

'''simple docstring'''



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 						__lowerCamelCase       (   __snake_case     :		Any,		__snake_case     :		Optional[int]=0.9_99,		__snake_case     :		Optional[int]="cosine",		)       ->							Union[str, Any]:






						"""simple docstring"""





						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__snake_case     :		int       ):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__snake_case     :		int       ):
																		return math.exp(t * -12.0       )

						else:
												raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"       )

						A__     :   Optional[int]      =[]
						for i in range(__snake_case       ):
												A__     :   Tuple      =i / num_diffusion_timesteps
												A__     :   Dict      =(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(__snake_case       ) / alpha_bar_fn(__snake_case       ),		__snake_case       )       )
						return torch.tensor(__snake_case,		dtype=torch.floataa       )



class lowerCamelCase (  lowercase_       ,						lowercase_						):



			'''simple docstring'''






			__snake_case    	=       [e.name for e in KarrasDiffusionSchedulers]
			__snake_case    	=       2


			@register_to_config
			def __init__(      self       :						Tuple		,			lowerCAmelCase_       :						int = 10_00		,			lowerCAmelCase_       :						float = 0.00085		,			lowerCAmelCase_       :						float = 0.012		,			lowerCAmelCase_       :						str = "linear"		,			lowerCAmelCase_       :						Optional[Union[np.ndarray, List[float]]] = None		,			lowerCAmelCase_       :						str = "epsilon"		,			lowerCAmelCase_       :						str = "linspace"		,			lowerCAmelCase_       :						int = 0		,			)			->		str:





									'''simple docstring'''


									if trained_betas is not None:
															A__     :   Optional[Any]      =torch.tensor(__UpperCAmelCase		,			dtype=torch.floataa )
									elif beta_schedule == "linear":
															A__     :   int      =torch.linspace(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			dtype=torch.floataa )
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															A__     :   int      =(
															    torch.linspace(beta_start**0.5		,			beta_end**0.5		,			__UpperCAmelCase		,			dtype=torch.floataa ) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															A__     :   Optional[int]      =betas_for_alpha_bar(__UpperCAmelCase )
									else:
															raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )

									A__     :   Dict      =1.0 - self.betas
									A__     :   Dict      =torch.cumprod(self.alphas		,			dim=0 )

									#  set all values
									self.set_timesteps(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase )


			def lowercase__						(      self       :						int		,			lowerCAmelCase_       :						int		,			lowerCAmelCase_       :						Tuple=None )			->		Union[str, Any]:





									'''simple docstring'''


									if schedule_timesteps is None:
															A__     :   Union[str, Any]      =self.timesteps

									A__     :   Optional[Any]      =(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter ) == 0:
															A__     :   List[Any]      =1 if len(__UpperCAmelCase ) > 1 else 0
									else:
															A__     :   Dict      =timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase ) else timestep
															A__     :   List[str]      =self._index_counter[timestep_int]

									return indices[pos].item()


			@property
			def lowercase__						(      self       :						List[Any] )			->		Tuple:





									'''simple docstring'''


									if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


			def lowercase__						(      self       :						Optional[Any]		,			lowerCAmelCase_       :						torch.FloatTensor		,			lowerCAmelCase_       :						Union[float, torch.FloatTensor]		,			)			->		torch.FloatTensor:





									'''simple docstring'''
									A__     :   Union[str, Any]      =self.index_for_timestep(__UpperCAmelCase )

									if self.state_in_first_order:
															A__     :   int      =self.sigmas[step_index]
									else:
															A__     :   int      =self.sigmas_interpol[step_index]

									A__     :   Any      =sample / ((sigma**2 + 1) ** 0.5)
									return sample


			def lowercase__						(      self       :						int		,			lowerCAmelCase_       :						int		,			lowerCAmelCase_       :						Union[str, torch.device] = None		,			lowerCAmelCase_       :						Optional[int] = None		,			)			->		Any:





									'''simple docstring'''
									A__     :   str      =num_inference_steps

									A__     :   Dict      =num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
															A__     :   Tuple      =np.linspace(0		,			num_train_timesteps - 1		,			__UpperCAmelCase		,			dtype=__UpperCAmelCase )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
															A__     :   Dict      =num_train_timesteps // self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															A__     :   Tuple      =(np.arange(0		,			__UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase )
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															A__     :   Optional[Any]      =num_train_timesteps / self.num_inference_steps
															# creates integer timesteps by multiplying by ratio
															# casting to int to avoid issues when num_inference_step is power of 3
															A__     :   Dict      =(np.arange(__UpperCAmelCase		,			0		,			-step_ratio )).round().copy().astype(__UpperCAmelCase )
															timesteps -= 1
									else:
															raise ValueError(
															    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

									A__     :   List[str]      =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
									A__     :   Union[str, Any]      =torch.from_numpy(np.log(__UpperCAmelCase ) ).to(__UpperCAmelCase )

									A__     :   Dict      =np.interp(__UpperCAmelCase		,			np.arange(0		,			len(__UpperCAmelCase ) )		,			__UpperCAmelCase )
									A__     :   Any      =np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
									A__     :   Tuple      =torch.from_numpy(__UpperCAmelCase ).to(device=__UpperCAmelCase )

									# interpolate sigmas
									A__     :   Tuple      =sigmas.log().lerp(sigmas.roll(1 ).log()		,			0.5 ).exp()

									A__     :   Union[str, Any]      =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
									A__     :   Optional[Any]      =torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

									if str(__UpperCAmelCase ).startswith("""mps""" ):
															# mps does not support float64
															A__     :   str      =torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase		,			dtype=torch.floataa )
									else:
															A__     :   Tuple      =torch.from_numpy(__UpperCAmelCase ).to(__UpperCAmelCase )

									# interpolate timesteps
									A__     :   Tuple      =self.sigma_to_t(__UpperCAmelCase ).to(__UpperCAmelCase		,			dtype=timesteps.dtype )
									A__     :   str      =torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,			dim=-1 ).flatten()

									A__     :   List[Any]      =torch.cat([timesteps[:1], interleaved_timesteps] )

									A__     :   Optional[int]      =None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A__     :   List[str]      =defaultdict(__UpperCAmelCase )


			def lowercase__						(      self       :						Dict		,			lowerCAmelCase_       :						Optional[Any] )			->		List[str]:





									'''simple docstring'''
									A__     :   Union[str, Any]      =sigma.log()

									# get distribution
									A__     :   List[Any]      =log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A__     :   List[str]      =dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
									A__     :   Union[str, Any]      =low_idx + 1

									A__     :   Optional[Any]      =self.log_sigmas[low_idx]
									A__     :   Optional[Any]      =self.log_sigmas[high_idx]

									# interpolate sigmas
									A__     :   List[Any]      =(low - log_sigma) / (low - high)
									A__     :   Optional[int]      =w.clamp(0		,			1 )

									# transform interpolation to time range
									A__     :   Union[str, Any]      =(1 - w) * low_idx + w * high_idx
									A__     :   int      =t.view(sigma.shape )
									return t


			@property
			def lowercase__						(      self       :						List[Any] )			->		Union[str, Any]:





									'''simple docstring'''
									return self.sample is None


			def lowercase__						(      self       :						List[Any]		,			lowerCAmelCase_       :						Union[torch.FloatTensor, np.ndarray]		,			lowerCAmelCase_       :						Union[float, torch.FloatTensor]		,			lowerCAmelCase_       :						Union[torch.FloatTensor, np.ndarray]		,			lowerCAmelCase_       :						bool = True		,			)			->		Union[SchedulerOutput, Tuple]:





									'''simple docstring'''
									A__     :   Any      =self.index_for_timestep(__UpperCAmelCase )

									# advance index counter by 1
									A__     :   Any      =timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
															A__     :   List[Any]      =self.sigmas[step_index]
															A__     :   Optional[Any]      =self.sigmas_interpol[step_index + 1]
															A__     :   Optional[Any]      =self.sigmas[step_index + 1]
									else:
															# 2nd order / KDPM2's method
															A__     :   int      =self.sigmas[step_index - 1]
															A__     :   Union[str, Any]      =self.sigmas_interpol[step_index]
															A__     :   int      =self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									A__     :   List[Any]      =0
									A__     :   Dict      =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															A__     :   Union[str, Any]      =sigma_hat if self.state_in_first_order else sigma_interpol
															A__     :   List[str]      =sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
															A__     :   Any      =sigma_hat if self.state_in_first_order else sigma_interpol
															A__     :   Union[str, Any]      =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**2 + 1)
															)
									elif self.config.prediction_type == "sample":
															raise NotImplementedError("""prediction_type not implemented yet: sample""" )
									else:
															raise ValueError(
															    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

									if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															A__     :   Tuple      =(sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															A__     :   Optional[int]      =sigma_interpol - sigma_hat

															# store for 2nd order step
															A__     :   Optional[Any]      =sample
									else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															A__     :   List[Any]      =(sample - pred_original_sample) / sigma_interpol

															# 3. delta timestep
															A__     :   Dict      =sigma_next - sigma_hat

															A__     :   str      =self.sample
															A__     :   Tuple      =None

									A__     :   List[str]      =sample + derivative * dt

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=__UpperCAmelCase )


			def lowercase__						(      self       :						Dict		,			lowerCAmelCase_       :						torch.FloatTensor		,			lowerCAmelCase_       :						torch.FloatTensor		,			lowerCAmelCase_       :						torch.FloatTensor		,			)			->		torch.FloatTensor:





									'''simple docstring'''
									A__     :   List[str]      =self.sigmas.to(device=original_samples.device		,			dtype=original_samples.dtype )
									if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase ):
															# mps does not support float64
															A__     :   List[str]      =self.timesteps.to(original_samples.device		,			dtype=torch.floataa )
															A__     :   Optional[int]      =timesteps.to(original_samples.device		,			dtype=torch.floataa )
									else:
															A__     :   str      =self.timesteps.to(original_samples.device )
															A__     :   int      =timesteps.to(original_samples.device )

									A__     :   Optional[Any]      =[self.index_for_timestep(__UpperCAmelCase		,			__UpperCAmelCase ) for t in timesteps]

									A__     :   str      =sigmas[step_indices].flatten()
									while len(sigma.shape ) < len(original_samples.shape ):
															A__     :   List[str]      =sigma.unsqueeze(-1 )

									A__     :   Any      =original_samples + noise * sigma
									return noisy_samples



			def __len__(      self       :						Dict )			->		Any:





									'''simple docstring'''
									return self.config.num_train_timesteps









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()


