






'''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__			=      Lock()




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """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(_UpperCamelCase				)
         process_lock.release()

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

         # take the lower value since you are on the left
         lowercase_						:     Any							     =  min(_UpperCamelCase    ,		_UpperCamelCase				)
      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(_UpperCamelCase				)
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         lowercase_						:     Union[str, Any]							     =  lr_cv[0].recv()
         process_lock.release()

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     str							     =  []
   lowercase_						:     Dict							     =  []
   # 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
   lowercase_						:     str							     =  Pipe()
   lowercase_						:     Any							     =  Pipe()
   process_array_.append(
       Process(
           target=_UpperCamelCase    ,		args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])    ,		)				)
   lowercase_						:     str							     =  temp_rs
   lowercase_						:     str							     =  temp_rr

   for i in range(1    ,		len(_UpperCamelCase				) - 1				):
      lowercase_						:     Optional[int]							     =  Pipe()
      lowercase_						:     List[str]							     =  Pipe()
      process_array_.append(
          Process(
              target=_UpperCamelCase    ,		args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])    ,		)				)
      lowercase_						:     Optional[int]							     =  temp_rs
      lowercase_						:     Optional[int]							     =  temp_rr

   process_array_.append(
       Process(
           target=_UpperCamelCase    ,		args=(
               len(_UpperCamelCase				) - 1,
               arr[len(_UpperCamelCase				) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(_UpperCamelCase				) - 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(_UpperCamelCase				)				):
      lowercase_						:     Optional[int]							     =  result_pipe[p][0].recv()
      process_array_[p].join()
   return arr




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Any							     =  list(range(10    ,		0    ,		-1				)				)
   print("Initial List"				)
   print(*_UpperCamelCase				)
   lowercase_						:     Any							     =  odd_even_transposition(_UpperCamelCase				)
   print("Sorted List\n"				)
   print(*_UpperCamelCase				)


if __name__ == "__main__":
 main()












'''simple docstring'''
import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 							_UpperCAmelCase    (		unittest.TestCase      ):




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"      )

       # using the PNDM scheduler by default
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)
       assert np.abs(expected_image - image      ).max() < 1e-2




