






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCamelCase__							=     8






def   UpperCamelCase  (       snake_case__     :   Any	,snake_case__     :   Optional[int]=BITS  ):




     '''simple docstring'''






     __snake_case			:str          =						x.device

     __snake_case			:Any          =						(x * 255).int().clamp(0	,255  )

     __snake_case			:str          =						2 ** torch.arange(bits - 1	,-1	,-1	,device=snake_case__  )
     __snake_case			:List[Any]          =						rearrange(snake_case__	,"""d -> d 1 1"""  )
     __snake_case			:List[Any]          =						rearrange(snake_case__	,"""b c h w -> b c 1 h w"""  )

     __snake_case			:List[Any]          =						((x & mask) != 0).float()
     __snake_case			:str          =						rearrange(snake_case__	,"""b c d h w -> b (c d) h w"""  )
     __snake_case			:str          =						bits * 2 - 1
     return bits






def   UpperCamelCase  (       snake_case__     :   Any	,snake_case__     :   int=BITS  ):




     '''simple docstring'''






     __snake_case			:str          =						x.device

     __snake_case			:Any          =						(x > 0).int()
     __snake_case			:List[str]          =						2 ** torch.arange(bits - 1	,-1	,-1	,device=snake_case__	,dtype=torch.intaa  )

     __snake_case			:int          =						rearrange(snake_case__	,"""d -> d 1 1"""  )
     __snake_case			:Tuple          =						rearrange(snake_case__	,"""b (c d) h w -> b c d h w"""	,d=8  )
     __snake_case			:Tuple          =						reduce(x * mask	,"""b c d h w -> b c h w"""	,"""sum"""  )
     return (dec / 255).clamp(0.0	,1.0  )






def   UpperCamelCase  (       self     :   str	,snake_case__     :   torch.FloatTensor	,snake_case__     :   int	,snake_case__     :   torch.FloatTensor	,snake_case__     :   float = 0.0	,snake_case__     :   bool = True	,snake_case__     :   List[Any]=None	,snake_case__     :   bool = True	,):




     '''simple docstring'''






     if self.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     __snake_case			:Optional[Any]          =						timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     __snake_case			:List[str]          =						self.alphas_cumprod[timestep]
     __snake_case			:str          =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     __snake_case			:str          =						1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __snake_case			:Dict          =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     __snake_case			:Optional[int]          =						self.bit_scale
     if self.config.clip_sample:
          __snake_case			:Dict          =						torch.clamp(snake_case__	,-scale	,snake_case__  )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     __snake_case			:Union[str, Any]          =						self._get_variance(snake_case__	,snake_case__  )
     __snake_case			:int          =						eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          __snake_case			:Optional[Any]          =						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __snake_case			:Dict          =						(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __snake_case			:List[Any]          =						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          __snake_case			:Optional[Any]          =						model_output.device if torch.is_tensor(snake_case__  ) else """cpu"""
          __snake_case			:Optional[Any]          =						torch.randn(model_output.shape	,dtype=model_output.dtype	,generator=snake_case__  ).to(snake_case__  )
          __snake_case			:List[Any]          =						self._get_variance(snake_case__	,snake_case__  ) ** 0.5 * eta * noise

          __snake_case			:Optional[Any]          =						prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=snake_case__	,pred_original_sample=snake_case__  )






def   UpperCamelCase  (       self     :   Union[str, Any]	,snake_case__     :   torch.FloatTensor	,snake_case__     :   int	,snake_case__     :   torch.FloatTensor	,snake_case__     :   Tuple="epsilon"	,snake_case__     :   Optional[Any]=None	,snake_case__     :   bool = True	,):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          __snake_case       , __snake_case			:List[Any]          =						torch.split(snake_case__	,sample.shape[1]	,dim=1  )
     else:
          __snake_case			:Union[str, Any]          =						None

     # 1. compute alphas, betas
     __snake_case			:Tuple          =						self.alphas_cumprod[t]
     __snake_case			:int          =						self.alphas_cumprod[t - 1] if t > 0 else self.one
     __snake_case			:List[str]          =						1 - alpha_prod_t
     __snake_case			:int          =						1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          __snake_case			:List[Any]          =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          __snake_case			:Optional[Any]          =						model_output
     else:
          raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''  )

     # 3. Clip "predicted x_0"
     __snake_case			:List[Any]          =						self.bit_scale
     if self.config.clip_sample:
          __snake_case			:str          =						torch.clamp(snake_case__	,-scale	,snake_case__  )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __snake_case			:Tuple          =						(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     __snake_case			:List[str]          =						self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __snake_case			:Dict          =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     __snake_case			:Optional[Any]          =						0
     if t > 0:
          __snake_case			:str          =						torch.randn(
              model_output.size()	,dtype=model_output.dtype	,layout=model_output.layout	,generator=snake_case__  ).to(model_output.device  )
          __snake_case			:List[str]          =						(self._get_variance(snake_case__	,predicted_variance=snake_case__  ) ** 0.5) * noise

     __snake_case			:List[Any]          =						pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=snake_case__	,pred_original_sample=snake_case__  )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__ = 1.0			,						) ->      List[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:List[str]          =						bit_scale
            __snake_case			:str          =						(
                ddim_bit_scheduler_step if isinstance(a__			,						a__    ) else ddpm_bit_scheduler_step
            )

            self.register_modules(unet=a__			,						scheduler=a__    )



       @torch.no_grad()
       def __call__(							self			,						a__ = 2_56			,						a__ = 2_56			,						a__ = 50			,						a__ = None			,						a__ = 1			,						a__ = "pil"			,						a__ = True			,						**a__			,						) ->      Union[Tuple, ImagePipelineOutput]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)			,						generator=a__			,						)
            __snake_case			:Optional[int]          =						decimal_to_bits(a__    ) * self.bit_scale
            __snake_case			:Tuple          =						latents.to(self.device    )

            self.scheduler.set_timesteps(a__    )

            for t in self.progress_bar(self.scheduler.timesteps    ):
                 # predict the noise residual
                 __snake_case			:Optional[Any]          =						self.unet(a__			,						a__    ).sample

                 # compute the previous noisy sample x_t -> x_t-1
                 __snake_case			:str          =						self.scheduler.step(a__			,						a__			,						a__    ).prev_sample

            __snake_case			:int          =						bits_to_decimal(a__    )

            if output_type == "pil":
                 __snake_case			:Optional[Any]          =						self.numpy_to_pil(a__    )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=a__    )










from __future__ import annotations

import time

import numpy as np

lowerCamelCase__							=     [8, 5, 9, 7]
lowerCamelCase__							=     [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
lowerCamelCase__							=     [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 			snake_case__   :

       '''simple docstring'''



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






            '''simple docstring'''





            __snake_case			:Dict          =						claim_vector
            __snake_case			:Optional[int]          =						allocated_resources_table
            __snake_case			:Optional[int]          =						maximum_claim_table



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






            '''simple docstring'''





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



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






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						**a__    ) ->      None:






            '''simple docstring'''





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






            '''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()


