

'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor



class lowerCamelCase (  unittest.TestCase						):



			'''simple docstring'''






			def __init__(      self       :						Dict		,			lowerCAmelCase_       :						Any		,			lowerCAmelCase_       :						List[str]=13		,			lowerCAmelCase_       :						List[Any]=3		,			lowerCAmelCase_       :						Tuple=2_24		,			lowerCAmelCase_       :						Dict=30		,			lowerCAmelCase_       :						Any=4_00		,			lowerCAmelCase_       :						Any=True		,			lowerCAmelCase_       :						Any=None		,			lowerCAmelCase_       :						str=True		,			lowerCAmelCase_       :						Optional[int]=[0.5, 0.5, 0.5]		,			lowerCAmelCase_       :						Optional[Any]=[0.5, 0.5, 0.5]		,			)			->		Optional[int]:





									'''simple docstring'''
									A__     :   Dict      =size if size is not None else {"""height""": 18, """width""": 18}
									A__     :   Any      =parent
									A__     :   int      =batch_size
									A__     :   str      =num_channels
									A__     :   int      =image_size
									A__     :   Optional[int]      =min_resolution
									A__     :   Any      =max_resolution
									A__     :   List[str]      =do_resize
									A__     :   List[str]      =size
									A__     :   Union[str, Any]      =do_normalize
									A__     :   Dict      =image_mean
									A__     :   Union[str, Any]      =image_std



			def lowercase__						(      self       :						Optional[int] )			->		List[Any]:





									'''simple docstring'''
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}



@require_torch
@require_vision
class lowerCamelCase (  lowercase_       ,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case    	=       ViTImageProcessor if is_vision_available() else None


			def lowercase__						(      self       :						List[str] )			->		Tuple:





									'''simple docstring'''
									A__     :   Any      =EfficientFormerImageProcessorTester(self )


			@property
			def lowercase__						(      self       :						int )			->		Optional[int]:





									'''simple docstring'''
									return self.image_proc_tester.prepare_image_processor_dict()


			def lowercase__						(      self       :						Dict )			->		Tuple:





									'''simple docstring'''
									A__     :   List[str]      =self.image_processing_class(**self.image_processor_dict )
									self.assertTrue(hasattr(__UpperCAmelCase		,			"""image_mean""" ) )
									self.assertTrue(hasattr(__UpperCAmelCase		,			"""image_std""" ) )
									self.assertTrue(hasattr(__UpperCAmelCase		,			"""do_normalize""" ) )
									self.assertTrue(hasattr(__UpperCAmelCase		,			"""do_resize""" ) )
									self.assertTrue(hasattr(__UpperCAmelCase		,			"""size""" ) )


			def lowercase__						(      self       :						Tuple )			->		int:





									'''simple docstring'''
									pass


			def lowercase__						(      self       :						List[str] )			->		Optional[int]:





									'''simple docstring'''
									A__     :   Tuple      =self.image_processing_class(**self.image_processor_dict )
									# create random PIL images
									A__     :   Dict      =prepare_image_inputs(self.image_proc_tester		,			equal_resolution=__UpperCAmelCase )
									for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase		,			Image.Image )

									# Test not batched input
									A__     :   List[Any]      =image_processor(image_inputs[0]		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)

									# Test batched
									A__     :   Dict      =image_processor(__UpperCAmelCase		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)


			def lowercase__						(      self       :						str )			->		str:





									'''simple docstring'''
									A__     :   Dict      =self.image_processing_class(**self.image_processor_dict )
									# create random numpy tensors
									A__     :   Union[str, Any]      =prepare_image_inputs(self.image_proc_tester		,			equal_resolution=__UpperCAmelCase		,			numpify=__UpperCAmelCase )
									for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase		,			np.ndarray )

									# Test not batched input
									A__     :   Optional[Any]      =image_processor(image_inputs[0]		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)

									# Test batched
									A__     :   str      =image_processor(__UpperCAmelCase		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)



			def lowercase__						(      self       :						Dict )			->		Any:





									'''simple docstring'''
									A__     :   Any      =self.image_processing_class(**self.image_processor_dict )
									# create random PyTorch tensors
									A__     :   List[Any]      =prepare_image_inputs(self.image_proc_tester		,			equal_resolution=__UpperCAmelCase		,			torchify=__UpperCAmelCase )
									for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase		,			torch.Tensor )

									# Test not batched input
									A__     :   Dict      =image_processor(image_inputs[0]		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)

									# Test batched
									A__     :   Tuple      =image_processor(__UpperCAmelCase		,			return_tensors="""pt""" ).pixel_values
									self.assertEqual(
									    encoded_images.shape		,			(
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )		,			)









from __future__ import annotations

import time

import numpy as np

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






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						list[int] ,					__UpperCAmelCase      :						list[list[int]] ,					__UpperCAmelCase      :						list[list[int]] ,					)    ->None:




       """simple docstring"""


       a		  =      claim_vector
       a		  =      allocated_resources_table
       a		  =      maximum_claim_table







      def       __lowerCAmelCase					(      self      :						Any  )    ->list[int]:




       """simple docstring"""


       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table  )
           for i in range(len(self.__allocated_resources_table[0]  )  )
       ]







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->list[int]:




       """simple docstring"""


       return np.array(self.__claim_vector  ) - np.array(
           self.__processes_resource_summation()  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->list[list[int]]:




       """simple docstring"""


       return [
           list(np.array(self.__maximum_claim_table[i]  ) - np.array(__UpperCAmelCase  )  )
           for i, allocated_resource in enumerate(self.__allocated_resources_table  )
       ]







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->dict[int, list[int]]:




       """simple docstring"""


       return {self.__need().index(__UpperCAmelCase  ): i for i in self.__need()}







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					**__UpperCAmelCase      :						Any  )    ->None:




       """simple docstring"""


       a		  =      self.__need()
       a		  =      self.__allocated_resources_table
       a		  =      self.__available_resources()
       a		  =      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:
        a		  =      False
        for each_need in need_list:
         a		  =      True
         for index, need in enumerate(__UpperCAmelCase  ):
          if need > available_resources[index]:
           a		  =      False
           break
         if execution:
          a		  =      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:
            a		  =      original_need_index
          print(F"""Process {process_number + 1} is executing."""  )
          # remove the process run from stack
          need_list.remove(__UpperCAmelCase  )
          # update available/freed resources stack
          a		  =      np.array(__UpperCAmelCase  ) + np.array(
              alloc_resources_table[process_number]  )
          print(
              '''Updated available resource stack for processes: '''
              + ''' '''.join([str(__UpperCAmelCase  ) 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       __lowerCAmelCase					(      self      :						List[Any]  )    ->Dict:




       """simple docstring"""


       print(''' ''' * 9 + '''Allocated Resource Table'''  )
       for item in self.__allocated_resources_table:
        print(
            F"""P{self.__allocated_resources_table.index(__UpperCAmelCase  ) + 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(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(
           '''Current Usage by Active Processes: '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__claim_vector  )  )
       print(
           '''Initial Available Resources:       '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__available_resources()  )  )
       time.sleep(1  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


