






from __future__ import annotations

import time

import numpy as np

a_		:						Union[str, Any]             =    [8, 5, 9, 7]
a_		:						Tuple             =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a_		:						Tuple             =    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					)      ->       None:
						SCREAMING_SNAKE_CASE						= claim_vector
						SCREAMING_SNAKE_CASE						= allocated_resources_table
						SCREAMING_SNAKE_CASE						= maximum_claim_table






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






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






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






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


if __name__ == "__main__":
				import doctest

				doctest.testmod()












from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


a_		:						Optional[int]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= b.T
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=1)
			SCREAMING_SNAKE_CASE						= np.sum(np.square(_UpperCAmelCase)							,       axis=0)
			SCREAMING_SNAKE_CASE						= np.matmul(_UpperCAmelCase							,       _UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= aa[:, None] - 2 * ab + ba[None, :]
			return d






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= x.reshape(-1							,       3)
			SCREAMING_SNAKE_CASE						= squared_euclidean_distance(_UpperCAmelCase							,       _UpperCAmelCase)
			return np.argmin(_UpperCAmelCase							,       axis=1)







class 		_snake_case  (  A__					):
			_lowercase		:    str			     =							['''pixel_values''']






			def __init__(       self		,					a = None		,					a = True		,					a = None		,					a = PILImageResampling.BILINEAR		,					a = True		,					a = True		,					**a		,					)      ->       None:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE						= size if size is not None else {'height': 256, 'width': 256}
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= np.array(a) if clusters is not None else None
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= resample
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = PILImageResampling.BILINEAR		,					a = None		,					**a		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						if "height" not in size or "width" not in size:
									raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''')
						return resize(
						    a		,					size=(size['height'], size['width'])		,					resample=a		,					data_format=a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					)      ->       np.ndarray:
						SCREAMING_SNAKE_CASE						= rescale(image=a		,					scale=1 / 1_27.5		,					data_format=a)
						SCREAMING_SNAKE_CASE						= image - 1
						return image







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = None		,					a = ChannelDimension.FIRST		,					**a		,					)      ->       PIL.Image.Image:
						SCREAMING_SNAKE_CASE						= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE						= size if size is not None else self.size
						SCREAMING_SNAKE_CASE						= get_size_dict(a)
						SCREAMING_SNAKE_CASE						= resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE						= do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE						= do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						SCREAMING_SNAKE_CASE						= clusters if clusters is not None else self.clusters
						SCREAMING_SNAKE_CASE						= np.array(a)

						SCREAMING_SNAKE_CASE						= make_list_of_images(a)

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

						if do_resize and size is None or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

						if do_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

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

						if do_resize:
									SCREAMING_SNAKE_CASE						= [self.resize(image=a		,					size=a		,					resample=a) for image in images]

						if do_normalize:
									SCREAMING_SNAKE_CASE						= [self.normalize(image=a) for image in images]

						if do_color_quantize:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									SCREAMING_SNAKE_CASE						= np.array(a)
									SCREAMING_SNAKE_CASE						= color_quantize(a		,					a).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									SCREAMING_SNAKE_CASE						= images.shape[0]
									SCREAMING_SNAKE_CASE						= images.reshape(a		,					-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									SCREAMING_SNAKE_CASE						= list(a)
						else:
									SCREAMING_SNAKE_CASE						= [to_channel_dimension_format(a		,					a) for image in images]

						SCREAMING_SNAKE_CASE						= {'input_ids': images}
						return BatchFeature(data=a		,					tensor_type=a)




