

from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase_             =     8
def   a   (    A__			:     Optional[int]   ,     A__			:     int=BITS  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =x.device

      _lowercase			 =(x * 255).int().clamp(0   ,     255  )

      _lowercase			 =2 ** torch.arange(bits - 1   ,     -1   ,     -1   ,     device=A__  )
      _lowercase			 =rearrange(A__   ,     'd -> d 1 1'  )
      _lowercase			 =rearrange(A__   ,     'b c h w -> b c 1 h w'  )

      _lowercase			 =((x & mask) != 0).float()
      _lowercase			 =rearrange(A__   ,     'b c d h w -> b (c d) h w'  )
      _lowercase			 =bits * 2 - 1
      return bits
def   a   (    A__			:     List[Any]   ,     A__			:     int=BITS  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =x.device

      _lowercase			 =(x > 0).int()
      _lowercase			 =2 ** torch.arange(bits - 1   ,     -1   ,     -1   ,     device=A__   ,     dtype=torch.intaa  )

      _lowercase			 =rearrange(A__   ,     'd -> d 1 1'  )
      _lowercase			 =rearrange(A__   ,     'b (c d) h w -> b c d h w'   ,     d=8  )
      _lowercase			 =reduce(x * mask   ,     'b c d h w -> b c h w'   ,     'sum'  )
      return (dec / 255).clamp(0.0   ,     1.0  )
def   a   (    self			:     Optional[Any]   ,     A__			:     torch.FloatTensor   ,     A__			:     int   ,     A__			:     torch.FloatTensor   ,     A__			:     float = 0.0   ,     A__			:     bool = True   ,     A__			:     int=None   ,     A__			:     bool = True   ,     )					->   Union[DDIMSchedulerOutput, Tuple]:





      """simple docstring"""



      if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      _lowercase			 =timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      _lowercase			 =self.alphas_cumprod[timestep]
      _lowercase			 =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      _lowercase			 =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
      _lowercase			 =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      _lowercase			 =self.bit_scale
      if self.config.clip_sample:
            _lowercase			 =torch.clamp(A__   ,     -scale   ,     A__  )

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      _lowercase			 =self._get_variance(A__   ,     A__  )
      _lowercase			 =eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            _lowercase			 =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowercase			 =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

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

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            _lowercase			 =model_output.device if torch.is_tensor(A__  ) else 'cpu'
            _lowercase			 =torch.randn(model_output.shape   ,     dtype=model_output.dtype   ,     generator=A__  ).to(A__  )
            _lowercase			 =self._get_variance(A__   ,     A__  ) ** 0.5 * eta * noise

            _lowercase			 =prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=A__   ,     pred_original_sample=A__  )
def   a   (    self			:     Any   ,     A__			:     torch.FloatTensor   ,     A__			:     int   ,     A__			:     torch.FloatTensor   ,     A__			:     Tuple="epsilon"   ,     A__			:     Any=None   ,     A__			:     bool = True   ,     )					->   Union[DDPMSchedulerOutput, Tuple]:





      """simple docstring"""



      _lowercase			 =timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            _lowercase			 =torch.split(A__   ,     sample.shape[1]   ,     dim=1  )
      else:
            _lowercase			 =None

      # 1. compute alphas, betas
      _lowercase			 =self.alphas_cumprod[t]
      _lowercase			 =self.alphas_cumprod[t - 1] if t > 0 else self.one
      _lowercase			 =1 - alpha_prod_t
      _lowercase			 =1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if prediction_type == "epsilon":
            _lowercase			 =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            _lowercase			 =model_output
      else:
            raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''  )

      # 3. Clip "predicted x_0"
      _lowercase			 =self.bit_scale
      if self.config.clip_sample:
            _lowercase			 =torch.clamp(A__   ,     -scale   ,     A__  )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _lowercase			 =(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      _lowercase			 =self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _lowercase			 =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      _lowercase			 =0
      if t > 0:
            _lowercase			 =torch.randn(
                model_output.size()   ,     dtype=model_output.dtype   ,     layout=model_output.layout   ,     generator=A__  ).to(model_output.device  )
            _lowercase			 =(self._get_variance(A__   ,     predicted_variance=A__  ) ** 0.5) * noise

      _lowercase			 =pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=A__   ,     pred_original_sample=A__  )







class 				__lowerCAmelCase  (      UpperCAmelCase_							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase = 1.0							,     )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase			 =bit_scale
        _lowercase			 =(
            ddim_bit_scheduler_step if isinstance(_lowercase							,     _lowercase      ) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=_lowercase							,     scheduler=_lowercase      )




  @torch.no_grad()
  def __call__(       self							,     lowerCAmelCase = 256							,     lowerCAmelCase = 256							,     lowerCAmelCase = 50							,     lowerCAmelCase = None							,     lowerCAmelCase = 1							,     lowerCAmelCase = "pil"							,     lowerCAmelCase = True							,     **lowerCAmelCase							,     )						->				Union[Tuple, ImagePipelineOutput]:
        '''simple docstring'''






        _lowercase			 =torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)							,     generator=_lowercase							,     )
        _lowercase			 =decimal_to_bits(_lowercase      ) * self.bit_scale
        _lowercase			 =latents.to(self.device      )

        self.scheduler.set_timesteps(_lowercase      )

        for t in self.progress_bar(self.scheduler.timesteps      ):
              # predict the noise residual
              _lowercase			 =self.unet(_lowercase							,     _lowercase      ).sample

              # compute the previous noisy sample x_t -> x_t-1
              _lowercase			 =self.scheduler.step(_lowercase							,     _lowercase							,     _lowercase      ).prev_sample

        _lowercase			 =bits_to_decimal(_lowercase      )

        if output_type == "pil":
              _lowercase			 =self.numpy_to_pil(_lowercase      )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=_lowercase      )





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=7							,     lowerCAmelCase=3							,     lowerCAmelCase=18							,     lowerCAmelCase=30							,     lowerCAmelCase=400							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=False							,     )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =size if size is not None else {'height': 20, 'width': 20}
        _lowercase			 =crop_size if crop_size is not None else {'height': 18, 'width': 18}
        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =num_channels
        _lowercase			 =image_size
        _lowercase			 =min_resolution
        _lowercase			 =max_resolution
        _lowercase			 =do_resize
        _lowercase			 =size
        _lowercase			 =do_center_crop
        _lowercase			 =crop_size
        _lowercase			 =do_normalize
        _lowercase			 =image_mean
        _lowercase			 =image_std
        _lowercase			 =do_reduce_labels




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }
def   a   (    )					->   Tuple:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(dataset[0]['file']  )
      _lowercase			 =Image.open(dataset[1]['file']  )

      return image, map
def   a   (    )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(ds[0]['file']  )
      _lowercase			 =Image.open(ds[1]['file']  )
      _lowercase			 =Image.open(ds[2]['file']  )
      _lowercase			 =Image.open(ds[3]['file']  )

      return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						BeitImageProcessor if is_vision_available() else None




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =BeitImageProcessingTester(self      )




  @property
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_resize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'size'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_normalize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_mean'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_std'      )      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size							,     {'height': 20, 'width': 20}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 18, 'width': 18}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )

        _lowercase			 =self.image_processing_class.from_dict(
            self.image_processor_dict							,     size=42							,     crop_size=84							,     reduce_labels=lowerCAmelCase      )
        self.assertEqual(image_processor.size							,     {'height': 42, 'width': 42}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 84, 'width': 84}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     Image.Image      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     numpify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     np.ndarray      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        _lowercase			 =[]
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )
              maps.append(torch.zeros(image.shape[-2:]      ).long()      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test not batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_batch_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                2,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 150      )

        _lowercase			 =True
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )


