


"""simple docstring"""






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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     torch.FloatTensor
							__magic_name__					:     Optional[torch.FloatTensor]				  =  None



def 			A__		(					_UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      str=0.9_9_9				,    _UpperCAmelCase    :      List[Any]="cosine"				,    )							->	Tuple:

	'''simple docstring'''


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_UpperCAmelCase    :      List[str]	):
			return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_UpperCAmelCase    :      Optional[int]	):
			return math.exp(t * -1_2.0	)

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

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



class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							__magic_name__					:     Any				  =  [e.name for e in KarrasDiffusionSchedulers]
							__magic_name__					:     Optional[Any]				  =  1




							@register_to_config
							def __init__(						self	,					lowerCamelCase__ = 1_000	,					lowerCamelCase__ = 0.00_01	,					lowerCamelCase__ = 0.02	,					lowerCamelCase__ = "linear"	,					lowerCamelCase__ = None	,					lowerCamelCase__ = False	,					lowerCamelCase__ = "epsilon"	,					lowerCamelCase__ = "linspace"	,					lowerCamelCase__ = 0	,					)   ->	Any:







								'''simple docstring'''




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

								snake_case__  :						Union[str, Any]		=      1.0 - self.betas
								snake_case__  :						Optional[int]		=      torch.cumprod(self.alphas	,					dim=0)

								snake_case__  :						Any		=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								snake_case__  :						Union[str, Any]		=      np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								snake_case__  :						str		=      torch.from_numpy(lowerCamelCase__)

								# setable values
								snake_case__  :						str		=      None
								snake_case__  :						Dict		=      use_karras_sigmas
								self.set_timesteps(lowerCamelCase__	,					lowerCamelCase__)
								snake_case__  :						Dict		=      []
								snake_case__  :						int		=      False




							@property
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




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

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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	torch.FloatTensor:







								'''simple docstring'''




								if isinstance(lowerCamelCase__	,					torch.Tensor):
									snake_case__  :						int		=      timestep.to(self.timesteps.device)
								snake_case__  :						Dict		=      (self.timesteps == timestep).nonzero().item()
								snake_case__  :						int		=      self.sigmas[step_index]
								snake_case__  :						Any		=      sample / ((sigma**2 + 1) ** 0.5)
								snake_case__  :						int		=      True
								return sample




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								def lms_derivative(lowerCamelCase__):
									snake_case__  :						Any		=      1.0
									for k in range(lowerCamelCase__):
										if current_order == k:
											continue
										prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
									return prod

								snake_case__  :						List[str]		=      integrate.quad(lowerCamelCase__	,					self.sigmas[t]	,					self.sigmas[t + 1]	,					epsrel=1E-4)[0]

								return integrated_coeff




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
									snake_case__  :						int		=      np.linspace(0	,					self.config.num_train_timesteps - 1	,					lowerCamelCase__	,					dtype=lowerCamelCase__)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									snake_case__  :						Tuple		=      self.config.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
									snake_case__  :						Tuple		=      (np.arange(0	,					lowerCamelCase__) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									snake_case__  :						Union[str, Any]		=      self.config.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
									snake_case__  :						Optional[int]		=      (np.arange(self.config.num_train_timesteps	,					0	,					-step_ratio)).round().copy().astype(lowerCamelCase__)
									timesteps -= 1
								else:
									raise ValueError(
									    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								snake_case__  :						Optional[int]		=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								snake_case__  :						List[str]		=      np.log(lowerCamelCase__)
								snake_case__  :						int		=      np.interp(lowerCamelCase__	,					np.arange(0	,					len(lowerCamelCase__))	,					lowerCamelCase__)

								if self.use_karras_sigmas:
									snake_case__  :						Optional[Any]		=      self._convert_to_karras(in_sigmas=lowerCamelCase__)
									snake_case__  :						int		=      np.array([self._sigma_to_t(lowerCamelCase__	,					lowerCamelCase__) for sigma in sigmas])

								snake_case__  :						int		=      np.concatenate([sigmas, [0.0]]).astype(np.floataa)

								snake_case__  :						Any		=      torch.from_numpy(lowerCamelCase__).to(device=lowerCamelCase__)
								if str(lowerCamelCase__).startswith("mps"):
									# mps does not support float64
									snake_case__  :						int		=      torch.from_numpy(lowerCamelCase__).to(lowerCamelCase__	,					dtype=torch.floataa)
								else:
									snake_case__  :						Optional[int]		=      torch.from_numpy(lowerCamelCase__).to(device=lowerCamelCase__)

								snake_case__  :						List[Any]		=      []




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      np.log(lowerCamelCase__)

								# get distribution
								snake_case__  :						int		=      log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								snake_case__  :						Any		=      np.cumsum((dists >= 0)	,					axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
								snake_case__  :						Union[str, Any]		=      low_idx + 1

								snake_case__  :						List[str]		=      log_sigmas[low_idx]
								snake_case__  :						int		=      log_sigmas[high_idx]

								# interpolate sigmas
								snake_case__  :						List[str]		=      (low - log_sigma) / (low - high)
								snake_case__  :						int		=      np.clip(lowerCamelCase__	,					0	,					1)

								# transform interpolation to time range
								snake_case__  :						Tuple		=      (1 - w) * low_idx + w * high_idx
								snake_case__  :						str		=      t.reshape(sigma.shape)
								return t




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	torch.FloatTensor:







								'''simple docstring'''




								snake_case__  :						float		=      in_sigmas[-1].item()
								snake_case__  :						float		=      in_sigmas[0].item()

								snake_case__  :						Optional[Any]		=      7.0  # 7.0 is the value used in the paper
								snake_case__  :						Tuple		=      np.linspace(0	,					1	,					self.num_inference_steps)
								snake_case__  :						Union[str, Any]		=      sigma_min ** (1 / rho)
								snake_case__  :						Dict		=      sigma_max ** (1 / rho)
								snake_case__  :						int		=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = 4	,					lowerCamelCase__ = True	,					)   ->	Union[LMSDiscreteSchedulerOutput, Tuple]:







								'''simple docstring'''




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

								if isinstance(lowerCamelCase__	,					torch.Tensor):
									snake_case__  :						List[Any]		=      timestep.to(self.timesteps.device)
								snake_case__  :						Dict		=      (self.timesteps == timestep).nonzero().item()
								snake_case__  :						str		=      self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									snake_case__  :						Optional[int]		=      sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									snake_case__  :						Optional[Any]		=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
									snake_case__  :						Tuple		=      model_output
								else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								# 2. Convert to an ODE derivative
								snake_case__  :						List[Any]		=      (sample - pred_original_sample) / sigma
								self.derivatives.append(lowerCamelCase__)
								if len(self.derivatives) > order:
									self.derivatives.pop(0)

								# 3. Compute linear multistep coefficients
								snake_case__  :						Any		=      min(step_index + 1	,					lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      [self.get_lms_coefficient(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__) for curr_order in range(lowerCamelCase__)]

								# 4. Compute previous sample based on the derivatives path
								snake_case__  :						List[Any]		=      sample + sum(
								    coeff * derivative for coeff, derivative in zip(lowerCamelCase__	,					reversed(self.derivatives)))

								if not return_dict:
									return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase__	,					pred_original_sample=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	torch.FloatTensor:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.sigmas.to(device=original_samples.device	,					dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__):
									# mps does not support float64
									snake_case__  :						str		=      self.timesteps.to(original_samples.device	,					dtype=torch.floataa)
									snake_case__  :						int		=      timesteps.to(original_samples.device	,					dtype=torch.floataa)
								else:
									snake_case__  :						List[str]		=      self.timesteps.to(original_samples.device)
									snake_case__  :						Any		=      timesteps.to(original_samples.device)

								snake_case__  :						Dict		=      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								snake_case__  :						Optional[int]		=      sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									snake_case__  :						Tuple		=      sigma.unsqueeze(-1)

								snake_case__  :						Tuple		=      original_samples + noise * sigma
								return noisy_samples







							def __len__(						self)   ->	Any:







								'''simple docstring'''




								return self.config.num_train_timesteps












"""simple docstring"""





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 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




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

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

								return img_augs





class 							_lowerCamelCase   :


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


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    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 ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[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
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						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
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

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



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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






