



'''simple docstring'''
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 							lowercase__     :





						@staticmethod
						def      UpperCamelCase_  (			*lowerCamelCase__ :    Optional[Any]			,**lowerCamelCase__ :    Any			):







												'''simple docstring'''
												pass






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		str			    =       hashlib.mda(image.tobytes()							)
						return m.hexdigest()[:1_0]






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       np.array(UpperCAmelCase_							)
						_UpperCamelCase :		Optional[int]			    =       npimg.shape
						return {"hash": hashimage(UpperCAmelCase_							), "shape": shape}






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





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       MaskGenerationPipeline(model=lowerCamelCase__			,image_processor=lowerCamelCase__			)
							return image_segmenter, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    int			,lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							pass





	@require_tf
	@unittest.skip('Image segmentation not implemented in TF'			)
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							pass





	@slow
	@require_torch
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       pipeline('mask-generation'			,model='facebook/sam-vit-huge'			)

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

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

							# fmt: off
							self.assertEqual(
							    nested_simplify(lowerCamelCase__			,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      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       'facebook/sam-vit-huge'
							_UpperCamelCase :		Dict			    =       pipeline('mask-generation'			,model=lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       image_segmenter(
							    'http://images.cocodataset.org/val2017/000000039769.jpg'			,pred_iou_thresh=1			,points_per_batch=256			)

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

							self.assertEqual(
							    nested_simplify(lowerCamelCase__			,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},
							    ]			,)







'''simple docstring'''
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						_UpperCamelCase :		str			    =       (low + high) // 2
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Dict			    =       max_subarray(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       max_subarray(UpperCAmelCase_  ,  mid + 1  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       max_cross_sum(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       float('-inf'							), -1
						_UpperCamelCase    ,      _UpperCamelCase :		int			    =       float('-inf'							), -1

						_UpperCamelCase :		int | float			    =       0
						for i in range(UpperCAmelCase_  ,  low - 1  ,  -1							):
												summ += arr[i]
												if summ > left_sum:
																		_UpperCamelCase :		Optional[int]			    =       summ
																		_UpperCamelCase :		Union[str, Any]			    =       i

						_UpperCamelCase :		List[Any]			    =       0
						for i in range(mid + 1  ,  high + 1							):
												summ += arr[i]
												if summ > right_sum:
																		_UpperCamelCase :		List[Any]			    =       summ
																		_UpperCamelCase :		List[str]			    =       i

						return max_left, max_right, (left_sum + right_sum)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       [randint(1  ,  UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)]
						_UpperCamelCase :		Optional[Any]			    =       time.time()
						max_subarray(UpperCAmelCase_  ,  0  ,  input_size - 1							)
						_UpperCamelCase :		str			    =       time.time()
						return end - start






def A__   (					):
						_UpperCamelCase :		Any			    =       [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
						_UpperCamelCase :		Dict			    =       [time_max_subarray(UpperCAmelCase_							) for input_size in input_sizes]
						print('No of Inputs\t\tTime Taken'							)
						for input_size, runtime in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												print(UpperCAmelCase_  ,  '\t\t'  ,  UpperCAmelCase_							)
						plt.plot(UpperCAmelCase_  ,  UpperCAmelCase_							)
						plt.xlabel('Number of Inputs'							)
						plt.ylabel('Time taken in seconds'							)
						plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()


