






"""simple docstring"""




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case          =   False
_snake_case          =   True
_snake_case          =   False


if __name__ == "__main__":
 _snake_case          =   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.')

 _snake_case          =   parser.parse_args()

 _snake_case          =   {
     '''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''',
 }

 _snake_case          =   {
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 _snake_case          =   '''''' 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:
  _snake_case          =   reader.read()
  _snake_case          =   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'):
  _snake_case          =   UNetaDModel(**config)
 else:
  _snake_case          =   UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  _snake_case          =   class_name(**config)

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

 _snake_case          =   dict(model.config)

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

  _snake_case          =   [k.replace('UNetRes', '') for k in config['''down_block_types''']]
  _snake_case          =   [k.replace('UNetRes', '') for k in config['''up_block_types''']]

 if do_only_weights:
  _snake_case          =   torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

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

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








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A    :					Dict =      Lock()







def    __lowerCamelCase					(							__a   :Dict    ,     __a   :List[str]    ,     __a   :Optional[int]    ,     __a   :Optional[int]    ,     __a   :Optional[Any]    ,     __a   :Optional[int]    ,     __a   :int    )		->				Dict:






		"""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    ,     1_0    ):
				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(__a    )
						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(__a    ,     __a    )
				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(__a    )
						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(__a    ,     __a    )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(__a    )







def    __lowerCamelCase					(							__a   :List[str]    )		->				int:






		"""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=__a    ,     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(__a    ) - 1    ):
				A__        =   Pipe()
				A__        =   Pipe()
				process_array_.append(
				    Process(
				        target=__a    ,     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=__a    ,     args=(
		            len(__a    ) - 1,
		            arr[len(__a    ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(__a    ) - 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(__a    )    ):
				A__        =   result_pipe[p][0].recv()
				process_array_[p].join()
		return arr







def    __lowerCamelCase					(							)		->				str:






		"""simple docstring"""

		A__        =   list(range(1_0    ,     0    ,     -1    )    )
		print("""Initial List"""    )
		print(*__a    )
		A__        =   odd_even_transposition(__a    )
		print("""Sorted List\n"""    )
		print(*__a    )


if __name__ == "__main__":
					main()



