



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       snake_case__(_a			):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		None






def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__=0.999     , lowerCamelCase__="cosine"     , ):

				"""simple docstring"""


				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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

class       snake_case__(_a				,			_a			):

					"""simple docstring"""






					@register_to_config
					def __init__(							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	int = 1_000      ,   SCREAMING_SNAKE_CASE					:	str = "fixed_small_log"      ,   SCREAMING_SNAKE_CASE					:	bool = True      ,   SCREAMING_SNAKE_CASE					:	Optional[float] = 1.0      ,   SCREAMING_SNAKE_CASE					:	str = "epsilon"      ,   SCREAMING_SNAKE_CASE					:	str = "squaredcos_cap_v2"      ,   ):
									if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"     )

									lowercase__     :    Tuple													=     betas_for_alpha_bar(__UpperCAmelCase     )

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

									# standard deviation of the initial noise distribution
									lowercase__     :    str													=     1.0

									# setable values
									lowercase__     :    Dict													=     None
									lowercase__     :    Optional[Any]													=     torch.from_numpy(np.arange(0      ,   __UpperCAmelCase     )[::-1].copy()     )

									lowercase__     :    Optional[Any]													=     variance_type







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







					def  snake_case     (							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	int      ,   SCREAMING_SNAKE_CASE					:	Union[str, torch.device] = None     ):
									lowercase__     :    int													=     num_inference_steps
									lowercase__     :    str													=     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
									lowercase__     :    Optional[int]													=     (np.arange(0      ,   __UpperCAmelCase     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
									lowercase__     :    List[Any]													=     torch.from_numpy(__UpperCAmelCase     ).to(__UpperCAmelCase     )







					def  snake_case     (							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	Optional[int]      ,   SCREAMING_SNAKE_CASE					:	str=None      ,   SCREAMING_SNAKE_CASE					:	Optional[int]=None      ,   SCREAMING_SNAKE_CASE					:	str=None     ):
									if prev_timestep is None:
													lowercase__     :    Union[str, Any]													=     t - 1

									lowercase__     :    str													=     self.alphas_cumprod[t]
									lowercase__     :    str													=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									lowercase__     :    int													=     1 - alpha_prod_t
									lowercase__     :    Union[str, Any]													=     1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
													lowercase__     :    Optional[Any]													=     self.betas[t]
									else:
													lowercase__     :    Any													=     1 - alpha_prod_t / alpha_prod_t_prev

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									lowercase__     :    List[str]													=     beta_prod_t_prev / beta_prod_t * beta

									if variance_type is None:
													lowercase__     :    Any													=     self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small_log":
													lowercase__     :    Union[str, Any]													=     torch.log(torch.clamp(__UpperCAmelCase      ,   min=1E-2_0     )     )
													lowercase__     :    Any													=     torch.exp(0.5 * variance     )
									elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													lowercase__     :    List[str]													=     variance.log()
													lowercase__     :    Union[str, Any]													=     beta.log()

													lowercase__     :    Any													=     (predicted_variance + 1) / 2
													lowercase__     :    Tuple													=     frac * max_log + (1 - frac) * min_log

									return variance







					def  snake_case     (							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	torch.FloatTensor      ,   SCREAMING_SNAKE_CASE					:	int      ,   SCREAMING_SNAKE_CASE					:	torch.FloatTensor      ,   SCREAMING_SNAKE_CASE					:	Optional[int] = None      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]=None      ,   SCREAMING_SNAKE_CASE					:	bool = True      ,   ):
									lowercase__     :    Any													=     timestep

									if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
													lowercase__     :    List[Any]													=     torch.split(__UpperCAmelCase      ,   sample.shape[1]      ,   dim=1     )
									else:
													lowercase__     :    List[Any]													=     None

									# 1. compute alphas, betas
									if prev_timestep is None:
													lowercase__     :    int													=     t - 1

									lowercase__     :    Optional[Any]													=     self.alphas_cumprod[t]
									lowercase__     :    Union[str, Any]													=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									lowercase__     :    List[Any]													=     1 - alpha_prod_t
									lowercase__     :    str													=     1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
													lowercase__     :    str													=     self.betas[t]
													lowercase__     :    Optional[int]													=     self.alphas[t]
									else:
													lowercase__     :    List[Any]													=     1 - alpha_prod_t / alpha_prod_t_prev
													lowercase__     :    Optional[Any]													=     1 - beta

									# 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 self.config.prediction_type == "epsilon":
													lowercase__     :    Union[str, Any]													=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
													lowercase__     :    Optional[int]													=     model_output
									else:
													raise ValueError(
													    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
													    " for the UnCLIPScheduler."     )

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
													lowercase__     :    List[str]													=     torch.clamp(
													    __UpperCAmelCase      ,   -self.config.clip_sample_range      ,   self.config.clip_sample_range     )

									# 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__     :    Union[str, Any]													=     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
									lowercase__     :    List[Any]													=     alpha ** 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__     :    Tuple													=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									lowercase__     :    Optional[Any]													=     0
									if t > 0:
													lowercase__     :    List[Any]													=     randn_tensor(
													    model_output.shape      ,   dtype=model_output.dtype      ,   generator=__UpperCAmelCase      ,   device=model_output.device     )

													lowercase__     :    Any													=     self._get_variance(
													    __UpperCAmelCase      ,   predicted_variance=__UpperCAmelCase      ,   prev_timestep=__UpperCAmelCase      ,   )

													if self.variance_type == "fixed_small_log":
																	lowercase__     :    int													=     variance
													elif self.variance_type == "learned_range":
																	lowercase__     :    Any													=     (0.5 * variance).exp()
													else:
																	raise ValueError(
																	    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																	    " for the UnCLIPScheduler."     )

													lowercase__     :    List[str]													=     variance * variance_noise

									lowercase__     :    List[Any]													=     pred_prev_sample + variance

									if not return_dict:
													return (pred_prev_sample,)

									return UnCLIPSchedulerOutput(prev_sample=__UpperCAmelCase      ,   pred_original_sample=__UpperCAmelCase     )







					def  snake_case     (							self					:	int      ,   SCREAMING_SNAKE_CASE					:	torch.FloatTensor      ,   SCREAMING_SNAKE_CASE					:	torch.FloatTensor      ,   SCREAMING_SNAKE_CASE					:	torch.IntTensor      ,   ):
									# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
									lowercase__     :    Dict													=     self.alphas_cumprod.to(device=original_samples.device      ,   dtype=original_samples.dtype     )
									lowercase__     :    Union[str, Any]													=     timesteps.to(original_samples.device     )

									lowercase__     :    int													=     alphas_cumprod[timesteps] ** 0.5
									lowercase__     :    Optional[Any]													=     sqrt_alpha_prod.flatten()
									while len(sqrt_alpha_prod.shape     ) < len(original_samples.shape     ):
													lowercase__     :    Any													=     sqrt_alpha_prod.unsqueeze(-1     )

									lowercase__     :    str													=     (1 - alphas_cumprod[timesteps]) ** 0.5
									lowercase__     :    Tuple													=     sqrt_one_minus_alpha_prod.flatten()
									while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
													lowercase__     :    Optional[int]													=     sqrt_one_minus_alpha_prod.unsqueeze(-1     )

									lowercase__     :    List[Any]													=     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
									return noisy_samples








"""simple docstring"""







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 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       KandinskyVaaControlnetPipeline
    UpperCAmelCase       :							List[str]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Optional[Any]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Dict	=       [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase       :							Optional[int]	=       False






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return self.time_input_dim






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






    @property
    def 	__snake_case							(					self				:    str):
      return 100






    @property
    def 	__snake_case							(					self				:    str):
      torch.manual_seed(0)

      a						:		str      							=			{
          "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,
      }

      a						:		Dict      							=			UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def 	__snake_case							(					self				:    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 	__snake_case							(					self				:    Union[str, Any]):
      torch.manual_seed(0)
      a						:		Dict      							=			VQModel(**self.dummy_movq_kwargs)
      return model






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Optional[Any]      							=			self.dummy_unet
      a						:		int      							=			self.dummy_movq

      a						:		str      							=			DDIMScheduler(
          num_train_timesteps=1000				, beta_schedule="linear"				, beta_start=0.00_085				, beta_end=0.012				, clip_sample=__UpperCAmelCase				, set_alpha_to_one=__UpperCAmelCase				, steps_offset=1				, prediction_type="epsilon"				, thresholding=__UpperCAmelCase				, )

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    int=0):
      a						:		Optional[int]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)

      # create hint
      a						:		Union[str, Any]      							=			floats_tensor((1, 3, 64, 64)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[Any]      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		str      							=			{
          "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 	__snake_case							(					self				:    Dict):
      a						:		str      							=			"cpu"

      a						:		Tuple      							=			self.get_dummy_components()

      a						:		Dict      							=			self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[int]      							=			pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]      							=			pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Any      							=			output.images

      a						:		Any      							=			pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)				, return_dict=__UpperCAmelCase				, )[0]

      a						:		Union[str, Any]      							=			image[0, -3:, -3:, -1]
      a						:		int      							=			image_from_tuple[0, -3:, -3:, -1]

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

      a						:		Tuple      							=			np.array(
          [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])

      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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[int]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






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

      a						:		Union[str, Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png")
      a						:		List[Any]      							=			torch.from_numpy(np.array(__UpperCAmelCase)).float() / 255.0
      a						:		str      							=			hint.permute(2				, 0				, 1).unsqueeze(0)

      a						:		Optional[int]      							=			KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"				, torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		List[str]      							=			KandinskyVaaControlnetPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"				, torch_dtype=torch.floataa)
      a						:		int      							=			pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Tuple      							=			"A robot, 4k photo"

      a						:		Any      							=			torch.Generator(device="cuda").manual_seed(0)
      a							,		a						:		int      							=			pipe_prior(
          __UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=5				, negative_prompt=""				, ).to_tuple()

      a						:		str      							=			torch.Generator(device="cuda").manual_seed(0)
      a						:		Union[str, Any]      							=			pipeline(
          image_embeds=__UpperCAmelCase				, negative_image_embeds=__UpperCAmelCase				, hint=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=100				, output_type="np"				, )

      a						:		str      							=			output.images[0]

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

      assert_mean_pixel_difference(__UpperCAmelCase				, __UpperCAmelCase)






