


'''simple docstring'''
import os
import tempfile
import unittest

import numpy as np

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


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

  from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          with tempfile.TemporaryDirectory() as tmpdirname:
              # pipeline has Flax weights
              UpperCAmelCase__			:		Optional[Any]													=				FlaxDiffusionPipeline.from_pretrained(
                  '''hf-internal-testing/tiny-stable-diffusion-pipe'''						,						safety_checker=_A						,						cache_dir=_A )

              UpperCAmelCase__			:		List[str]													=				[t[-1] for t in os.walk(os.path.join(_A						,						os.listdir(_A )[0]						,						'''snapshots''' ) )]
              UpperCAmelCase__			:		Tuple													=				[item for sublist in all_root_files for item in sublist]

              # None of the downloaded files should be a PyTorch file even if we have some here:
              # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
              assert not any(f.endswith('''.bin''' ) for f in files )




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






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Union[str, Any]													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''hf-internal-testing/tiny-stable-diffusion-pipe'''						,						safety_checker=_A )

          UpperCAmelCase__			:		Optional[Any]													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		Tuple													=				jax.random.PRNGKey(0 )
          UpperCAmelCase__			:		Union[str, Any]													=				4

          UpperCAmelCase__			:		int													=				jax.device_count()
          UpperCAmelCase__			:		str													=				num_samples * [prompt]
          UpperCAmelCase__			:		str													=				pipeline.prepare_inputs(_A )

          # shard inputs and rng
          UpperCAmelCase__			:		int													=				replicate(_A )
          UpperCAmelCase__			:		Tuple													=				jax.random.split(_A						,						_A )
          UpperCAmelCase__			:		Union[str, Any]													=				shard(_A )

          UpperCAmelCase__			:		List[str]													=				pipeline(_A						,						_A						,						_A						,						_A						,						jit=_A ).images

          assert images.shape == (num_samples, 1, 64, 64, 3)
          if jax.device_count() == 8:
              assert np.abs(np.abs(images[0, 0, :2, :2, -2:]						,						dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1e-3
              assert np.abs(np.abs(_A						,						dtype=np.floataa ).sum() - 4_9_9_4_7.8_7_5 ) < 5e-1

          UpperCAmelCase__			:		Tuple													=				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
          assert len(_A ) == num_samples






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''flax'''						,						safety_checker=_A )

          UpperCAmelCase__			:		Optional[Any]													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		int													=				jax.random.PRNGKey(0 )
          UpperCAmelCase__			:		Tuple													=				50

          UpperCAmelCase__			:		int													=				jax.device_count()
          UpperCAmelCase__			:		Union[str, Any]													=				num_samples * [prompt]
          UpperCAmelCase__			:		str													=				pipeline.prepare_inputs(_A )

          # shard inputs and rng
          UpperCAmelCase__			:		Any													=				replicate(_A )
          UpperCAmelCase__			:		Optional[Any]													=				jax.random.split(_A						,						_A )
          UpperCAmelCase__			:		List[str]													=				shard(_A )

          UpperCAmelCase__			:		List[Any]													=				pipeline(_A						,						_A						,						_A						,						_A						,						jit=_A ).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
              assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,						dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1e-3
              assert np.abs((np.abs(_A						,						dtype=np.floataa ).sum() - 2_3_8_3_8_0_8.2) ) < 5e-1






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''bf16'''						,						dtype=jnp.bfloataa						,						safety_checker=_A )

          UpperCAmelCase__			:		Any													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		Tuple													=				jax.random.PRNGKey(0 )
          UpperCAmelCase__			:		Optional[Any]													=				50

          UpperCAmelCase__			:		int													=				jax.device_count()
          UpperCAmelCase__			:		List[str]													=				num_samples * [prompt]
          UpperCAmelCase__			:		List[Any]													=				pipeline.prepare_inputs(_A )

          # shard inputs and rng
          UpperCAmelCase__			:		Union[str, Any]													=				replicate(_A )
          UpperCAmelCase__			:		Optional[int]													=				jax.random.split(_A						,						_A )
          UpperCAmelCase__			:		Tuple													=				shard(_A )

          UpperCAmelCase__			:		Tuple													=				pipeline(_A						,						_A						,						_A						,						_A						,						jit=_A ).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
              assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,						dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
              assert np.abs((np.abs(_A						,						dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''bf16'''						,						dtype=jnp.bfloataa )

          UpperCAmelCase__			:		Tuple													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		List[Any]													=				jax.random.PRNGKey(0 )
          UpperCAmelCase__			:		Optional[int]													=				50

          UpperCAmelCase__			:		Optional[Any]													=				jax.device_count()
          UpperCAmelCase__			:		int													=				num_samples * [prompt]
          UpperCAmelCase__			:		Tuple													=				pipeline.prepare_inputs(_A )

          # shard inputs and rng
          UpperCAmelCase__			:		Optional[Any]													=				replicate(_A )
          UpperCAmelCase__			:		Dict													=				jax.random.split(_A						,						_A )
          UpperCAmelCase__			:		str													=				shard(_A )

          UpperCAmelCase__			:		List[Any]													=				pipeline(_A						,						_A						,						_A						,						_A						,						jit=_A ).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
              assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,						dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1e-3
              assert np.abs((np.abs(_A						,						dtype=np.floataa ).sum() - 2_3_7_3_5_1_6.7_5) ) < 5e-1






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				FlaxDDIMScheduler(
              beta_start=0.0_0_0_8_5						,						beta_end=0.0_1_2						,						beta_schedule='''scaled_linear'''						,						set_alpha_to_one=_A						,						steps_offset=1						,						)

          UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''bf16'''						,						dtype=jnp.bfloataa						,						scheduler=_A						,						safety_checker=_A						,						)
          UpperCAmelCase__			:		Any													=				scheduler.create_state()

          UpperCAmelCase__			:		Union[str, Any]													=				scheduler_state

          UpperCAmelCase__			:		Optional[int]													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		Dict													=				jax.random.PRNGKey(0 )
          UpperCAmelCase__			:		Any													=				50

          UpperCAmelCase__			:		Optional[Any]													=				jax.device_count()
          UpperCAmelCase__			:		List[str]													=				num_samples * [prompt]
          UpperCAmelCase__			:		List[str]													=				pipeline.prepare_inputs(_A )

          # shard inputs and rng
          UpperCAmelCase__			:		List[Any]													=				replicate(_A )
          UpperCAmelCase__			:		str													=				jax.random.split(_A						,						_A )
          UpperCAmelCase__			:		int													=				shard(_A )

          UpperCAmelCase__			:		Any													=				pipeline(_A						,						_A						,						_A						,						_A						,						jit=_A ).images

          assert images.shape == (num_samples, 1, 512, 512, 3)
          if jax.device_count() == 8:
              assert np.abs((np.abs(images[0, 0, :2, :2, -2:]						,						dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1e-3
              assert np.abs((np.abs(_A						,						dtype=np.floataa ).sum() - 2_3_4_7_6_9_3.5) ) < 5e-1






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				(
              '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
              ''' field, close up, split lighting, cinematic'''
          )

          UpperCAmelCase__			:		List[Any]													=				jax.device_count()
          UpperCAmelCase__			:		int													=				num_samples * [prompt]
          UpperCAmelCase__			:		int													=				jax.random.split(jax.random.PRNGKey(0 )						,						_A )

          UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''bf16'''						,						dtype=jnp.bfloataa						,						safety_checker=_A						,						)

          UpperCAmelCase__			:		Dict													=				replicate(_A )
          UpperCAmelCase__			:		Optional[int]													=				pipeline.prepare_inputs(_A )
          UpperCAmelCase__			:		Optional[int]													=				shard(_A )
          UpperCAmelCase__			:		int													=				pipeline(_A						,						_A						,						_A						,						jit=_A ).images
          assert images.shape == (num_samples, 1, 512, 512, 3)
          UpperCAmelCase__			:		Dict													=				images[2, 0, 256, 10:17, 1]

          # With memory efficient attention
          UpperCAmelCase__						,						UpperCAmelCase__			:		int													=				FlaxStableDiffusionPipeline.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''						,						revision='''bf16'''						,						dtype=jnp.bfloataa						,						safety_checker=_A						,						use_memory_efficient_attention=_A						,						)

          UpperCAmelCase__			:		Dict													=				replicate(_A )
          UpperCAmelCase__			:		Optional[int]													=				pipeline.prepare_inputs(_A )
          UpperCAmelCase__			:		Dict													=				shard(_A )
          UpperCAmelCase__			:		Union[str, Any]													=				pipeline(_A						,						_A						,						_A						,						jit=_A ).images
          assert images_eff.shape == (num_samples, 1, 512, 512, 3)
          UpperCAmelCase__			:		Union[str, Any]													=				images[2, 0, 256, 10:17, 1]

          # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
          # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
          assert abs(slice_eff - slice ).max() < 1e-2




'''simple docstring'''
import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 			lowerCamelCase_   (	unittest.TestCase      ):






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          debug_launcher(test_script.main )






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          debug_launcher(test_ops.main )
