCase_							: Dict =							UNetaDModel(**config)
 else:
  UpperCAmelCase_							: Union[str, Any] =							UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
  UpperCAmelCase_							: str =							class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 UpperCAmelCase_							: Optional[int] =							dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    UpperCAmelCase_							: Optional[int] =							config[key]
    del config[key]

  UpperCAmelCase_							: List[str] =							[k.replace('UNetRes', '') for k in config['down_block_types']]
  UpperCAmelCase_							: Union[str, Any] =							[k.replace('UNetRes', '') for k in config['up_block_types']]

 if do_only_weights:
  UpperCAmelCase_							: Optional[int] =							torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

  UpperCAmelCase_							: List[str] =							{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
    continue
   UpperCAmelCase_							: Optional[Any] =							False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('.')[0] == key:
     UpperCAmelCase_							: Optional[int] =							param_value
     UpperCAmelCase_							: List[Any] =							True
   if not has_changed:
    UpperCAmelCase_							: Tuple =							param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))







'''simple docstring'''



from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__UpperCamelCase   :							Optional[int]         =   Lock()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      int:

   """simple docstring"""
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0,       10     ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         __a					  =     rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         __a					  =     min(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         __a					  =     lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         __a					  =     max(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     []
   __a					  =     []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()     )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   __a					  =     Pipe()
   __a					  =     Pipe()
   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),       )     )
   __a					  =     temp_rs
   __a					  =     temp_rr

   for i in range(1,       len(SCREAMING_SNAKE_CASE__     ) - 1     ):
      __a					  =     Pipe()
      __a					  =     Pipe()
      process_array_.append(
          Process(
              target=SCREAMING_SNAKE_CASE__,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),       )     )
      __a					  =     temp_rs
      __a					  =     temp_rr

   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(
               len(SCREAMING_SNAKE_CASE__     ) - 1,
               arr[len(SCREAMING_SNAKE_CASE__     ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(SCREAMING_SNAKE_CASE__     ) - 1],
           ),       )     )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0,       len(SCREAMING_SNAKE_CASE__     )     ):
      __a					  =     result_pipe[p][0].recv()
      process_array_[p].join()
   return arr



def  __UpperCAmelCase						(					)	->      Any:

   """simple docstring"""
   __a					  =     list(range(10,       0,       -1     )     )
   print('Initial List'     )
   print(*SCREAMING_SNAKE_CASE__     )
   __a					  =     odd_even_transposition(SCREAMING_SNAKE_CASE__     )
   print('Sorted List\n'     )
   print(*SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()