






"""simple docstring"""

import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
						import jax
						import jax.numpy as jnp

@slow
@require_flax
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[Any]	)				->					str:
										return f'''gaussian_noise_s={seed}_shape={"_".join([str(__lowerCamelCase	) for s in shape]	)}.npy'''







				def     _snake_case					(							self							:						Any	)				->					Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Union[str, Any]=0						,    __lowerCamelCase							:						List[str]=(4, 4, 6_4, 6_4)						,    __lowerCamelCase							:						Any=False	)				->					Any:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase						,    __lowerCamelCase	)	)						,    dtype=__lowerCamelCase	)
										return image







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						Tuple=False						,    __lowerCamelCase							:						Union[str, Any]="CompVis/stable-diffusion-v1-4"	)				->					List[str]:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     "bf16" if fpaa else None

										__magic_name__      ,    __magic_name__            =     FlaxUNetaDConditionModel.from_pretrained(
										    __lowerCamelCase						,    subfolder="unet"						,    dtype=__lowerCamelCase						,    revision=__lowerCamelCase	)
										return model, params







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple=0						,    __lowerCamelCase							:						Optional[Any]=(4, 7_7, 7_6_8)						,    __lowerCamelCase							:						int=False	)				->					List[Any]:
										__magic_name__            =     jnp.bfloataa if fpaa else jnp.floataa
										__magic_name__            =     jnp.array(load_hf_numpy(self.get_file_format(__lowerCamelCase						,    __lowerCamelCase	)	)						,    dtype=__lowerCamelCase	)
										return hidden_states







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
				        [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
				        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
				        [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
				        # fmt: on
				    ]	)
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						int	)				->					Any:
										__magic_name__      ,    __magic_name__            =     self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_latents(__lowerCamelCase						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_encoder_hidden_states(__lowerCamelCase						,    fpaa=__lowerCamelCase	)

										__magic_name__            =     model.apply(
										    {"params": params}						,    __lowerCamelCase						,    jnp.array(__lowerCamelCase						,    dtype=jnp.intaa	)						,    encoder_hidden_states=__lowerCamelCase						,    ).sample

										assert sample.shape == latents.shape

										__magic_name__            =     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)						,    dtype=jnp.floataa	)
										__magic_name__            =     jnp.array(__lowerCamelCase						,    dtype=jnp.floataa	)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
										assert jnp.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-2	)







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
				        [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
				        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
				        [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
				        # fmt: on
				    ]	)
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[int]	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.get_unet_model(model_id="stabilityai/stable-diffusion-2"						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_latents(__lowerCamelCase						,    shape=(4, 4, 9_6, 9_6)						,    fpaa=__lowerCamelCase	)
										__magic_name__            =     self.get_encoder_hidden_states(__lowerCamelCase						,    shape=(4, 7_7, 1_0_2_4)						,    fpaa=__lowerCamelCase	)

										__magic_name__            =     model.apply(
										    {"params": params}						,    __lowerCamelCase						,    jnp.array(__lowerCamelCase						,    dtype=jnp.intaa	)						,    encoder_hidden_states=__lowerCamelCase						,    ).sample

										assert sample.shape == latents.shape

										__magic_name__            =     jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)						,    dtype=jnp.floataa	)
										__magic_name__            =     jnp.array(__lowerCamelCase						,    dtype=jnp.floataa	)

										# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
										assert jnp.allclose(__lowerCamelCase						,    __lowerCamelCase						,    atol=1e-2	)











"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(UnCLIPScheduler,)







				def     _snake_case					(							self							:						Any						,    **__lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     {
										    "num_train_timesteps": 1_0_0_0,
										    "variance_type": "fixed_small_log",
										    "clip_sample": True,
										    "clip_sample_range": 1.0,
										    "prediction_type": "epsilon",
										}

										config.update(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Any:
										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										for clip_sample_range in [1, 5, 1_0, 2_0]:
																self.check_over_configs(clip_sample_range=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										for time_step in [0, 5_0_0, 9_9_9]:
																for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																												continue

																						self.check_over_forward(time_step=__lowerCamelCase						,    prev_timestep=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="fixed_small_log"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.054_9625	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.999_4987	)	) < 1e-5







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="learned_range"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     0.5

										assert scheduler._get_variance(1						,    predicted_variance=__lowerCamelCase	) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_8_7						,    predicted_variance=__lowerCamelCase	) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_9_9						,    predicted_variance=__lowerCamelCase	) - -0.001_0011 < 1e-5







				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 252.268_2495	) < 1e-2
										assert abs(result_mean.item() - 0.328_4743	) < 1e-3







				def     _snake_case					(							self							:						int	)				->					int:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(2_5	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																if i + 1 == timesteps.shape[0]:
																						__magic_name__            =     None
																else:
																						__magic_name__            =     timesteps[i + 1]

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(
																    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    prev_timestep=__lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 258.204_4983	) < 1e-2
										assert abs(result_mean.item() - 0.336_2038	) < 1e-3







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







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										pass



