


# 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 	_a							:
							_lowercase     :      CommonSchedulerState

							# setable values
							_lowercase     :      jnp.ndarray
							_lowercase     :      jnp.ndarray
							_lowercase     :      Optional[int]  				=			None

							@classmethod
							def 				lowerCamelCase_			(							cls:     Dict   ,						UpperCamelCase_:     CommonSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray							)      ->       List[str]:





									"""simple docstring"""







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


@dataclass
class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      DDPMSchedulerState


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__      ):
							_lowercase     :      Tuple  				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowercase     :      jnp.dtype

							@property
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return True

							@register_to_config
							def __init__(							self:     Any   ,						UpperCamelCase_:     int = 1_000   ,						UpperCamelCase_:     float = 0.0001   ,						UpperCamelCase_:     float = 0.02   ,						UpperCamelCase_:     str = "linear"   ,						UpperCamelCase_:     Optional[jnp.ndarray] = None   ,						UpperCamelCase_:     str = "fixed_small"   ,						UpperCamelCase_:     bool = True   ,						UpperCamelCase_:     str = "epsilon"   ,						UpperCamelCase_:     jnp.dtype = jnp.floataa   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				dtype

							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									if common is None:
											lowercase__						=				CommonSchedulerState.create(self							)

									# 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 DDPMSchedulerState.create(
									    common=UpperCamelCase_   ,						init_noise_sigma=UpperCamelCase_   ,						timesteps=UpperCamelCase_   ,						)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     Optional[int] = None							)      ->       jnp.ndarray:





									"""simple docstring"""







									return sample

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Tuple = ()							)      ->       DDPMSchedulerState:





									"""simple docstring"""







									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]

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

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     str=None							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				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
									lowercase__						=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											lowercase__						=				jnp.log(jnp.clip(UpperCamelCase_   ,						a_min=1E-2_0							)							)
									elif variance_type == "fixed_large":
											lowercase__						=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											lowercase__						=				jnp.log(state.common.betas[t]							)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											lowercase__						=				variance
											lowercase__						=				state.common.betas[t]
											lowercase__						=				(predicted_variance + 1) / 2
											lowercase__						=				frac * max_log + (1 - frac) * min_log

									return variance

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





									"""simple docstring"""







									lowercase__						=				timestep

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

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

									# 1. compute alphas, betas
									lowercase__						=				state.common.alphas_cumprod[t]
									lowercase__						=				jnp.where(t > 0   ,						state.common.alphas_cumprod[t - 1]   ,						jnp.array(1.0   ,						dtype=self.dtype							)							)
									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 self.config.prediction_type == "epsilon":
											lowercase__						=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											lowercase__						=				model_output
									elif self.config.prediction_type == "v_prediction":
											lowercase__						=				(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:
											lowercase__						=				jnp.clip(UpperCamelCase_   ,						-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
									lowercase__						=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									lowercase__						=				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
									lowercase__						=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

									lowercase__						=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_   ,						state=UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







									return add_noise_common(state.common   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     DDPMSchedulerState   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						UpperCamelCase_:     jnp.ndarray   ,						)      ->       jnp.ndarray:





									"""simple docstring"""







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


							def __len__(							self:     str							)      ->       List[Any]:





									"""simple docstring"""







									return self.config.num_train_timesteps






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Union[str, Any]  				=			ShapEImgaImgPipeline
							_lowercase     :      Optional[Any]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			['''image''']
							_lowercase     :      Optional[int]  				=			[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase     :      Tuple  				=			False

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Union[str, Any]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       int:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									return 8

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,						image_size=64   ,						projection_dim=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_channels=3   ,						num_hidden_layers=5   ,						patch_size=1   ,						)

									lowercase__						=				CLIPVisionModel(UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				CLIPImageProcessor(
									    crop_size=224   ,						do_center_crop=UpperCamelCase_   ,						do_normalize=UpperCamelCase_   ,						do_resize=UpperCamelCase_   ,						image_mean=[0.48145466, 0.4578275, 0.40821073]   ,						image_std=[0.26862954, 0.26130258, 0.27577711]   ,						resample=3   ,						size=224   ,						)

									return image_processor

							@property
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 16,
									    '''embedding_dim''': self.time_input_dim,
									    '''num_embeddings''': 32,
									    '''embedding_proj_dim''': self.text_embedder_hidden_size,
									    '''time_embed_dim''': self.time_embed_dim,
									    '''num_layers''': 1,
									    '''clip_embed_dim''': self.time_input_dim * 2,
									    '''additional_embeddings''': 0,
									    '''time_embed_act_fn''': '''gelu''',
									    '''norm_in_type''': '''layer''',
									    '''embedding_proj_norm_type''': '''layer''',
									    '''encoder_hid_proj_type''': None,
									    '''added_emb_type''': None,
									}

									lowercase__						=				PriorTransformer(**UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       List[Any]:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''param_shapes''': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    '''d_latent''': self.time_input_dim,
									    '''d_hidden''': self.renderer_dim,
									    '''n_output''': 12,
									    '''background''': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									lowercase__						=				ShapERenderer(**UpperCamelCase_							)
									return model

							def 				lowerCamelCase_			(							self:     str							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.dummy_prior
									lowercase__						=				self.dummy_image_encoder
									lowercase__						=				self.dummy_image_processor
									lowercase__						=				self.dummy_renderer

									lowercase__						=				HeunDiscreteScheduler(
									    beta_schedule='''exp'''   ,						num_train_timesteps=1_024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase_   ,						clip_sample=UpperCamelCase_   ,						clip_sample_range=1.0   ,						)
									lowercase__						=				{
									    '''prior''': prior,
									    '''image_encoder''': image_encoder,
									    '''image_processor''': image_processor,
									    '''renderer''': renderer,
									    '''scheduler''': scheduler,
									}

									return components

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[int]=0							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)

									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''image''': input_image,
									    '''generator''': generator,
									    '''num_inference_steps''': 1,
									    '''frame_size''': 32,
									    '''output_type''': '''np''',
									}
									return inputs

							def 				lowerCamelCase_			(							self:     int							)      ->       str:





									"""simple docstring"""







									lowercase__						=				'''cpu'''

									lowercase__						=				self.get_dummy_components()

									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
									lowercase__						=				output.images[0]
									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									lowercase__						=				np.array(
									    [
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									    ]							)

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

							def 				lowerCamelCase_			(							self:     int							)      ->       int:





									"""simple docstring"""







									self._test_inference_batch_consistent(batch_sizes=[1, 2]							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				torch_device == '''cpu'''
									lowercase__						=				True
									self._test_inference_batch_single_identical(
									    batch_size=2   ,						test_max_difference=UpperCamelCase_   ,						relax_max_difference=UpperCamelCase_   ,						)


							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.get_dummy_components()
									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				1
									lowercase__						=				2

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)

									for key in inputs.keys():
											if key in self.batch_params:
													lowercase__						=				batch_size * [inputs[key]]

									lowercase__						=				pipe(**UpperCamelCase_   ,						num_images_per_prompt=UpperCamelCase_							)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt


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

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def 				lowerCamelCase_			(							self:     str							)      ->       str:





									"""simple docstring"""







									lowercase__						=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''							)
									lowercase__						=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/shap_e/test_shap_e_img2img_out.npy'''							)
									lowercase__						=				ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''							)
									lowercase__						=				pipe.to(UpperCamelCase_							)
									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(0							)

									lowercase__						=				pipe(
									    UpperCamelCase_   ,						generator=UpperCamelCase_   ,						guidance_scale=3.0   ,						num_inference_steps=64   ,						frame_size=64   ,						output_type='''np'''   ,						).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(UpperCamelCase_   ,						UpperCamelCase_							)


