






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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    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


UpperCAmelCase__				   =		logging.get_logger(__name__)






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  ['''pixel_values''']







      def __init__(      self      :						str ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						int = 0.9 ,					__UpperCAmelCase      :						PILImageResampling = PILImageResampling.BICUBIC ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						Union[int, float] = 1 / 255 ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Optional[Union[float, List[float]]] = None ,					__UpperCAmelCase      :						Optional[Union[float, List[float]]] = None ,					**__UpperCAmelCase      :						str ,					)    ->None:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )
       a		  =      size if size is not None else {'''shortest_edge''': 224}
       a		  =      get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase  )
       a		  =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       a		  =      get_size_dict(__UpperCAmelCase ,					param_name='''crop_size'''  )

       a		  =      do_resize
       a		  =      size
       a		  =      crop_pct
       a		  =      resample
       a		  =      do_center_crop
       a		  =      crop_size
       a		  =      do_rescale
       a		  =      rescale_factor
       a		  =      do_normalize
       a		  =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       a		  =      image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						np.ndarray ,					__UpperCAmelCase      :						Dict[str, int] ,					__UpperCAmelCase      :						Optional[float] = None ,					__UpperCAmelCase      :						PILImageResampling = PILImageResampling.BICUBIC ,					__UpperCAmelCase      :						Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase      :						List[Any] ,					)    ->np.ndarray:




       """simple docstring"""


       a		  =      get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase  )
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
        raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
       if crop_pct is not None:
        if "shortest_edge" in size:
         a		  =      int(size['''shortest_edge'''] / crop_pct  )
        elif "height" in size and "width" in size:
         if size["height"] == size["width"]:
          a		  =      int(size['''height'''] / crop_pct  )
         else:
          a		  =      (int(size['''height'''] / crop_pct  ), int(size['''width'''] / crop_pct  ))
        else:
         raise ValueError('''Invalid size for resize: {}'''.format(__UpperCAmelCase  )  )

        a		  =      get_resize_output_image_size(__UpperCAmelCase ,					size=__UpperCAmelCase ,					default_to_square=__UpperCAmelCase  )
       else:
        if "shortest_edge" in size:
         a		  =      get_resize_output_image_size(__UpperCAmelCase ,					size=size['''shortest_edge'''] ,					default_to_square=__UpperCAmelCase  )
        elif "height" in size and "width" in size:
         a		  =      (size['''height'''], size['''width'''])
        else:
         raise ValueError('''Invalid size for resize: {}'''.format(__UpperCAmelCase  )  )

       return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						np.ndarray ,					__UpperCAmelCase      :						Dict[str, int] ,					__UpperCAmelCase      :						Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase      :						Any ,					)    ->np.ndarray:




       """simple docstring"""


       a		  =      get_size_dict(__UpperCAmelCase  )
       if "height" not in size or "width" not in size:
        raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""  )
       return center_crop(__UpperCAmelCase ,					size=(size['''height'''], size['''width''']) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						np.ndarray ,					__UpperCAmelCase      :						Union[int, float] ,					__UpperCAmelCase      :						Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase      :						Tuple ,					)    ->Tuple:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						np.ndarray ,					__UpperCAmelCase      :						Union[float, List[float]] ,					__UpperCAmelCase      :						Union[float, List[float]] ,					__UpperCAmelCase      :						Optional[Union[str, ChannelDimension]] = None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->np.ndarray:




       """simple docstring"""


       return normalize(__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						ImageInput ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						int = None ,					__UpperCAmelCase      :						PILImageResampling = None ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						Dict[str, int] = None ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						float = None ,					__UpperCAmelCase      :						bool = None ,					__UpperCAmelCase      :						Optional[Union[float, List[float]]] = None ,					__UpperCAmelCase      :						Optional[Union[float, List[float]]] = None ,					__UpperCAmelCase      :						Optional[Union[str, TensorType]] = None ,					__UpperCAmelCase      :						ChannelDimension = ChannelDimension.FIRST ,					**__UpperCAmelCase      :						Tuple ,					)    ->PIL.Image.Image:




       """simple docstring"""


       a		  =      do_resize if do_resize is not None else self.do_resize
       a		  =      crop_pct if crop_pct is not None else self.crop_pct
       a		  =      resample if resample is not None else self.resample
       a		  =      do_center_crop if do_center_crop is not None else self.do_center_crop
       a		  =      do_rescale if do_rescale is not None else self.do_rescale
       a		  =      rescale_factor if rescale_factor is not None else self.rescale_factor
       a		  =      do_normalize if do_normalize is not None else self.do_normalize
       a		  =      image_mean if image_mean is not None else self.image_mean
       a		  =      image_std if image_std is not None else self.image_std

       a		  =      size if size is not None else self.size
       a		  =      get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase  )
       a		  =      crop_size if crop_size is not None else self.crop_size
       a		  =      get_size_dict(__UpperCAmelCase ,					param_name='''crop_size'''  )

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

       if do_center_crop and crop_pct is None:
        raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''  )

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

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

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

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

       if do_center_crop:
        a		  =      [self.center_crop(image=__UpperCAmelCase ,					size=__UpperCAmelCase  ) for image in images]

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

       if do_normalize:
        a		  =      [self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase  ) for image in images]

       a		  =      [to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase  ) for image in images]

       a		  =      {'''pixel_values''': images}
       return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase  )










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()


