


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


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

@dataclass
class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  42

class   A__							(						UpperCamelCase__				,       UpperCamelCase__					):
						UpperCAmelCase     =  1




						@register_to_config
						def __init__(    self   : Optional[int]			,      _a   : int = 40			,      _a   : float = 0.0_02			,      _a   : float = 80.0			,      _a   : float = 0.5			,      _a   : float = 1.0			,      _a   : float = 7.0			,      _a   : bool = True			,      )							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=sigma_max

										_SCREAMING_SNAKE_CASE     					=np.linspace(0			,      1			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self._convert_to_karras(_a					)
										_SCREAMING_SNAKE_CASE     					=self.sigma_to_t(_a					)

										# setable values
										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=torch.from_numpy(_a					)
										_SCREAMING_SNAKE_CASE     					=torch.from_numpy(_a					)
										_SCREAMING_SNAKE_CASE     					=False
										_SCREAMING_SNAKE_CASE     					=False




						def 							__UpperCamelCase			(    self   : Dict			,      _a   : Optional[Any]			,      _a   : int=None					)							->       Dict:


										"""simple docstring"""





										if schedule_timesteps is None:
														_SCREAMING_SNAKE_CASE     					=self.timesteps

										_SCREAMING_SNAKE_CASE     					=(schedule_timesteps == timestep).nonzero()
										return indices.item()




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : torch.FloatTensor			,      _a   : Union[float, torch.FloatTensor]					)							->       torch.FloatTensor:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE     					=True
										return sample




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Union[float, np.ndarray]					)							->       Tuple:


										"""simple docstring"""





										if not isinstance(_a			,      np.ndarray					):
														_SCREAMING_SNAKE_CASE     					=np.array(_a			,      dtype=np.floataa					)

										_SCREAMING_SNAKE_CASE     					=1000 * 0.25 * np.log(sigmas + 1E-44					)

										return timesteps




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[int] = None			,      _a   : Union[str, torch.device] = None			,      _a   : Optional[List[int]] = None			,      )							->       List[str]:


										"""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(_a					)					):
																		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}."					)

														_SCREAMING_SNAKE_CASE     					=np.array(_a			,      dtype=np.intaa					)
														_SCREAMING_SNAKE_CASE     					=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."					)

														_SCREAMING_SNAKE_CASE     					=num_inference_steps

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

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

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

										if str(_a					).startswith('''mps'''					):
														# mps does not support float64
														_SCREAMING_SNAKE_CASE     					=torch.from_numpy(_a					).to(_a			,      dtype=torch.floataa					)
										else:
														_SCREAMING_SNAKE_CASE     					=torch.from_numpy(_a					).to(device=_a					)




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : int					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.config.sigma_min
										_SCREAMING_SNAKE_CASE     					=self.config.sigma_max

										_SCREAMING_SNAKE_CASE     					=self.config.rho
										_SCREAMING_SNAKE_CASE     					=sigma_min ** (1 / rho)
										_SCREAMING_SNAKE_CASE     					=sigma_max ** (1 / rho)
										_SCREAMING_SNAKE_CASE     					=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.config.sigma_data

										_SCREAMING_SNAKE_CASE     					=sigma_data**2 / (sigma**2 + sigma_data**2)
										_SCREAMING_SNAKE_CASE     					=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Dict					)							->       Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.config.sigma_min
										_SCREAMING_SNAKE_CASE     					=self.config.sigma_data

										_SCREAMING_SNAKE_CASE     					=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										_SCREAMING_SNAKE_CASE     					=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : torch.FloatTensor			,      _a   : Union[float, torch.FloatTensor]			,      _a   : torch.FloatTensor			,      _a   : Optional[torch.Generator] = None			,      _a   : bool = True			,      )							->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:


										"""simple docstring"""





										if (
										    isinstance(_a			,      _a					)
										    or isinstance(_a			,      torch.IntTensor					)
										    or isinstance(_a			,      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(_a			,      torch.Tensor					):
														_SCREAMING_SNAKE_CASE     					=timestep.to(self.timesteps.device					)

										_SCREAMING_SNAKE_CASE     					=self.config.sigma_min
										_SCREAMING_SNAKE_CASE     					=self.config.sigma_max

										_SCREAMING_SNAKE_CASE     					=self.index_for_timestep(_a					)

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

										# Get scalings for boundary conditions
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_scalings_for_boundary_condition(_a					)

										# 1. Denoise model output using boundary conditions
										_SCREAMING_SNAKE_CASE     					=c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
														_SCREAMING_SNAKE_CASE     					=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:
														_SCREAMING_SNAKE_CASE     					=randn_tensor(
														    model_output.shape			,      dtype=model_output.dtype			,      device=model_output.device			,      generator=_a					)
										else:
														_SCREAMING_SNAKE_CASE     					=torch.zeros_like(_a					)
										_SCREAMING_SNAKE_CASE     					=noise * self.config.s_noise

										_SCREAMING_SNAKE_CASE     					=sigma_next.clamp(min=_a			,      max=_a					)

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

										if not return_dict:
														return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=_a					)




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : torch.FloatTensor			,      _a   : torch.FloatTensor			,      _a   : torch.FloatTensor			,      )							->       torch.FloatTensor:


										"""simple docstring"""





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

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

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

										_SCREAMING_SNAKE_CASE     					=original_samples + noise * sigma
										return noisy_samples




						def __len__(    self   : int					)							->       int:


										"""simple docstring"""





										return self.config.num_train_timesteps



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[]
										for img in imgs:
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=img.shape[:2]
														# later: provide list and randomly choose index for resize
														_SCREAMING_SNAKE_CASE     					=np.random.randint(self.short_edge_length[0]			,      self.short_edge_length[1] + 1					)
														if size == 0:
																		return img
														_SCREAMING_SNAKE_CASE     					=size * 1.0 / min(_a			,      _a					)
														if h < w:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=size, scale * w
														else:
																		_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=scale * h, size
														if max(_a			,      _a					) > self.max_size:
																		_SCREAMING_SNAKE_CASE     					=self.max_size * 1.0 / max(_a			,      _a					)
																		_SCREAMING_SNAKE_CASE     					=newh * scale
																		_SCREAMING_SNAKE_CASE     					=neww * scale
														_SCREAMING_SNAKE_CASE     					=int(neww + 0.5					)
														_SCREAMING_SNAKE_CASE     					=int(newh + 0.5					)

														if img.dtype == np.uinta:
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(_a					)
																		_SCREAMING_SNAKE_CASE     					=pil_image.resize((neww, newh)			,      PILImageResampling.BILINEAR					)
																		_SCREAMING_SNAKE_CASE     					=np.asarray(_a					)
														else:
																		_SCREAMING_SNAKE_CASE     					=img.permute(2			,      0			,      1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																		_SCREAMING_SNAKE_CASE     					=nn.functional.interpolate(
																		    _a			,      (newh, neww)			,      mode=self.interp_method			,      align_corners=_a					).squeeze(0					)
														img_augs.append(_a					)

										return img_augs

class   A__							:




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
										_SCREAMING_SNAKE_CASE     					=[im.shape[-2:] for im in images]
										_SCREAMING_SNAKE_CASE     					=[
										    nn.functional.pad(
										        _a			,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,      value=self.pad_value			,      )
										    for size, im in zip(_a			,      _a					)
										]

										return torch.stack(_a					), torch.tensor(_a					)




						def __call__(    self   : str			,      _a   : Union[str, Any]			,      _a   : Any=False					)							->       Tuple:


										"""simple docstring"""





										with torch.no_grad():
														if not isinstance(_a			,      _a					):
																		_SCREAMING_SNAKE_CASE     					=[images]
														if single_image:
																		assert len(_a					) == 1
														for i in range(len(_a					)					):
																		if isinstance(images[i]			,      torch.Tensor					):
																						images.insert(_a			,      images.pop(_a					).to(self.device					).float()					)
																		elif not isinstance(images[i]			,      torch.Tensor					):
																						images.insert(
																						    _a			,      torch.as_tensor(img_tensorize(images.pop(_a					)			,      input_format=self.input_format					)					)
																						    .to(self.device					)
																						    .float()			,      )
            # resize smallest edge
														_SCREAMING_SNAKE_CASE     					=torch.tensor([im.shape[:2] for im in images]					)
														_SCREAMING_SNAKE_CASE     					=self.aug(_a					)
														# transpose images and convert to torch tensors
														# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
														# now normalize before pad to avoid useless arithmetic
														_SCREAMING_SNAKE_CASE     					=[self.normalizer(_a					) for x in images]
														# now pad them to do the following operations
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.pad(_a					)
														# Normalize

														if self.size_divisibility > 0:
																		raise NotImplementedError()
														# pad
														_SCREAMING_SNAKE_CASE     					=torch.true_divide(_a			,      _a					)
														if single_image:
																		return images[0], sizes[0], scales_yx[0]
														else:
																		return images, sizes, scales_yx







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=box_size
				tensor[:, 0].clamp_(min=0		,max=a__)
				tensor[:, 1].clamp_(min=0		,max=a__)
				tensor[:, 2].clamp_(min=0		,max=a__)
				tensor[:, 3].clamp_(min=0		,max=a__)