





"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class      snake_case						(  a_					,	unittest.TestCase  ):
      lowerCamelCase__         =					DDIMPipeline
      lowerCamelCase__         =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      lowerCamelCase__         =					PipelineTesterMixin.required_optional_params - {
          '''num_images_per_prompt''',
          '''latents''',
          '''callback''',
          '''callback_steps''',
      }
      lowerCamelCase__         =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      lowerCamelCase__         =					False

      def 		SCREAMING_SNAKE_CASE_     (     self  :Union[str, Any]		):
       torch.manual_seed(0		)
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			UNetaDModel(
           block_out_channels=(3_2, 6_4)		,				layers_per_block=2		,				sample_size=3_2		,				in_channels=3		,				out_channels=3		,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,				)
       __SCREAMING_SNAKE_CASE					:     Any   				=			DDIMScheduler()
       __SCREAMING_SNAKE_CASE					:     str   				=			{'''unet''': unet, '''scheduler''': scheduler}
       return components

      def 		SCREAMING_SNAKE_CASE_     (     self  :Any		,				_lowerCamelCase  :Any		,				_lowerCamelCase  :Union[str, Any]=0		):

       if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
        __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			torch.manual_seed(_SCREAMING_SNAKE_CASE		)
       else:
        __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			{
           '''batch_size''': 1,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

      def 		SCREAMING_SNAKE_CASE_     (     self  :Optional[int]		):
       __SCREAMING_SNAKE_CASE					:     Dict   				=			'''cpu'''

       __SCREAMING_SNAKE_CASE					:     str   				=			self.get_dummy_components()
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
       pipe.to(_SCREAMING_SNAKE_CASE		)
       pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			pipe(**_SCREAMING_SNAKE_CASE		).images
       __SCREAMING_SNAKE_CASE					:     int   				=			image[0, -3:, -3:, -1]

       self.assertEqual(image.shape		,				(1, 3_2, 3_2, 3)		)
       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			np.array(
           [1.0_00e00, 5.7_17e-01, 4.7_17e-01, 1.0_00e00, 0.0_00e00, 1.0_00e00, 3.0_00e-04, 0.0_00e00, 9.0_00e-04]		)
       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			np.abs(image_slice.flatten() - expected_slice		).max()
       self.assertLessEqual(_SCREAMING_SNAKE_CASE		,				1e-3		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :Any		):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :List[str]		):
       super().test_save_load_local(expected_max_difference=3e-3		)

      def 		SCREAMING_SNAKE_CASE_     (     self  :List[str]		):
       super().test_save_load_optional_components(expected_max_difference=3e-3		)



      def 		SCREAMING_SNAKE_CASE_     (     self  :str		):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3		)







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

      def 		SCREAMING_SNAKE_CASE_     (     self  :List[str]		):
       __SCREAMING_SNAKE_CASE					:     Any   				=			'''google/ddpm-cifar10-32'''

       __SCREAMING_SNAKE_CASE					:     Dict   				=			UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			DDIMScheduler()

       __SCREAMING_SNAKE_CASE					:     Any   				=			DDIMPipeline(unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		)
       ddim.to(_SCREAMING_SNAKE_CASE		)
       ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Dict   				=			torch.manual_seed(0		)
       __SCREAMING_SNAKE_CASE					:     Any   				=			ddim(generator=_SCREAMING_SNAKE_CASE		,				eta=0.0		,				output_type='''numpy'''		).images

       __SCREAMING_SNAKE_CASE					:     List[Any]   				=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 3_2, 3_2, 3)
       __SCREAMING_SNAKE_CASE					:     Any   				=			np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

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



      def 		SCREAMING_SNAKE_CASE_     (     self  :List[Any]		):
       __SCREAMING_SNAKE_CASE					:     List[str]   				=			'''google/ddpm-ema-bedroom-256'''

       __SCREAMING_SNAKE_CASE					:     Any   				=			UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
       __SCREAMING_SNAKE_CASE					:     Any   				=			DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     Any   				=			DDIMPipeline(unet=_SCREAMING_SNAKE_CASE		,				scheduler=_SCREAMING_SNAKE_CASE		)
       ddpm.to(_SCREAMING_SNAKE_CASE		)
       ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

       __SCREAMING_SNAKE_CASE					:     int   				=			torch.manual_seed(0		)
       __SCREAMING_SNAKE_CASE					:     Optional[Any]   				=			ddpm(generator=_SCREAMING_SNAKE_CASE		,				output_type='''numpy'''		).images

       __SCREAMING_SNAKE_CASE					:     Optional[int]   				=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 2_5_6, 2_5_6, 3)
       __SCREAMING_SNAKE_CASE					:     Tuple   				=			np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

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




'''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





