


from __future__ import annotations

import time

import numpy as np

a					  =     [8, 5, 9, 7]
a					  =     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a					  =     [
    [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       :   Optional[int]							,					UpperCamelCase__       :   list[int]							,					UpperCamelCase__       :   list[list[int]]							,					UpperCamelCase__       :   list[list[int]]							,					):






										'''simple docstring'''






										lowercase_  							=			claim_vector
										lowercase_  							=			allocated_resources_table
										lowercase_  							=			maximum_claim_table

					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''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   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''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   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					**UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.__need()
										lowercase_  							=			self.__allocated_resources_table
										lowercase_  							=			self.__available_resources()
										lowercase_  							=			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:
															lowercase_  							=			False
															for each_need in need_list:
																				lowercase_  							=			True
																				for index, need in enumerate(UpperCamelCase__       ):
																									if need > available_resources[index]:
																														lowercase_  							=			False
																														break
																				if execution:
																									lowercase_  							=			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:
																																			lowercase_  							=			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
																									lowercase_  							=			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   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''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()






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

					def __init__(     self       :   List[Any]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   int = 8							,					**UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										return rescale(UpperCamelCase__							,					scale=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None       ):






										'''simple docstring'''






										lowercase_							,					lowercase_  							=			get_image_size(UpperCamelCase__       )
										lowercase_  							=			(old_height // size + 1) * size - old_height
										lowercase_  							=			(old_width // size + 1) * size - old_width

										return pad(UpperCamelCase__							,					((0, pad_height), (0, pad_width))							,					mode="""symmetric"""							,					data_format=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_pad if do_pad is not None else self.do_pad
										lowercase_  							=			pad_size if pad_size is not None else self.pad_size

										lowercase_  							=			make_list_of_images(UpperCamelCase__       )

										if not valid_images(UpperCamelCase__       ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

										if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

										# All transformations expect numpy arrays.
										lowercase_  							=			[to_numpy_array(UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(image=UpperCamelCase__							,					scale=UpperCamelCase__       ) for image in images]

										if do_pad:
															lowercase_  							=			[self.pad(UpperCamelCase__							,					size=UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )


