



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     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     str					,						**snake_case_				:     Tuple)->				List[str]:
										pass






def        __lowerCAmelCase		(	__lowerCamelCase						:     Image						)	->    str:
					__lowerCAmelCase						 =hashlib.mda(image.tobytes()						)
					return m.hexdigest()[:10]






def        __lowerCAmelCase		(	__lowerCamelCase						:     Image						)	->    Dict:
					__lowerCAmelCase						 =np.array(__lowerCamelCase						)
					__lowerCAmelCase						 =npimg.shape
					return {"hash": hashimage(__lowerCamelCase						), "shape": shape}






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



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     Any					,						snake_case_				:     Any					,						snake_case_				:     List[str])->				Dict:
						__lowerCAmelCase						 =MaskGenerationPipeline(model=snake_case_					,						image_processor=snake_case_)
						return image_segmenter, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     Tuple					,						snake_case_				:     Dict)->				Optional[Any]:
						pass



	@require_tf
	@unittest.skip("""Image segmentation not implemented in TF""")
	def 		UpperCamelCase				(    self				:     Tuple)->				List[str]:
						pass



	@slow
	@require_torch
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Any:
						__lowerCAmelCase						 =pipeline("""mask-generation"""					,						model="""facebook/sam-vit-huge""")

						__lowerCAmelCase						 =image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,						points_per_batch=2_56)

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

						# fmt: off
						self.assertEqual(
						    nested_simplify(snake_case_					,						decimals=4)					,						[
						        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
						        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1},
						        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
						        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
						        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
						        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_6_7},
						        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_3},
						        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_0_9},
						        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_7_9},
						        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_3_4},
						        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9_7_1_6},
						        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9_6_1_2},
						        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_9_9},
						        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_5_2},
						        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_3_2},
						        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_1_6},
						        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_9_9},
						        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_8_3},
						        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_6_4},
						        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
						        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
						        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_0_8},
						        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_3_5},
						        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_2_6},
						        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9_2_6_2},
						        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_9_9},
						        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_6},
						        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_4},
						        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_3},
						        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_1}
						    ]					,						)
						# fmt: on



	@require_torch
	@slow
	def 		UpperCamelCase				(    self				:     Tuple)->				Tuple:
						__lowerCAmelCase						 ="""facebook/sam-vit-huge"""
						__lowerCAmelCase						 =pipeline("""mask-generation"""					,						model=snake_case_)

						__lowerCAmelCase						 =image_segmenter(
						    """http://images.cocodataset.org/val2017/000000039769.jpg"""					,						pred_iou_thresh=1					,						points_per_batch=2_56)

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

						self.assertEqual(
						    nested_simplify(snake_case_					,						decimals=4)					,						[
						        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
						        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1_0},
						        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
						        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
						        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
						    ]					,						)









from __future__ import annotations

import time

import numpy as np

lowercase_													=      [8, 5, 9, 7]
lowercase_													=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowercase_													=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



	def 		UpperCamelCase				(    self				:     Optional[int])->				list[int]:
						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]



	def 		UpperCamelCase				(    self				:     Dict)->				list[int]:
						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



	def 		UpperCamelCase				(    self				:     Dict)->				dict[int, list[int]]:
						return {self.__need().index(snake_case_): i for i in self.__need()}



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				None:
						__lowerCAmelCase						 =self.__need()
						__lowerCAmelCase						 =self.__allocated_resources_table
						__lowerCAmelCase						 =self.__available_resources()
						__lowerCAmelCase						 =self.__need_index_manager()
						for kw, val in kwargs.items():
											if kw and val is True:
																self.__pretty_data()
						print("""_""" * 50 + """\n""")
						while need_list:
											__lowerCAmelCase						 =False
											for each_need in need_list:
																__lowerCAmelCase						 =True
																for index, need in enumerate(snake_case_):
																					if need > available_resources[index]:
																										__lowerCAmelCase						 =False
																										break
																if execution:
																					__lowerCAmelCase						 =True
																					# get the original index of the process from ind_ctrl db
																					for original_need_index, need_clone in need_index_manager.items():
																										if each_need == need_clone:
																															__lowerCAmelCase						 =original_need_index
																					print(F"""Process {process_number + 1} is executing.""")
																					# remove the process run from stack
																					need_list.remove(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) for x in available_resources]))
																					break
											if safe:
																print("""The process is in a safe state.\n""")
											else:
																print("""System in unsafe state. Aborting...\n""")
																break



	def 		UpperCamelCase				(    self				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(""" """ * 9 + """System Resource Table""")
						for item in self.__maximum_claim_table:
											print(
											    F"""P{self.__maximum_claim_table.index(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




