






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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_				(      UpperCAmelCase__			,				unittest.TestCase ):
				a__					       =  KandinskyVaaPipeline
				a__					       =  [
				    "image_embeds",
				    "negative_image_embeds",
				]
				a__					       =  ["image_embeds", "negative_image_embeds"]
				a__					       =  [
				    "generator",
				    "height",
				    "width",
				    "latents",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				a__					       =  False







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 3_2







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 3_2







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.time_input_dim







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.time_input_dim * 4







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return 1_0_0







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )

								__magic_name__		:Any        =      {
								    "in_channels": 4,
								    # 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,
								}

								__magic_name__		:int        =      UNetaDConditionModel(**lowerCamelCase__     )
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Any        =      VQModel(**self.dummy_movq_kwargs     )
								return model







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      self.dummy_unet
								__magic_name__		:List[Any]        =      self.dummy_movq

								__magic_name__		:int        =      DDIMScheduler(
								    num_train_timesteps=1_0_0_0       ,					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__       ,					)

								__magic_name__		:Union[str, Any]        =      {
								    "unet": unet,
								    "scheduler": scheduler,
								    "movq": movq,
								}
								return components







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=0     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
								__magic_name__		:Any        =      floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1     )     ).to(
								    lowerCamelCase__     )
								if str(lowerCamelCase__     ).startswith('''mps'''     ):
												__magic_name__		:Tuple        =      torch.manual_seed(lowerCamelCase__     )
								else:
												__magic_name__		:Any        =      torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )
								__magic_name__		:Dict        =      {
								    "image_embeds": image_embeds,
								    "negative_image_embeds": negative_image_embeds,
								    "generator": generator,
								    "height": 6_4,
								    "width": 6_4,
								    "guidance_scale": 4.0,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}
								return inputs







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      "cpu"

								__magic_name__		:Tuple        =      self.get_dummy_components()

								__magic_name__		:List[Any]        =      self.pipeline_class(**lowerCamelCase__     )
								__magic_name__		:List[Any]        =      pipe.to(lowerCamelCase__     )

								pipe.set_progress_bar_config(disable=lowerCamelCase__     )

								__magic_name__		:List[Any]        =      pipe(**self.get_dummy_inputs(lowerCamelCase__     )     )
								__magic_name__		:Union[str, Any]        =      output.images

								__magic_name__		:List[Any]        =      pipe(
								    **self.get_dummy_inputs(lowerCamelCase__     )       ,					return_dict=lowerCamelCase__       ,					)[0]

								__magic_name__		:Optional[int]        =      image[0, -3:, -3:, -1]
								__magic_name__		:Any        =      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								__magic_name__		:Tuple        =      np.array(
								    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]     )

								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 ):







				def 			A	(						self     ):
								"""simple docstring"""
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

								__magic_name__		:List[Any]        =      KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''       ,					torch_dtype=torch.floataa     )
								pipe_prior.to(lowerCamelCase__     )

								__magic_name__		:List[Any]        =      KandinskyVaaPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder'''       ,					torch_dtype=torch.floataa     )
								__magic_name__		:Optional[int]        =      pipeline.to(lowerCamelCase__     )
								pipeline.set_progress_bar_config(disable=lowerCamelCase__     )

								__magic_name__		:Tuple        =      "red cat, 4k photo"

								__magic_name__		:List[str]        =      torch.Generator(device='''cuda'''     ).manual_seed(0     )
								__magic_name__		:List[str]        =      pipe_prior(
								    lowerCamelCase__       ,					generator=lowerCamelCase__       ,					num_inference_steps=5       ,					negative_prompt=''''''       ,					).to_tuple()

								__magic_name__		:str        =      torch.Generator(device='''cuda'''     ).manual_seed(0     )
								__magic_name__		:str        =      pipeline(
								    image_embeds=lowerCamelCase__       ,					negative_image_embeds=lowerCamelCase__       ,					generator=lowerCamelCase__       ,					num_inference_steps=1_0_0       ,					output_type='''np'''       ,					)

								__magic_name__		:Optional[Any]        =      output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(lowerCamelCase__       ,					lowerCamelCase__     )














# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						lowerCamelCase_				:
				a__					       =  42

				# setable values
				a__					       =  42
				a__					       =  42
				a__					       =  None







				@classmethod
				def 			A	(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return cls(common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







@dataclass
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  42







class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

				a__					       =  42







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return True







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase = 1_0_0_0       ,					__lowerCAmelCase = 0.0001       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase = "linear"       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "fixed_small"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "epsilon"       ,					__lowerCAmelCase = jnp.floataa       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      dtype







				def 			A	(						self       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if common is None:
												__magic_name__		:Dict        =      CommonSchedulerState.create(self     )

								# standard deviation of the initial noise distribution
								__magic_name__		:Optional[Any]        =      jnp.array(1.0       ,					dtype=self.dtype     )

								__magic_name__		:str        =      jnp.arange(0       ,					self.config.num_train_timesteps     ).round()[::-1]

								return DDPMSchedulerState.create(
								    common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								return sample







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = ()     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								__magic_name__		:List[Any]        =      (jnp.arange(0       ,					__lowerCAmelCase     ) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      state.common.alphas_cumprod[t]
								__magic_name__		:Optional[Any]        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )

								# 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
								__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												__magic_name__		:Optional[Any]        =      self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
												__magic_name__		:Optional[Any]        =      jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
												__magic_name__		:Dict        =      jnp.log(jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )     )
								elif variance_type == "fixed_large":
												__magic_name__		:Tuple        =      state.common.betas[t]
								elif variance_type == "fixed_large_log":
												# Glide max_log
												__magic_name__		:Optional[Any]        =      jnp.log(state.common.betas[t]     )
								elif variance_type == "learned":
												return predicted_variance
								elif variance_type == "learned_range":
												__magic_name__		:Union[str, Any]        =      variance
												__magic_name__		:List[str]        =      state.common.betas[t]
												__magic_name__		:Any        =      (predicted_variance + 1) / 2
												__magic_name__		:str        =      frac * max_log + (1 - frac) * min_log

								return variance







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      timestep

								if key is None:
												__magic_name__		:Union[str, Any]        =      jax.random.PRNGKey(0     )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												__magic_name__     ,				__magic_name__		:Dict        =      jnp.split(__lowerCAmelCase       ,					sample.shape[1]       ,					axis=1     )
								else:
												__magic_name__		:Optional[int]        =      None

								# 1. compute alphas, betas
								__magic_name__		:Any        =      state.common.alphas_cumprod[t]
								__magic_name__		:int        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )
								__magic_name__		:Optional[int]        =      1 - alpha_prod_t
								__magic_name__		:Union[str, Any]        =      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 self.config.prediction_type == "epsilon":
												__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
												__magic_name__		:Tuple        =      model_output
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Dict        =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
												    ''' for the FlaxDDPMScheduler.'''     )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												__magic_name__		:Union[str, Any]        =      jnp.clip(__lowerCAmelCase       ,					-1       ,					1     )

								# 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
								__magic_name__		:Union[str, Any]        =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								__magic_name__		:Union[str, Any]        =      state.common.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
								__magic_name__		:Union[str, Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
												__magic_name__		:Tuple        =      jax.random.split(__lowerCAmelCase       ,					num=1     )
												__magic_name__		:Dict        =      jax.random.normal(__lowerCAmelCase       ,					shape=model_output.shape       ,					dtype=self.dtype     )
												return (self._get_variance(__lowerCAmelCase       ,					__lowerCAmelCase       ,					predicted_variance=__lowerCAmelCase     ) ** 0.5) * noise

								__magic_name__		:List[str]        =      jnp.where(t > 0       ,					random_variance()       ,					jnp.zeros(model_output.shape       ,					dtype=self.dtype     )     )

								__magic_name__		:int        =      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,					state=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return add_noise_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return get_velocity_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps






