
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        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				return F'''gaussian_noise_s={seed}_shape={'_'.join([str(A_ ) for s in shape] )}.npy'''



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()



		def __UpperCamelCase				(				self       ,   A_=0       ,   A_=(4, 4, 64, 64)       ,   A_=False )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  jnp.bfloataa if fpaa else jnp.floataa
				UpperCamelCase						=  jnp.array(load_hf_numpy(self.get_file_format(A_       ,   A_ ) )       ,   dtype=A_ )
				return image



		def __UpperCamelCase				(				self       ,   A_=False       ,   A_="CompVis/stable-diffusion-v1-4" )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  jnp.bfloataa if fpaa else jnp.floataa
				UpperCamelCase						=  'bf16' if fpaa else None

				UpperCamelCase			,						UpperCamelCase						=  FlaxUNetaDConditionModel.from_pretrained(
				    A_       ,   subfolder='unet'       ,   dtype=A_       ,   revision=A_ )
				return model, params



		def __UpperCamelCase				(				self       ,   A_=0       ,   A_=(4, 77, 768)       ,   A_=False )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  jnp.bfloataa if fpaa else jnp.floataa
				UpperCamelCase						=  jnp.array(load_hf_numpy(self.get_file_format(A_       ,   A_ ) )       ,   dtype=A_ )
				return hidden_states



		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
		        [17, 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_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
		        # fmt: on
		    ] )
		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'       ,   fpaa=A_ )
				UpperCamelCase						=  self.get_latents(A_       ,   fpaa=A_ )
				UpperCamelCase						=  self.get_encoder_hidden_states(A_       ,   fpaa=A_ )

				UpperCamelCase						=  model.apply(
				    {'params': params}       ,   A_       ,   jnp.array(A_       ,   dtype=jnp.intaa )       ,   encoder_hidden_states=A_       ,   ).sample

				assert sample.shape == latents.shape

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

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



		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
		        [17, 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_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
		        # fmt: on
		    ] )
		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.get_unet_model(model_id='stabilityai/stable-diffusion-2'       ,   fpaa=A_ )
				UpperCamelCase						=  self.get_latents(A_       ,   shape=(4, 4, 96, 96)       ,   fpaa=A_ )
				UpperCamelCase						=  self.get_encoder_hidden_states(A_       ,   shape=(4, 77, 1_024)       ,   fpaa=A_ )

				UpperCamelCase						=  model.apply(
				    {'params': params}       ,   A_       ,   jnp.array(A_       ,   dtype=jnp.intaa )       ,   encoder_hidden_states=A_       ,   ).sample

				assert sample.shape == latents.shape

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

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


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       (DDPMScheduler,)



		def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  {
				    'num_train_timesteps': 1_000,
				    'beta_start': 0.0001,
				    'beta_end': 0.02,
				    'beta_schedule': 'linear',
				    'variance_type': 'fixed_small',
				    'clip_sample': True,
				}

				config.update(**A_ )
				return config



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				for timesteps in [1, 5, 100, 1_000]:
						self.check_over_configs(num_train_timesteps=A_ )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,   [0.002, 0.02, 0.2, 2] ):
						self.check_over_configs(beta_start=A_       ,   beta_end=A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				self.check_over_configs(thresholding=A_ )
				for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(
								    thresholding=A_       ,   prediction_type=A_       ,   sample_max_value=A_       ,   )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				for t in [0, 500, 999]:
						self.check_over_forward(time_step=A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

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

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 258.9606 ) < 1e-2
				assert abs(result_mean.item() - 0.3372 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config(prediction_type='v_prediction' )
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  len(A_ )

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

				for t in reversed(range(A_ ) ):
						# 1. predict noise residual
						UpperCamelCase						=  model(A_       ,   A_ )

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

						# if t > 0:
						#     noise = self.dummy_sample_deter
						#     variance = scheduler.get_variance(t) ** (0.5) * noise
						#
						# sample = pred_prev_sample + variance
						UpperCamelCase						=  pred_prev_sample

				UpperCamelCase						=  torch.sum(torch.abs(A_ ) )
				UpperCamelCase						=  torch.mean(torch.abs(A_ ) )

				assert abs(result_sum.item() - 202.0296 ) < 1e-2
				assert abs(result_mean.item() - 0.2631 ) < 1e-3



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=A_ )

				UpperCamelCase						=  scheduler.timesteps

				for i, timestep in enumerate(A_ ):
						if i == len(A_ ) - 1:
								UpperCamelCase						=  -1
						else:
								UpperCamelCase						=  timesteps[i + 1]

						UpperCamelCase						=  scheduler.previous_timestep(A_ )
						UpperCamelCase						=  prev_t.item()

						self.assertEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 51, 0]

				with self.assertRaises(A_       ,   msg='`custom_timesteps` must be in descending order.' ):
						scheduler.set_timesteps(timesteps=A_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [100, 87, 50, 1, 0]
				UpperCamelCase						=  len(A_ )

				with self.assertRaises(A_       ,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
						scheduler.set_timesteps(num_inference_steps=A_       ,   timesteps=A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.scheduler_classes[0]
				UpperCamelCase						=  self.get_scheduler_config()
				UpperCamelCase						=  scheduler_class(**A_ )

				UpperCamelCase						=  [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    A_       ,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,   ):
						scheduler.set_timesteps(timesteps=A_ )
