

'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     (DDPMScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 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   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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 				_snake_case					(   unittest.TestCase			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().tearDown()
      gc.collect()

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      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, 7_68, 5_12, 3)

      lowerCAmelCase 						=							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
      lowerCAmelCase 						=							images[0, 2_53:2_56, 2_53:2_56, -1]

      lowerCAmelCase 						=							jnp.asarray(jax.device_get(image_slice.flatten()	)	)
      lowerCAmelCase 						=							jnp.array(
          [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]	)
      print(F'output_slice: {output_slice}'	)
      assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





