

"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 		_A       (			__a					):
      __a								=	(DDPMScheduler,)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         lowerCamelCase__						=			{
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "variance_type": "fixed_small",
             "clip_sample": True,
         }

         config.update(**SCREAMING_SNAKE_CASE__		)
         return config





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         for timesteps in [1, 5, 100, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,						[0.0_02, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__    ,						beta_end=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__		)
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=SCREAMING_SNAKE_CASE__    ,						prediction_type=SCREAMING_SNAKE_CASE__    ,						sample_max_value=SCREAMING_SNAKE_CASE__    ,						)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         for t in [0, 500, 999]:
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_09_79		)		) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)

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

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

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

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

         lowerCamelCase__						=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 258.9606		) < 1e-2
         assert abs(result_mean.item() - 0.33_72		) < 1e-3





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config(prediction_type="v_prediction"		)
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)

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

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

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

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

         lowerCamelCase__						=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 202.0296		) < 1e-2
         assert abs(result_mean.item() - 0.26_31		) < 1e-3





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

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

         scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			scheduler.timesteps

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

            lowerCamelCase__						=			scheduler.previous_timestep(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			prev_t.item()

            self.assertEqual(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

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

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





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[100, 87, 50, 1, 0]
         lowerCamelCase__						=			len(SCREAMING_SNAKE_CASE__		)

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





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			self.scheduler_classes[0]
         lowerCamelCase__						=			self.get_scheduler_config()
         lowerCamelCase__						=			scheduler_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			[scheduler.config.num_train_timesteps]

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






"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


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





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"bird"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([canny_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__					,       lowerCamelCase__						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,						controlnet=SCREAMING_SNAKE_CASE__    ,						from_pt=SCREAMING_SNAKE_CASE__    ,						dtype=jnp.bfloataa		)
         lowerCamelCase__						=			controlnet_params

         lowerCamelCase__						=			"Chef in the kitchen"
         lowerCamelCase__						=			jax.device_count()
         lowerCamelCase__						=			pipe.prepare_text_inputs([prompts] * num_samples		)

         lowerCamelCase__						=			load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         lowerCamelCase__						=			pipe.prepare_image_inputs([pose_image] * num_samples		)

         lowerCamelCase__						=			jax.random.PRNGKey(0		)
         lowerCamelCase__						=			jax.random.split(SCREAMING_SNAKE_CASE__    ,						jax.device_count()		)

         lowerCamelCase__						=			replicate(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			shard(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe(
             prompt_ids=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						params=SCREAMING_SNAKE_CASE__    ,						prng_seed=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=50    ,						jit=SCREAMING_SNAKE_CASE__    ,						).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowerCamelCase__						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         lowerCamelCase__						=			images[0, 253:256, 253:256, -1]

         lowerCamelCase__						=			jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         lowerCamelCase__						=			jnp.array(
             [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
         print(f'output_slice: {output_slice}'		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1e-2




