

import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
       from PIL import Image
else:



       class       __snake_case  :

         @staticmethod
         def 						lowerCAmelCase__     (					*_A						,		**_A):
             pass


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Image       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						hashlib.mda(image.tobytes()       )
    return m.hexdigest()[:10]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Image       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						np.array(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						npimg.shape
    return {"hash": hashimage(_SCREAMING_SNAKE_CASE       ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       __snake_case  (    unittest.TestCase       ):
  __lowerCAmelCase     :	Dict						 =  dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
  __lowerCAmelCase     :	int						 =  dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						MaskGenerationPipeline(model=_A						,		image_processor=_A)
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      pass

  @require_tf
  @unittest.skip('Image segmentation not implemented in TF')
  def 						lowerCAmelCase__     (					self):
      pass

  @slow
  @require_torch
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						pipeline('mask-generation'						,		model='facebook/sam-vit-huge')

      SCREAMING_SNAKE_CASE_									=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'						,		points_per_batch=256)

      # Shortening by hashing
      SCREAMING_SNAKE_CASE_									=						[]
      for i, o in enumerate(outputs['masks']):
          new_outupt += [{"mask": mask_to_test_readable(_A), "scores": outputs["scores"][i]}]

      # fmt: off
      self.assertEqual(
          nested_simplify(_A						,		decimals=4)						,		[
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
              {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
              {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
              {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
              {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
              {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
              {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
              {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
              {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
              {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
              {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
              {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
              {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
              {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
              {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
              {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
              {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
              {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
              {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
              {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
              {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
              {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
              {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
              {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
              {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
          ]						,		)
      # fmt: on


  @require_torch
  @slow
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'facebook/sam-vit-huge'
      SCREAMING_SNAKE_CASE_									=						pipeline('mask-generation'						,		model=_A)

      SCREAMING_SNAKE_CASE_									=						image_segmenter(
          'http://images.cocodataset.org/val2017/000000039769.jpg'						,		pred_iou_thresh=1						,		points_per_batch=256)

      # Shortening by hashing
      SCREAMING_SNAKE_CASE_									=						[]
      for i, o in enumerate(outputs['masks']):
          new_outupt += [{"mask": mask_to_test_readable(_A), "scores": outputs["scores"][i]}]

      self.assertEqual(
          nested_simplify(_A						,		decimals=4)						,		[
              {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
              {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
              {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
              {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
              {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
          ]						,		)





from multiprocessing import Lock, Pipe, Process

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """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()
            SCREAMING_SNAKE_CASE_									=						rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            SCREAMING_SNAKE_CASE_									=						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()
            SCREAMING_SNAKE_CASE_									=						lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            SCREAMING_SNAKE_CASE_									=						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					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    # 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
    SCREAMING_SNAKE_CASE_									=						Pipe()
    SCREAMING_SNAKE_CASE_									=						Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
    SCREAMING_SNAKE_CASE_									=						temp_rs
    SCREAMING_SNAKE_CASE_									=						temp_rr

    for i in range(1 ,   len(_SCREAMING_SNAKE_CASE       ) - 1       ):
        SCREAMING_SNAKE_CASE_									=						Pipe()
        SCREAMING_SNAKE_CASE_									=						Pipe()
        process_array_.append(
            Process(
                target=_SCREAMING_SNAKE_CASE ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,   )       )
        SCREAMING_SNAKE_CASE_									=						temp_rs
        SCREAMING_SNAKE_CASE_									=						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       )       ):
        SCREAMING_SNAKE_CASE_									=						result_pipe[p][0].recv()
        process_array_[p].join()
    return arr


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						list(range(10 ,   0 ,   -1       )       )
    print('Initial List'       )
    print(*_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						odd_even_transposition(_SCREAMING_SNAKE_CASE       )
    print('Sorted List\n'       )
    print(*_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()


