

"""simple docstring"""


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a					:Optional[Any]    = logging.get_logger(__name__)  # pylint: disable=invalid-name




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""  ,					__lowerCAmelCase  ,					)
      if isinstance(__lowerCAmelCase  ,					torch.Tensor							):
            return image
      elif isinstance(__lowerCAmelCase  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[image]

      if isinstance(image[0]  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image[0].size
            SCREAMING_SNAKE_CASE__   :      Dict      		=	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            SCREAMING_SNAKE_CASE__   :      int      		=	[np.array(i.resize((w, h)  ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	np.concatenate(__lowerCAmelCase  ,					axis=0							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	np.array(__lowerCAmelCase							).astype(np.floataa							) / 255.0
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image.transpose(0  ,					3  ,					1  ,					2							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	2.0 * image - 1.0
            SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.from_numpy(__lowerCAmelCase							)
      elif isinstance(image[0]  ,					torch.Tensor							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.cat(__lowerCAmelCase  ,					dim=0							)
      return image




def  _lowercase					(    __lowerCAmelCase							)       ->				Optional[int]:
      if isinstance(__lowerCAmelCase  ,					torch.Tensor							):
            return mask
      elif isinstance(__lowerCAmelCase  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[mask]

      if isinstance(mask[0]  ,					PIL.Image.Image							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	mask[0].size
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
            SCREAMING_SNAKE_CASE__   :      Dict      		=	[np.array(m.convert("""L"""							).resize((w, h)  ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
            SCREAMING_SNAKE_CASE__   :      int      		=	np.concatenate(__lowerCAmelCase  ,					axis=0							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	mask.astype(np.floataa							) / 255.0
            SCREAMING_SNAKE_CASE__   :      Any      		=	0
            SCREAMING_SNAKE_CASE__   :      Dict      		=	1
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.from_numpy(__lowerCAmelCase							)
      elif isinstance(mask[0]  ,					torch.Tensor							):
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.cat(__lowerCAmelCase  ,					dim=0							)
      return mask



class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:UNetaDModel
     _SCREAMING_SNAKE_CASE						:RePaintScheduler

     def __init__(						self	,    _a	,    _a  )  ->      Any:


           """simple docstring"""


           super().__init__()
           self.register_modules(unet=_a	,    scheduler=_a  )





     @torch.no_grad()
     def __call__(						self	,    _a	,    _a	,    _a = 250	,    _a = 0.0	,    _a = 10	,    _a = 10	,    _a = None	,    _a = "pil"	,    _a = True	,    )  ->      Union[ImagePipelineOutput, Tuple]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	image

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	_preprocess_image(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	original_image.to(device=self.device	,    dtype=self.unet.dtype  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	_preprocess_mask(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	mask_image.to(device=self.device	,    dtype=self.unet.dtype  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	original_image.shape[0]

           # sample gaussian noise to begin the loop
           if isinstance(_a	,    _a  ) and len(_a  ) != batch_size:
                 raise ValueError(
                     f'''You have passed a list of generators of length {len(_a  )}, but requested an effective batch'''
                     f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

           SCREAMING_SNAKE_CASE__   :      int      		=	original_image.shape
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	randn_tensor(_a	,    generator=_a	,    device=self.device	,    dtype=self.unet.dtype  )

           # set step values
           self.scheduler.set_timesteps(_a	,    _a	,    _a	,    self.device  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	eta

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.scheduler.timesteps[0] + 1
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	generator[0] if isinstance(_a	,    _a  ) else generator
           for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
                 if t < t_last:
                       # predict the noise residual
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.unet(_a	,    _a  ).sample
                       # compute previous image: x_t -> x_t-1
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler.step(_a	,    _a	,    _a	,    _a	,    _a	,    _a  ).prev_sample

                 else:
                       # compute the reverse: x_t-1 -> x_t
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler.undo_step(_a	,    _a	,    _a  )
                 SCREAMING_SNAKE_CASE__   :      int      		=	t

           SCREAMING_SNAKE_CASE__   :      int      		=	(image / 2 + 0.5).clamp(0	,    1  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	image.cpu().permute(0	,    2	,    3	,    1  ).numpy()
           if output_type == "pil":
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.numpy_to_pil(_a  )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=_a  )







"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

a					:Optional[Any]    = [8, 5, 9, 7]
a					:List[Any]    = [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a					:int    = [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	claim_vector
           SCREAMING_SNAKE_CASE__   :      Any      		=	allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Any      		=	maximum_claim_table

     def 					_a			(						self  )  ->      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 					_a			(						self  )  ->      list[int]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      list[list[int]]:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self	,    **_a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.__need()
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.__allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.__available_resources()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	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__   :      List[str]      		=	False
                 for each_need in need_list:
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	True
                       for index, need in enumerate(_a  ):
                             if need > available_resources[index]:
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
                                   break
                       if execution:
                             SCREAMING_SNAKE_CASE__   :      Any      		=	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__   :      Tuple      		=	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__   :      Dict      		=	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 					_a			(						self  )  ->      Any:


           """simple docstring"""


           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()




