


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      KandinskyVaaInpaintPipeline
     _lowerCamelCase   =      ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     _lowerCamelCase   =      [
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     _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 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim * 4


     @property
     def 	snake_case_  (	self    ):
        return 100


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )

        __a					=  {
            """in_channels""": 9,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image""",
            """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,
        }

        __a					=  UNetaDConditionModel(**__lowerCAmelCase    )
        return model


     @property
     def 	snake_case_  (	self    ):
        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["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",
            ],
            "vq_embed_dim": 4,
        }


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  VQModel(**self.dummy_movq_kwargs    )
        return model


     def 	snake_case_  (	self    ):
        __a					=  self.dummy_unet
        __a					=  self.dummy_movq

        __a					=  DDIMScheduler(
            num_train_timesteps=1000  ,       beta_schedule="""linear"""  ,       beta_start=0.00085  ,       beta_end=0.012  ,       clip_sample=__lowerCAmelCase  ,       set_alpha_to_one=__lowerCAmelCase  ,       steps_offset=1  ,       prediction_type="""epsilon"""  ,       thresholding=__lowerCAmelCase  ,       )

        __a					=  {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        __a					=  floats_tensor((1, self.text_embedder_hidden_size)  ,       rng=random.Random(__lowerCAmelCase    )    ).to(__lowerCAmelCase    )
        __a					=  floats_tensor((1, self.text_embedder_hidden_size)  ,       rng=random.Random(seed + 1    )    ).to(
            __lowerCAmelCase    )
        # create init_image
        __a					=  floats_tensor((1, 3, 64, 64)  ,       rng=random.Random(__lowerCAmelCase    )    ).to(__lowerCAmelCase    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__lowerCAmelCase    )    ).convert("""RGB"""    ).resize((256, 256)    )
        # create mask
        __a					=  np.ones((64, 64)  ,       dtype=np.floataa    )
        __a					=  0

        if str(__lowerCAmelCase    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__lowerCAmelCase    )
        else:
           __a					=  torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
        __a					=  {
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs


     def 	snake_case_  (	self    ):
        __a					=  """cpu"""

        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__lowerCAmelCase    )
        __a					=  pipe.to(__lowerCAmelCase    )

        pipe.set_progress_bar_config(disable=__lowerCAmelCase    )

        __a					=  pipe(**self.get_dummy_inputs(__lowerCAmelCase    )    )
        __a					=  output.images

        __a					=  pipe(
            **self.get_dummy_inputs(__lowerCAmelCase    )  ,       return_dict=__lowerCAmelCase  ,       )[0]

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

        print(f'''image.shape {image.shape}'''    )

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

        __a					=  np.array(
            [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

        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()}'''


     def 	snake_case_  (	self    ):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3    )







@slow
@require_torch_gpu
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self    ):
        __a					=  load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""    )

        __a					=  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
        __a					=  np.ones((768, 768)  ,       dtype=np.floataa    )
        __a					=  0

        __a					=  """a hat"""

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

        __a					=  KandinskyVaaInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-decoder-inpaint"""  ,       torch_dtype=torch.floataa    )
        __a					=  pipeline.to(__lowerCAmelCase    )
        pipeline.set_progress_bar_config(disable=__lowerCAmelCase    )

        __a					=  torch.Generator(device="""cpu"""    ).manual_seed(0    )
        __a	,    __a					=  pipe_prior(
            __lowerCAmelCase  ,       generator=__lowerCAmelCase  ,       num_inference_steps=5  ,       negative_prompt=""""""  ,       ).to_tuple()

        __a					=  pipeline(
            image=__lowerCAmelCase  ,       mask_image=__lowerCAmelCase  ,       image_embeds=__lowerCAmelCase  ,       negative_image_embeds=__lowerCAmelCase  ,       generator=__lowerCAmelCase  ,       num_inference_steps=100  ,       height=768  ,       width=768  ,       output_type="""np"""  ,       )

        __a					=  output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowerCAmelCase  ,       __lowerCAmelCase    )




"""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       :			Tuple   	=8


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int=BITS						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x * 2_5_5).int().clamp(0   ,						2_5_5						)

      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c h w -> b c 1 h w"""						)

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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int=BITS						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      x.device

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

      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""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 / 2_5_5).clamp(0.0   ,						1.0						)


def      UpperCAmelCase__			(							self	:Union[str, Any]   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:float = 0.0   ,						lowerCAmelCase__	:bool = True   ,						lowerCAmelCase__	:Optional[Any]=None   ,						lowerCAmelCase__	: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(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 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(lowerCAmelCase__   ,						lowerCAmelCase__						)
      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(lowerCAmelCase__						) else """cpu"""
            lowercase								=      torch.randn(model_output.shape   ,						dtype=model_output.dtype   ,						generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
            lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						) ** 0.5 * eta * noise

            lowercase								=      prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)


def      UpperCAmelCase__			(							self	:Any   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:List[str]="epsilon"   ,						lowerCAmelCase__	:Optional[int]=None   ,						lowerCAmelCase__	: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				,				lowercase								=      torch.split(lowerCAmelCase__   ,						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(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 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=lowerCAmelCase__						).to(model_output.device						)
            lowercase								=      (self._get_variance(lowerCAmelCase__   ,						predicted_variance=lowerCAmelCase__						) ** 0.5) * noise

      lowercase								=      pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)






class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 1.0						,			):







            """simple docstring"""



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

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







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 50						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



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

            self.scheduler.set_timesteps(__lowerCAmelCase		)

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

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

            lowercase								=      bits_to_decimal(__lowerCAmelCase		)

            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



