


import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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()


class 	_a							(      UpperCamelCase__					,     UpperCamelCase__					,     UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      int  				=			AltDiffusionPipeline
							_lowercase     :      Tuple  				=			TEXT_TO_IMAGE_PARAMS
							_lowercase     :      List[str]  				=			TEXT_TO_IMAGE_BATCH_PARAMS
							_lowercase     :      Union[str, Any]  				=			TEXT_TO_IMAGE_IMAGE_PARAMS
							_lowercase     :      int  				=			TEXT_TO_IMAGE_IMAGE_PARAMS

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





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				UNetaDConditionModel(
									    block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,						cross_attention_dim=32   ,						)
									lowercase__						=				DDIMScheduler(
									    beta_start=0.00085   ,						beta_end=0.012   ,						beta_schedule='''scaled_linear'''   ,						clip_sample=UpperCamelCase_   ,						set_alpha_to_one=UpperCamelCase_   ,						)
									torch.manual_seed(0							)
									lowercase__						=				AutoencoderKL(
									    block_out_channels=[32, 64]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=4   ,						)

									# TODO: address the non-deterministic text encoder (fails for save-load tests)
									# torch.manual_seed(0)
									# text_encoder_config = RobertaSeriesConfig(
									#     hidden_size=32,
									#     project_dim=32,
									#     intermediate_size=37,
									#     layer_norm_eps=1e-05,
									#     num_attention_heads=4,
									#     num_hidden_layers=5,
									#     vocab_size=5002,
									# )
									# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

									torch.manual_seed(0							)
									lowercase__						=				CLIPTextConfig(
									    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=32   ,						projection_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=5_002   ,						)
									lowercase__						=				CLIPTextModel(UpperCamelCase_							)

									lowercase__						=				XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
									lowercase__						=				77

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

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





									"""simple docstring"""







									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''output_type''': '''numpy''',
									}
									return inputs

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





									"""simple docstring"""







									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3							)

							def 				lowerCamelCase_			(							self:     Dict							)      ->       str:





									"""simple docstring"""







									super().test_inference_batch_single_identical(expected_max_diff=3E-3							)

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





									"""simple docstring"""







									lowercase__						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									lowercase__						=				self.get_dummy_components()
									torch.manual_seed(0							)
									lowercase__						=				RobertaSeriesConfig(
									    hidden_size=32   ,						project_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=5_002   ,						)
									# TODO: remove after fixing the non-deterministic text encoder
									lowercase__						=				RobertaSeriesModelWithTransformation(UpperCamelCase_							)
									lowercase__						=				text_encoder

									lowercase__						=				AltDiffusionPipeline(**UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				self.get_dummy_inputs(UpperCamelCase_							)
									lowercase__						=				'''A photo of an astronaut'''
									lowercase__						=				alt_pipe(**UpperCamelCase_							)
									lowercase__						=				output.images
									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__						=				np.array(
									    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]							)

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


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





									"""simple docstring"""







									lowercase__						=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									lowercase__						=				self.get_dummy_components()
									lowercase__						=				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
									torch.manual_seed(0							)
									lowercase__						=				RobertaSeriesConfig(
									    hidden_size=32   ,						project_dim=32   ,						intermediate_size=37   ,						layer_norm_eps=1E-0_5   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=5_002   ,						)
									# TODO: remove after fixing the non-deterministic text encoder
									lowercase__						=				RobertaSeriesModelWithTransformation(UpperCamelCase_							)
									lowercase__						=				text_encoder
									lowercase__						=				AltDiffusionPipeline(**UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

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

									assert image.shape == (1, 64, 64, 3)
									lowercase__						=				np.array(
									    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]							)

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


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

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





									"""simple docstring"""







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

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





									"""simple docstring"""







									lowercase__						=				AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,						safety_checker=UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				'''A painting of a squirrel eating a burger'''
									lowercase__						=				torch.manual_seed(0							)
									lowercase__						=				alt_pipe([prompt]   ,						generator=UpperCamelCase_   ,						guidance_scale=6.0   ,						num_inference_steps=20   ,						output_type='''np'''							)

									lowercase__						=				output.images

									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									lowercase__						=				np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]							)

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


							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,						subfolder='''scheduler'''							)

									lowercase__						=				AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,						scheduler=UpperCamelCase_   ,						safety_checker=UpperCamelCase_							)
									lowercase__						=				alt_pipe.to(UpperCamelCase_							)
									alt_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				'''A painting of a squirrel eating a burger'''
									lowercase__						=				torch.manual_seed(0							)

									lowercase__						=				alt_pipe([prompt]   ,						generator=UpperCamelCase_   ,						num_inference_steps=2   ,						output_type='''numpy'''							)
									lowercase__						=				output.images

									lowercase__						=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									lowercase__						=				np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]							)

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






# 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


