

# 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 _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =42

			# setable values
			lowerCamelCase__							     =42
			lowerCamelCase__							     =42
			lowerCamelCase__							     =None
			@classmethod
			def 				__UpperCamelCase			(      cls				:			Tuple   ,   a				:			CommonSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray						)				->							Dict:

				"""simple docstring"""

				return cls(common=a   ,   init_noise_sigma=a   ,   timesteps=a						)






@dataclass
class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =42






class _UpperCamelCase				(				__A				,						__A ):
			'''simple docstring'''







			lowerCamelCase__							     =[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase__							     =42
			@property
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(      self				:			str   ,   a				:			int = 1000   ,   a				:			float = 0.0001   ,   a				:			float = 0.02   ,   a				:			str = "linear"   ,   a				:			Optional[jnp.ndarray] = None   ,   a				:			str = "fixed_small"   ,   a				:			bool = True   ,   a				:			str = "epsilon"   ,   a				:			jnp.dtype = jnp.floataa   ,   )				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							dtype
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			Optional[CommonSchedulerState] = None						)				->							DDPMSchedulerState:

				"""simple docstring"""

				if common is None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							CommonSchedulerState.create(self						)

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

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

				return DDPMSchedulerState.create(
				    common=a   ,   init_noise_sigma=a   ,   timesteps=a   ,   )
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			Optional[int] = None						)				->							jnp.ndarray:

				"""simple docstring"""

				return sample
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			DDPMSchedulerState   ,   a				:			int   ,   a				:			Tuple = ()						)				->							DDPMSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[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
				SCREAMING_SNAKE_CASE :   Dict					  =							(jnp.arange(0   ,   a						) * step_ratio).round()[::-1]

				return state.replace(
				    num_inference_steps=a   ,   timesteps=a   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			DDPMSchedulerState   ,   a				:			List[str]   ,   a				:			Any=None   ,   a				:			Dict=None						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE :   Union[str, 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
				SCREAMING_SNAKE_CASE :   List[str]					  =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

				if variance_type is None:
					SCREAMING_SNAKE_CASE :   int					  =							self.config.variance_type

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

				return variance
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			int   ,   a				:			jnp.ndarray   ,   a				:			Optional[jax.random.KeyArray] = None   ,   a				:			bool = True   ,   )				->							Union[FlaxDDPMSchedulerOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							timestep

				if key is None:
					SCREAMING_SNAKE_CASE :   str					  =							jax.random.PRNGKey(0						)

				if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							jnp.split(a   ,   sample.shape[1]   ,   axis=1						)
				else:
					SCREAMING_SNAKE_CASE :   int					  =							None

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE :   Tuple					  =							state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							jnp.where(t > 0   ,   state.common.alphas_cumprod[t - 1]   ,   jnp.array(1.0   ,   dtype=self.dtype						)						)
				SCREAMING_SNAKE_CASE :   str					  =							1 - alpha_prod_t
				SCREAMING_SNAKE_CASE :   str					  =							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":
					SCREAMING_SNAKE_CASE :   int					  =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model_output
				elif self.config.prediction_type == "v_prediction":
					SCREAMING_SNAKE_CASE :   List[Any]					  =							(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:
					SCREAMING_SNAKE_CASE :   Tuple					  =							jnp.clip(a   ,   -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
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE :   int					  =							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
				SCREAMING_SNAKE_CASE :   Dict					  =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				def random_variance():
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							jax.random.split(a   ,   num=1						)
					SCREAMING_SNAKE_CASE :   Dict					  =							jax.random.normal(a   ,   shape=model_output.shape   ,   dtype=self.dtype						)
					return (self._get_variance(a   ,   a   ,   predicted_variance=a						) ** 0.5) * noise

				SCREAMING_SNAKE_CASE :   str					  =							jnp.where(t > 0   ,   random_variance()   ,   jnp.zeros(model_output.shape   ,   dtype=self.dtype						)						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=a   ,   state=a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   )				->							jnp.ndarray:

				"""simple docstring"""

				return add_noise_common(state.common   ,   a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			str   ,   a				:			DDPMSchedulerState   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   a				:			jnp.ndarray   ,   )				->							jnp.ndarray:

				"""simple docstring"""

				return get_velocity_common(state.common   ,   a   ,   a   ,   a						)






			def __len__(      self				:			int						)				->							Any:

				"""simple docstring"""

				return self.config.num_train_timesteps


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-1






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

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