


'''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			:
    """simple docstring"""


    def __init__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[int]=sys.maxsize):
           _A					:     Optional[int]     = 'bilinear'
           _A					:     List[Any]     = max_size
           _A					:     Tuple     = short_edge_length


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

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

           return img_augs




class 			__lowerCamelCase			:
    """simple docstring"""


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


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           _A					:     Optional[int]     = tuple(max(SCREAMING_SNAKE_CASE) for s in zip(*[img.shape for img in images]))
           _A					:     List[Any]     = [im.shape[-2:] for im in images]
           _A					:     str     = [
               nn.functional.pad(
                   SCREAMING_SNAKE_CASE				,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,							value=self.pad_value				,							)
               for size, im in zip(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           ]

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


    def __call__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							int=False):
           with torch.no_grad():
                  if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                         _A					:     Optional[Any]     = [images]
                  if single_image:
                         assert len(SCREAMING_SNAKE_CASE) == 1
                  for i in range(len(SCREAMING_SNAKE_CASE)):
                         if isinstance(images[i]				,							torch.Tensor):
                                images.insert(SCREAMING_SNAKE_CASE				,							images.pop(SCREAMING_SNAKE_CASE).to(self.device).float())
                         elif not isinstance(images[i]				,							torch.Tensor):
                                images.insert(
                                    SCREAMING_SNAKE_CASE				,							torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE)				,							input_format=self.input_format))
                                    .to(self.device)
                                    .float()				,							)
            # resize smallest edge
                  _A					:     Optional[int]     = torch.tensor([im.shape[:2] for im in images])
                  _A					:     str     = self.aug(SCREAMING_SNAKE_CASE)
                  # 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
                  _A					:     Any     = [self.normalizer(SCREAMING_SNAKE_CASE) for x in images]
                  # now pad them to do the following operations
                  _A  ,			_A					:     int     = self.pad(SCREAMING_SNAKE_CASE)
                  # Normalize

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[int]       ):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes






def 				lowerCAmelCase__	(    lowerCamelCase					:      Dict  ,lowerCamelCase					:      Tuple[int, int]       ):
       assert torch.isfinite(lowerCamelCase       ).all(), "Box tensor contains infinite or NaN!"
       _A  ,			_A					:     Union[str, Any]     = box_size
       tensor[:, 0].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 1].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 2].clamp_(min=0  ,max=lowerCamelCase       )
       tensor[:, 3].clamp_(min=0  ,max=lowerCamelCase       )










'''simple docstring'''







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   42
    a													=   None






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Tuple=0.999  ,lowerCamelCase					:      int="cosine"  ,):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCamelCase					:      str       ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCamelCase					:      Dict       ):
                     return math.exp(t * -12.0       )

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

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


class 			__lowerCamelCase			(	a_    , a_	):
    """simple docstring"""
    a													=   1


    @register_to_config
    def __init__(  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int = 1000				,							SCREAMING_SNAKE_CASE      :							float = 0.0001				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							str = "linear"				,							SCREAMING_SNAKE_CASE      :							Optional[Union[np.ndarray, List[float]]] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							str = "epsilon"				,							SCREAMING_SNAKE_CASE      :							float = 1.0				,							**SCREAMING_SNAKE_CASE      :							List[str]				,							):
           if kwargs.get('set_alpha_to_one'				,							SCREAMING_SNAKE_CASE) is not None:
                  _A					:     Tuple     = (
                      'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
                  )
                  deprecate('set_alpha_to_one'				,							'1.0.0'				,							SCREAMING_SNAKE_CASE				,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A					:     Tuple     = kwargs['set_alpha_to_one']
           if trained_betas is not None:
                  _A					:     Any     = torch.tensor(SCREAMING_SNAKE_CASE				,							dtype=torch.floataa)
           elif beta_schedule == "linear":
                  _A					:     List[Any]     = torch.linspace(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							dtype=torch.floataa)
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  _A					:     List[str]     = (
                      torch.linspace(beta_start**0.5				,							beta_end**0.5				,							SCREAMING_SNAKE_CASE				,							dtype=torch.floataa) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  _A					:     List[Any]     = betas_for_alpha_bar(SCREAMING_SNAKE_CASE)
           else:
                  raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

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

           # At every step in inverted ddim, we are looking into the next alphas_cumprod
           # For the final step, there is no next alphas_cumprod, and the index is out of bounds
           # `set_alpha_to_zero` decides whether we set this parameter simply to zero
           # in this case, self.step() just output the predicted noise
           # or whether we use the final alpha of the "non-previous" one.
           _A					:     Optional[int]     = torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

           # standard deviation of the initial noise distribution
           _A					:     Union[str, Any]     = 1.0

           # setable values
           _A					:     List[str]     = None
           _A					:     Dict     = torch.from_numpy(np.arange(0				,							SCREAMING_SNAKE_CASE).copy().astype(np.intaa))


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None):
           return sample


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, torch.device] = None):

           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.')

           _A					:     Optional[Any]     = num_inference_steps
           _A					:     List[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
           _A					:     List[str]     = (np.arange(0				,							SCREAMING_SNAKE_CASE) * step_ratio).round().copy().astype(np.intaa)
           _A					:     int     = torch.from_numpy(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
           self.timesteps += self.config.steps_offset


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							float = 0.0				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[torch.FloatTensor] = None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           # 1. get previous step value (=t+1)
           _A					:     Union[str, Any]     = timestep + self.config.num_train_timesteps // self.num_inference_steps

           # 2. compute alphas, betas
           # change original implementation to exactly match noise levels for analogous forward process
           _A					:     List[str]     = self.alphas_cumprod[timestep]
           _A					:     List[str]     = (
               self.alphas_cumprod[prev_timestep]
               if prev_timestep < self.config.num_train_timesteps
               else self.final_alpha_cumprod
           )

           _A					:     List[str]     = 1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                  _A					:     Any     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                  _A					:     List[Any]     = model_output
           elif self.config.prediction_type == "sample":
                  _A					:     List[Any]     = model_output
                  _A					:     Dict     = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                  _A					:     List[str]     = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                  _A					:     Optional[int]     = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                  raise ValueError(
                      F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                      ' `v_prediction`')

           # 4. Clip or threshold "predicted x_0"
           if self.config.clip_sample:
                  _A					:     str     = pred_original_sample.clamp(
                      -self.config.clip_sample_range				,							self.config.clip_sample_range)

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _A					:     Any     = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           _A					:     Tuple     = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                  return (prev_sample, pred_original_sample)
           return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE				,							pred_original_sample=SCREAMING_SNAKE_CASE)


    def __len__(  self      :							List[Any]):
           return self.config.num_train_timesteps






