




'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


A					       =logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class      _a    (      __a					):
       __a							:	torch.FloatTensor






class      _a    (      __a				,				__a					):
       __a							:	Union[str, Any]  							=   1
       @register_to_config
       def __init__(      self		:      Union[str, Any]  ,							lowercase		:      int = 40  ,							lowercase		:      float = 0.002  ,							lowercase		:      float = 80.0  ,							lowercase		:      float = 0.5  ,							lowercase		:      float = 1.0  ,							lowercase		:      float = 7.0  ,							lowercase		:      bool = True  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						sigma_max

           UpperCAmelCase     			=						np.linspace(0  ,							1  ,							lowercase							)
           UpperCAmelCase     			=						self._convert_to_karras(lowercase							)
           UpperCAmelCase     			=						self.sigma_to_t(lowercase							)

           # setable values
           UpperCAmelCase     			=						None
           UpperCAmelCase     			=						torch.from_numpy(lowercase							)
           UpperCAmelCase     			=						torch.from_numpy(lowercase							)
           UpperCAmelCase     			=						False
           UpperCAmelCase     			=						False
       def        A (      self		:      int  ,							lowercase		:      Union[str, Any]  ,							lowercase		:      int=None							):



           '''simple docstring'''

           if schedule_timesteps is None:
               UpperCAmelCase     			=						self.timesteps

           UpperCAmelCase     			=						(schedule_timesteps == timestep).nonzero()
           return indices.item()
       def        A (      self		:      str  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      Union[float, torch.FloatTensor]							):



           '''simple docstring'''

           if isinstance(lowercase  ,							torch.Tensor							):
               UpperCAmelCase     			=						timestep.to(self.timesteps.device							)
           UpperCAmelCase     			=						self.index_for_timestep(lowercase							)
           UpperCAmelCase     			=						self.sigmas[step_idx]

           UpperCAmelCase     			=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

           UpperCAmelCase     			=						True
           return sample
       def        A (      self		:      str  ,							lowercase		:      Union[float, np.ndarray]							):



           '''simple docstring'''

           if not isinstance(lowercase  ,							np.ndarray							):
               UpperCAmelCase     			=						np.array(lowercase  ,							dtype=np.floataa							)

           UpperCAmelCase     			=						1_000 * 0.25 * np.log(sigmas + 1E-44							)

           return timesteps
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[int] = None  ,							lowercase		:      Union[str, torch.device] = None  ,							lowercase		:      Optional[List[int]] = None  ,							):



           '''simple docstring'''

           if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''							)

           if num_inference_steps is not None and timesteps is not None:
               raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''							)

           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
               for i in range(1  ,							len(lowercase							)							):
                   if timesteps[i] >= timesteps[i - 1]:
                       raise ValueError('''`timesteps` must be in descending order.'''							)

               if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       f"`timesteps` must start before `self.config.train_timesteps`:"
                       f" {self.config.num_train_timesteps}."							)

               UpperCAmelCase     			=						np.array(lowercase  ,							dtype=np.intaa							)
               UpperCAmelCase     			=						True
           else:
               if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                       f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                       f" maximal {self.config.num_train_timesteps} timesteps."							)

               UpperCAmelCase     			=						num_inference_steps

               UpperCAmelCase     			=						self.config.num_train_timesteps // self.num_inference_steps
               UpperCAmelCase     			=						(np.arange(0  ,							lowercase							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
               UpperCAmelCase     			=						False

           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           UpperCAmelCase     			=						self.config.num_train_timesteps
           UpperCAmelCase     			=						timesteps[::-1].copy()
           UpperCAmelCase     			=						ramp / (num_train_timesteps - 1)
           UpperCAmelCase     			=						self._convert_to_karras(lowercase							)
           UpperCAmelCase     			=						self.sigma_to_t(lowercase							)

           UpperCAmelCase     			=						np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
           UpperCAmelCase     			=						torch.from_numpy(lowercase							).to(device=lowercase							)

           if str(lowercase							).startswith('''mps'''							):
               # mps does not support float64
               UpperCAmelCase     			=						torch.from_numpy(lowercase							).to(lowercase  ,							dtype=torch.floataa							)
           else:
               UpperCAmelCase     			=						torch.from_numpy(lowercase							).to(device=lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      int							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config.sigma_min
           UpperCAmelCase     			=						self.config.sigma_max

           UpperCAmelCase     			=						self.config.rho
           UpperCAmelCase     			=						sigma_min ** (1 / rho)
           UpperCAmelCase     			=						sigma_max ** (1 / rho)
           UpperCAmelCase     			=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
       def        A (      self		:      int  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config.sigma_data

           UpperCAmelCase     			=						sigma_data**2 / (sigma**2 + sigma_data**2)
           UpperCAmelCase     			=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
       def        A (      self		:      str  ,							lowercase		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config.sigma_min
           UpperCAmelCase     			=						self.config.sigma_data

           UpperCAmelCase     			=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           UpperCAmelCase     			=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
       def        A (      self		:      int  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      Union[float, torch.FloatTensor]  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      Optional[torch.Generator] = None  ,							lowercase		:      bool = True  ,							):



           '''simple docstring'''

           if (
               isinstance(lowercase  ,							lowercase							)
               or isinstance(lowercase  ,							torch.IntTensor							)
               or isinstance(lowercase  ,							torch.LongTensor							)
           ):
               raise ValueError(
                   (
                       '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                       f" `{self.__class__}.step()` is not supported. Make sure to pass"
                       ''' one of the `scheduler.timesteps` as a timestep.'''
                   )  ,							)

           if not self.is_scale_input_called:
               logger.warning(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.'''							)

           if isinstance(lowercase  ,							torch.Tensor							):
               UpperCAmelCase     			=						timestep.to(self.timesteps.device							)

           UpperCAmelCase     			=						self.config.sigma_min
           UpperCAmelCase     			=						self.config.sigma_max

           UpperCAmelCase     			=						self.index_for_timestep(lowercase							)

           # sigma_next corresponds to next_t in original implementation
           UpperCAmelCase     			=						self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
               UpperCAmelCase     			=						self.sigmas[step_index + 1]
           else:
               # Set sigma_next to sigma_min
               UpperCAmelCase     			=						self.sigmas[-1]

           # Get scalings for boundary conditions
           UpperCAmelCase			,	UpperCAmelCase     			=						self.get_scalings_for_boundary_condition(lowercase							)

           # 1. Denoise model output using boundary conditions
           UpperCAmelCase     			=						c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
               UpperCAmelCase     			=						denoised.clamp(-1  ,							1							)

           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps							) > 1:
               UpperCAmelCase     			=						randn_tensor(
                   model_output.shape  ,							dtype=model_output.dtype  ,							device=model_output.device  ,							generator=lowercase							)
           else:
               UpperCAmelCase     			=						torch.zeros_like(lowercase							)
           UpperCAmelCase     			=						noise * self.config.s_noise

           UpperCAmelCase     			=						sigma_next.clamp(min=lowercase  ,							max=lowercase							)

           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           UpperCAmelCase     			=						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

           if not return_dict:
               return (prev_sample,)

           return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase							)
       def        A (      self		:      int  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      torch.FloatTensor  ,							lowercase		:      torch.FloatTensor  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.sigmas.to(device=original_samples.device  ,							dtype=original_samples.dtype							)
           if original_samples.device.type == "mps" and torch.is_floating_point(lowercase							):
               # mps does not support float64
               UpperCAmelCase     			=						self.timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
               UpperCAmelCase     			=						timesteps.to(original_samples.device  ,							dtype=torch.floataa							)
           else:
               UpperCAmelCase     			=						self.timesteps.to(original_samples.device							)
               UpperCAmelCase     			=						timesteps.to(original_samples.device							)

           UpperCAmelCase     			=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

           UpperCAmelCase     			=						original_samples + noise * sigma
           return noisy_samples







       def __len__(      self		:      Optional[int]							):



           '''simple docstring'''

           return self.config.num_train_timesteps










'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




