

"""simple docstring"""

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 			_snake_case     :
						__snake_case 			=     42
						__snake_case 			=     42

						# setable values
						__snake_case 			=     42
						__snake_case 			=     42
						__snake_case 			=     None







						@classmethod
						def        _UpperCamelCase    (cls     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						str:
											return cls(
											    common=UpperCAmelCase_     ,							final_alpha_cumprod=UpperCAmelCase_     ,							init_noise_sigma=UpperCAmelCase_     ,							timesteps=UpperCAmelCase_     ,							)



@dataclass
class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     42



class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase       ):
						__snake_case 			=     [e.name for e in FlaxKarrasDiffusionSchedulers]

						__snake_case 			=     42







						@property
						def        _UpperCamelCase    (self							)			->						Dict:
											return True







						@register_to_config
						def __init__(self     ,							UpperCAmelCase_ = 1_0_0_0     ,							UpperCAmelCase_ = 0.0_0_0_1     ,							UpperCAmelCase_ = 0.0_2     ,							UpperCAmelCase_ = "linear"     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = 0     ,							UpperCAmelCase_ = "epsilon"     ,							UpperCAmelCase_ = jnp.floataa     ,							)			->						Tuple:
											__lowercase												=			dtype







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = None							)			->						DDIMSchedulerState:
											if common is None:
																__lowercase												=			CommonSchedulerState.create(self							)

											# At every step in ddim, we are looking into the previous alphas_cumprod
											# For the final step, there is no previous alphas_cumprod because we are already at 0
											# `set_alpha_to_one` decides whether we set this parameter simply to one or
											# whether we use the final alpha of the "non-previous" one.
											__lowercase												=			(
											    jnp.array(1.0     ,							dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
											)

											# standard deviation of the initial noise distribution
											__lowercase												=			jnp.array(1.0     ,							dtype=self.dtype							)

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

											return DDIMSchedulerState.create(
											    common=UpperCAmelCase_     ,							final_alpha_cumprod=UpperCAmelCase_     ,							init_noise_sigma=UpperCAmelCase_     ,							timesteps=UpperCAmelCase_     ,							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None							)			->						jnp.ndarray:
											return sample







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = ()							)			->						DDIMSchedulerState:
											__lowercase												=			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
											__lowercase												=			(jnp.arange(0     ,							UpperCAmelCase_							) * step_ratio).round()[::-1] + self.config.steps_offset

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







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						int:
											__lowercase												=			state.common.alphas_cumprod[timestep]
											__lowercase												=			jnp.where(
											    prev_timestep >= 0     ,							state.common.alphas_cumprod[prev_timestep]     ,							state.final_alpha_cumprod							)
											__lowercase												=			1 - alpha_prod_t
											__lowercase												=			1 - alpha_prod_t_prev

											__lowercase												=			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = True     ,							)			->						Union[FlaxDDIMSchedulerOutput, Tuple]:
											if state.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 // state.num_inference_steps

											__lowercase												=			state.common.alphas_cumprod
											__lowercase												=			state.final_alpha_cumprod

											# 2. compute alphas, betas
											__lowercase												=			alphas_cumprod[timestep]
											__lowercase												=			jnp.where(prev_timestep >= 0     ,							alphas_cumprod[prev_timestep]     ,							UpperCAmelCase_							)

											__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
											if self.config.prediction_type == "epsilon":
																__lowercase												=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																__lowercase												=			model_output
											elif self.config.prediction_type == "sample":
																__lowercase												=			model_output
																__lowercase												=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																__lowercase												=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																__lowercase												=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
																    ''' `v_prediction`'''							)

											# 4. 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(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)
											__lowercase												=			eta * variance ** 0.5

											# 5. 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 * pred_epsilon

											# 6. 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 not return_dict:
																return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=UpperCAmelCase_     ,							state=UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						jnp.ndarray:
											return add_noise_common(state.common     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							)			->						jnp.ndarray:
											return get_velocity_common(state.common     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)







						def __len__(self							)			->						List[Any]:
											return self.config.num_train_timesteps





"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase						,			unittest.TestCase       ):
						__snake_case 			=     StableDiffusionModelEditingPipeline
						__snake_case 			=     TEXT_TO_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_BATCH_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS
						__snake_case 			=     TEXT_TO_IMAGE_IMAGE_PARAMS







						def        _UpperCamelCase    (self							)			->						int:
											torch.manual_seed(0							)
											__lowercase												=			UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)     ,							layers_per_block=2     ,							sample_size=3_2     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,							cross_attention_dim=3_2     ,							)
											__lowercase												=			DDIMScheduler()
											torch.manual_seed(0							)
											__lowercase												=			AutoencoderKL(
											    block_out_channels=[3_2, 6_4]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,							latent_channels=4     ,							)
											torch.manual_seed(0							)
											__lowercase												=			CLIPTextConfig(
											    bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=3_2     ,							intermediate_size=3_7     ,							layer_norm_eps=1e-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=1_0_0_0     ,							)
											__lowercase												=			CLIPTextModel(UpperCAmelCase_							)
											__lowercase												=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

											__lowercase												=			{
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_=0							)			->						List[str]:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    # Setting height and width to None to prevent OOMs on CPU.
											    '''height''': None,
											    '''width''': None,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			sd_pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1]
											assert image.shape == (1, 6_4, 6_4, 3)

											__lowercase												=			np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]							)

											assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2







						def        _UpperCamelCase    (self							)			->						Tuple:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			'''french fries'''
											__lowercase												=			sd_pipe(**UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_							)
											__lowercase												=			output.images
											__lowercase												=			image[0, -3:, -3:, -1]

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

											__lowercase												=			np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]							)

											assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2







						def        _UpperCamelCase    (self							)			->						Dict:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			EulerAncestralDiscreteScheduler(
											    beta_start=0.0_0_0_8_5     ,							beta_end=0.0_1_2     ,							beta_schedule='''scaled_linear'''							)
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											__lowercase												=			sd_pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1]

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

											__lowercase												=			np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]							)

											assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2







						def        _UpperCamelCase    (self							)			->						str:
											__lowercase												=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowercase												=			self.get_dummy_components()
											__lowercase												=			PNDMScheduler()
											__lowercase												=			StableDiffusionModelEditingPipeline(**UpperCAmelCase_							)
											__lowercase												=			sd_pipe.to(UpperCAmelCase_							)
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

											__lowercase												=			self.get_dummy_inputs(UpperCAmelCase_							)
											# the pipeline does not expect pndm so test if it raises error.
											with self.assertRaises(UpperCAmelCase_							):
																__lowercase												=			sd_pipe(**UpperCAmelCase_							).images







						def        _UpperCamelCase    (self							)			->						Tuple:
											super().test_inference_batch_single_identical(expected_max_diff=5e-3							)







						def        _UpperCamelCase    (self							)			->						str:
											super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)



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







						def        _UpperCamelCase    (self							)			->						int:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_=0							)			->						int:
											__lowercase												=			torch.manual_seed(UpperCAmelCase_							)
											__lowercase												=			{
											    '''prompt''': '''A field of roses''',
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											__lowercase												=			'''CompVis/stable-diffusion-v1-4'''
											__lowercase												=			StableDiffusionModelEditingPipeline.from_pretrained(UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_							)
											pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
											pipe.enable_attention_slicing()

											__lowercase												=			self.get_inputs()
											__lowercase												=			pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

											__lowercase												=			np.array(
											    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]							)

											assert np.abs(expected_slice - image_slice							).max() < 1e-2

											# make sure image changes after editing
											pipe.edit_model('''A pack of roses'''     ,							'''A pack of blue roses'''							)

											__lowercase												=			pipe(**UpperCAmelCase_							).images
											__lowercase												=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 5_1_2, 5_1_2, 3)

											assert np.abs(expected_slice - image_slice							).max() > 1e-1







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowercase												=			'''CompVis/stable-diffusion-v1-4'''
											__lowercase												=			DDIMScheduler.from_pretrained(UpperCAmelCase_     ,							subfolder='''scheduler'''							)
											__lowercase												=			StableDiffusionModelEditingPipeline.from_pretrained(
											    UpperCAmelCase_     ,							scheduler=UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_							)
											__lowercase												=			pipe.to(UpperCAmelCase_							)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
											pipe.enable_attention_slicing(1							)
											pipe.enable_sequential_cpu_offload()

											__lowercase												=			self.get_inputs()
											__lowercase												=			pipe(**UpperCAmelCase_							)

											__lowercase												=			torch.cuda.max_memory_allocated()
											# make sure that less than 4.4 GB is allocated
											assert mem_bytes < 4.4 * 1_0**9


