



'''simple docstring'''


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


_lowerCamelCase	:       List[Any]      	=	8







def 				__lowerCamelCase				(   A__    ,						A__=BITS    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  x.device

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

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

   UpperCamelCase					=  ((x & mask) != 0).float()
   UpperCamelCase					=  rearrange(__UpperCamelCase    ,						'b c d h w -> b (c d) h w'    )
   UpperCamelCase					=  bits * 2 - 1
   return bits







def 				__lowerCamelCase				(   A__    ,						A__=BITS    )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  x.device

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

   UpperCamelCase					=  rearrange(__UpperCamelCase    ,						'd -> d 1 1'    )
   UpperCamelCase					=  rearrange(__UpperCamelCase    ,						'b (c d) h w -> b c d h w'    ,						d=8    )
   UpperCamelCase					=  reduce(x * mask    ,						'b c d h w -> b c h w'    ,						'sum'    )
   return (dec / 255).clamp(0.0    ,						1.0    )







def 				__lowerCamelCase				(   self    ,						A__    ,						A__    ,						A__    ,						A__ = 0.0    ,						A__ = True    ,						A__=None    ,						A__ = True    ,						)       ->	Optional[int]:






   """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)
   UpperCamelCase					=  timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

   # 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)
   UpperCamelCase					=  self._get_variance(__UpperCamelCase    ,						__UpperCamelCase    )
   UpperCamelCase					=  eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      UpperCamelCase					=  (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
   UpperCamelCase					=  (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
   UpperCamelCase					=  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
      UpperCamelCase					=  model_output.device if torch.is_tensor(__UpperCamelCase    ) else 'cpu'
      UpperCamelCase					=  torch.randn(model_output.shape    ,						dtype=model_output.dtype    ,						generator=__UpperCamelCase    ).to(__UpperCamelCase    )
      UpperCamelCase					=  self._get_variance(__UpperCamelCase    ,						__UpperCamelCase    ) ** 0.5 * eta * noise

      UpperCamelCase					=  prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=__UpperCamelCase    ,						pred_original_sample=__UpperCamelCase    )







def 				__lowerCamelCase				(   self    ,						A__    ,						A__    ,						A__    ,						A__="epsilon"    ,						A__=None    ,						A__ = True    ,						)       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  timestep

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

   # 1. compute alphas, betas
   UpperCamelCase					=  self.alphas_cumprod[t]
   UpperCamelCase					=  self.alphas_cumprod[t - 1] if t > 0 else self.one
   UpperCamelCase					=  1 - alpha_prod_t
   UpperCamelCase					=  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":
      UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      UpperCamelCase					=  model_output
   else:
      raise ValueError(F"""Unsupported prediction_type {prediction_type}."""    )

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

   # 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
   UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   UpperCamelCase					=  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
   UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

   UpperCamelCase					=  pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=__UpperCamelCase    ,						pred_original_sample=__UpperCamelCase    )

class  SCREAMING_SNAKE_CASE (  SCREAMING_SNAKE_CASE__		):






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					UNetaDConditionModel  ,						UpperCamelCase__      :					Union[DDIMScheduler, DDPMScheduler]  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						):


       """simple docstring"""







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

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






    @torch.no_grad()
    def __call__(  self      :					List[Any]  ,						UpperCamelCase__      :					Optional[int] = 2_5_6  ,						UpperCamelCase__      :					Optional[int] = 2_5_6  ,						UpperCamelCase__      :					Optional[int] = 5_0  ,						UpperCamelCase__      :					Optional[torch.Generator] = None  ,						UpperCamelCase__      :					Optional[int] = 1  ,						UpperCamelCase__      :					Optional[str] = "pil"  ,						UpperCamelCase__      :					bool = True  ,						**UpperCamelCase__      :					int  ,						):


       """simple docstring"""







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

       self.scheduler.set_timesteps(UpperCamelCase__ )

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

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

       UpperCamelCase					=  bits_to_decimal(UpperCamelCase__ )

       if output_type == "pil":
          UpperCamelCase					=  self.numpy_to_pil(UpperCamelCase__ )

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__ )


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        SCREAMING_SNAKE_CASE_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        assert (
            np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any      )     ->		Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


