



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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: Union[str, Any]=0.9_99			,       _UpperCAmelCase							: Dict="cosine"			,       ):







		'''simple docstring'''





		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_UpperCAmelCase							: Union[str, Any]						):
						return math.exp(t * -12.0						)

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

		__UpperCAmelCase   :					Dict													=     []
		for i in range(_UpperCAmelCase						):
				__UpperCAmelCase   :					Union[str, Any]													=     i / num_diffusion_timesteps
				__UpperCAmelCase   :					str													=     (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 		UpperCAmelCase__      (      __UpperCamelCase						,__UpperCamelCase       ):




			'''simple docstring'''






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




			@register_to_config
			def __init__(       self							:			Tuple	,	a_							:			int = 10_00	,	a_							:			float = 0.0_0_0_8_5	,	a_							:			float = 0.0_1_2	,	a_							:			str = "linear"	,	a_							:			Optional[Union[np.ndarray, List[float]]] = None	,	a_							:			str = "epsilon"	,	a_							:			str = "linspace"	,	a_							:			int = 0	,	):
					'''simple docstring'''





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

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

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




			def 			snake_case__					(       self							:			List[str]	,	a_							:			Union[str, Any]	,	a_							:			Tuple=None			):
					'''simple docstring'''





					if schedule_timesteps is None:
							__UpperCAmelCase   :					Any													=     self.timesteps

					__UpperCAmelCase   :					Optional[int]													=     (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:
							__UpperCAmelCase   :					Any													=     1 if len(a_			) > 1 else 0
					else:
							__UpperCAmelCase   :					Any													=     timestep.cpu().item() if torch.is_tensor(a_			) else timestep
							__UpperCAmelCase   :					Any													=     self._index_counter[timestep_int]

					return indices[pos].item()




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





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

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




			def 			snake_case__					(       self							:			Any	,	a_							:			torch.FloatTensor	,	a_							:			Union[float, torch.FloatTensor]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.index_for_timestep(a_			)

					if self.state_in_first_order:
							__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index]
					else:
							__UpperCAmelCase   :					Dict													=     self.sigmas_interpol[step_index - 1]

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




			def 			snake_case__					(       self							:			Dict	,	a_							:			int	,	a_							:			Union[str, torch.device] = None	,	a_							:			Optional[int] = None	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     num_inference_steps

					__UpperCAmelCase   :					str													=     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":
							__UpperCAmelCase   :					int													=     np.linspace(0	,	num_train_timesteps - 1	,	a_	,	dtype=a_			)[::-1].copy()
					elif self.config.timestep_spacing == "leading":
							__UpperCAmelCase   :					int													=     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
							__UpperCAmelCase   :					int													=     (np.arange(0	,	a_			) * step_ratio).round()[::-1].copy().astype(a_			)
							timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
							__UpperCAmelCase   :					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
							__UpperCAmelCase   :					int													=     (np.arange(a_	,	0	,	-step_ratio			)).round().copy().astype(a_			)
							timesteps -= 1
					else:
							raise ValueError(
							    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)

					__UpperCAmelCase   :					Optional[int]													=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
					__UpperCAmelCase   :					List[Any]													=     torch.from_numpy(np.log(a_			)			).to(a_			)

					__UpperCAmelCase   :					List[Any]													=     np.interp(a_	,	np.arange(0	,	len(a_			)			)	,	a_			)
					__UpperCAmelCase   :					Optional[Any]													=     np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
					__UpperCAmelCase   :					int													=     torch.from_numpy(a_			).to(device=a_			)

					# compute up and down sigmas
					__UpperCAmelCase   :					Any													=     sigmas.roll(-1			)
					__UpperCAmelCase   :					int													=     0.0
					__UpperCAmelCase   :					List[Any]													=     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
					__UpperCAmelCase   :					List[str]													=     (sigmas_next**2 - sigmas_up**2) ** 0.5
					__UpperCAmelCase   :					Dict													=     0.0

					# compute interpolated sigmas
					__UpperCAmelCase   :					Tuple													=     sigmas.log().lerp(sigmas_down.log()	,	0.5			).exp()
					__UpperCAmelCase   :					Any													=     0.0

					# set sigmas
					__UpperCAmelCase   :					List[str]													=     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
					__UpperCAmelCase   :					str													=     torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)
					__UpperCAmelCase   :					Optional[int]													=     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2			), sigmas_up[-1:]]			)
					__UpperCAmelCase   :					Tuple													=     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2			), sigmas_down[-1:]]			)

					if str(a_			).startswith('''mps'''			):
							# mps does not support float64
							__UpperCAmelCase   :					str													=     torch.from_numpy(a_			).to(a_	,	dtype=torch.floataa			)
					else:
							__UpperCAmelCase   :					Any													=     torch.from_numpy(a_			).to(a_			)

					__UpperCAmelCase   :					Tuple													=     self.sigma_to_t(a_			).to(a_	,	dtype=timesteps.dtype			)
					__UpperCAmelCase   :					Union[str, Any]													=     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,	dim=-1			).flatten()

					__UpperCAmelCase   :					str													=     torch.cat([timesteps[:1], interleaved_timesteps]			)

					__UpperCAmelCase   :					Tuple													=     None

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




			def 			snake_case__					(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     sigma.log()

					# get distribution
					__UpperCAmelCase   :					Optional[int]													=     log_sigma - self.log_sigmas[:, None]

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

					__UpperCAmelCase   :					Union[str, Any]													=     self.log_sigmas[low_idx]
					__UpperCAmelCase   :					Optional[int]													=     self.log_sigmas[high_idx]

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

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




			@property
			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					return self.sample is None




			def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			Union[torch.FloatTensor, np.ndarray]	,	a_							:			Union[float, torch.FloatTensor]	,	a_							:			Union[torch.FloatTensor, np.ndarray]	,	a_							:			Optional[torch.Generator] = None	,	a_							:			bool = True	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.index_for_timestep(a_			)

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

					if self.state_in_first_order:
							__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index]
							__UpperCAmelCase   :					str													=     self.sigmas_interpol[step_index]
							__UpperCAmelCase   :					Optional[int]													=     self.sigmas_up[step_index]
							__UpperCAmelCase   :					int													=     self.sigmas_down[step_index - 1]
					else:
							# 2nd order / KPDM2's method
							__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index - 1]
							__UpperCAmelCase   :					Optional[int]													=     self.sigmas_interpol[step_index - 1]
							__UpperCAmelCase   :					List[str]													=     self.sigmas_up[step_index - 1]
							__UpperCAmelCase   :					Union[str, Any]													=     self.sigmas_down[step_index - 1]

					# 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
					__UpperCAmelCase   :					Optional[int]													=     0
					__UpperCAmelCase   :					Optional[Any]													=     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					__UpperCAmelCase   :					Union[str, Any]													=     model_output.device
					__UpperCAmelCase   :					str													=     randn_tensor(model_output.shape	,	dtype=model_output.dtype	,	device=a_	,	generator=a_			)

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
							__UpperCAmelCase   :					Optional[int]													=     sigma_hat if self.state_in_first_order else sigma_interpol
							__UpperCAmelCase   :					str													=     sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
							__UpperCAmelCase   :					Optional[Any]													=     sigma_hat if self.state_in_first_order else sigma_interpol
							__UpperCAmelCase   :					Optional[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
							__UpperCAmelCase   :					Union[str, Any]													=     (sample - pred_original_sample) / sigma_hat
							# 3. delta timestep
							__UpperCAmelCase   :					List[Any]													=     sigma_interpol - sigma_hat

							# store for 2nd order step
							__UpperCAmelCase   :					Optional[Any]													=     sample
							__UpperCAmelCase   :					str													=     dt
							__UpperCAmelCase   :					Union[str, Any]													=     sample + derivative * dt
					else:
							# DPM-Solver-2
							# 2. Convert to an ODE derivative for 2nd order
							__UpperCAmelCase   :					int													=     (sample - pred_original_sample) / sigma_interpol
							# 3. delta timestep
							__UpperCAmelCase   :					Dict													=     sigma_down - sigma_hat

							__UpperCAmelCase   :					Any													=     self.sample
							__UpperCAmelCase   :					str													=     None

							__UpperCAmelCase   :					Any													=     sample + derivative * dt
							__UpperCAmelCase   :					Optional[int]													=     prev_sample + noise * sigma_up

					if not return_dict:
							return (prev_sample,)

					return SchedulerOutput(prev_sample=a_			)




			def 			snake_case__					(       self							:			str	,	a_							:			torch.FloatTensor	,	a_							:			torch.FloatTensor	,	a_							:			torch.FloatTensor	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     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
							__UpperCAmelCase   :					str													=     self.timesteps.to(original_samples.device	,	dtype=torch.floataa			)
							__UpperCAmelCase   :					Optional[int]													=     timesteps.to(original_samples.device	,	dtype=torch.floataa			)
					else:
							__UpperCAmelCase   :					List[str]													=     self.timesteps.to(original_samples.device			)
							__UpperCAmelCase   :					str													=     timesteps.to(original_samples.device			)

					__UpperCAmelCase   :					str													=     [self.index_for_timestep(a_	,	a_			) for t in timesteps]

					__UpperCAmelCase   :					Union[str, Any]													=     sigmas[step_indices].flatten()
					while len(sigma.shape			) < len(original_samples.shape			):
							__UpperCAmelCase   :					Dict													=     sigma.unsqueeze(-1			)

					__UpperCAmelCase   :					Union[str, Any]													=     original_samples + noise * sigma
					return noisy_samples




			def __len__(       self							:			Tuple			):
					'''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 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			int	,	a_							:			str	,	a_							:			List[str]=sys.maxsize			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     '''bilinear'''
					__UpperCAmelCase   :					str													=     max_size
					__UpperCAmelCase   :					Dict													=     short_edge_length




			def __call__(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





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

							if img.dtype == np.uinta:
									__UpperCAmelCase   :					Any													=     Image.fromarray(a_			)
									__UpperCAmelCase   :					Any													=     pil_image.resize((neww, newh)	,	PILImageResampling.BILINEAR			)
									__UpperCAmelCase   :					List[Any]													=     np.asarray(a_			)
							else:
									__UpperCAmelCase   :					Union[str, Any]													=     img.permute(2	,	0	,	1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
									__UpperCAmelCase   :					Optional[int]													=     nn.functional.interpolate(
									    a_	,	(newh, neww)	,	mode=self.interp_method	,	align_corners=a_			).squeeze(0			)
							img_augs.append(a_			)

					return img_augs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,	cfg.INPUT.MAX_SIZE_TEST			)
					__UpperCAmelCase   :					List[str]													=     cfg.INPUT.FORMAT
					__UpperCAmelCase   :					int													=     cfg.SIZE_DIVISIBILITY
					__UpperCAmelCase   :					int													=     cfg.PAD_VALUE
					__UpperCAmelCase   :					Any													=     cfg.INPUT.MAX_SIZE_TEST
					__UpperCAmelCase   :					Tuple													=     cfg.MODEL.DEVICE
					__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					List[Any]													=     torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					Union[str, Any]													=     lambda a_			: (x - self.pixel_mean) / self.pixel_std




			def 			snake_case__					(       self							:			int	,	a_							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     tuple(max(a_			) for s in zip(*[img.shape for img in images]			)			)
					__UpperCAmelCase   :					Any													=     [im.shape[-2:] for im in images]
					__UpperCAmelCase   :					Union[str, Any]													=     [
					    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							:			List[str]	,	a_							:			int	,	a_							:			int=False			):
					'''simple docstring'''





					with torch.no_grad():
							if not isinstance(a_	,	a_			):
									__UpperCAmelCase   :					Optional[Any]													=     [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   :					Optional[Any]													=     torch.tensor([im.shape[:2] for im in images]			)
							__UpperCAmelCase   :					Dict													=     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   :					Optional[int]													=     [self.normalizer(a_			) for x in images]
							# now pad them to do the following operations
							__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.pad(a_			)
							# Normalize

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

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple[int, int]						):







		'''simple docstring'''





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





