"""simple docstring"""


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  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							)      ->      None:
            snake_case_					       =					claim_vector
            snake_case_					       =					allocated_resources_table
            snake_case_					       =					maximum_claim_table







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







       def 		a_						( self)      ->      list[list[int]]:
            return [
                list(np.array(self.__maximum_claim_table[i]) - np.array(lowerCAmelCase__))
                for i, allocated_resource in enumerate(self.__allocated_resources_table)
            ]







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







       def 		a_						( self,							**lowerCAmelCase__)      ->      None:
            snake_case_					       =					self.__need()
            snake_case_					       =					self.__allocated_resources_table
            snake_case_					       =					self.__available_resources()
            snake_case_					       =					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:
                 snake_case_					       =					False
                 for each_need in need_list:
                      snake_case_					       =					True
                      for index, need in enumerate(lowerCAmelCase__):
                           if need > available_resources[index]:
                                snake_case_					       =					False
                                break
                      if execution:
                           snake_case_					       =					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:
                                     snake_case_					       =					original_need_index
                           print(f'Process {process_number + 1} is executing.')
                           # remove the process run from stack
                           need_list.remove(lowerCAmelCase__)
                           # update available/freed resources stack
                           snake_case_					       =					np.array(lowerCAmelCase__) + np.array(
                               alloc_resources_table[process_number])
                           print(
                               'Updated available resource stack for processes: '
                               + ' '.join([str(lowerCAmelCase__) 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 		a_						( self)      ->      List[Any]:
            print(' ' * 9 + 'Allocated Resource Table')
            for item in self.__allocated_resources_table:
                 print(
                     f'P{self.__allocated_resources_table.index(lowerCAmelCase__) + 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(lowerCAmelCase__) + 1}'
                     + ' '.join(f'{it:>8}' for it in item)
                     + '\n')
            print(
                'Current Usage by Active Processes: '
                + ' '.join(str(lowerCAmelCase__) for x in self.__claim_vector))
            print(
                'Initial Available Resources:       '
                + ' '.join(str(lowerCAmelCase__) for x in self.__available_resources()))
            time.sleep(1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()








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 GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
