





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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Tuple					)							->						Any:
     with tempfile.TemporaryDirectory() as tmpdirname:
        # pipeline has Flax weights
        __lowerCAmelCase							     =     FlaxDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe' ,  safety_checker=lowerCAmelCase_ ,  cache_dir=lowerCAmelCase_					)

        __lowerCAmelCase							     =     [t[-1] for t in os.walk(os.path.join(lowerCAmelCase_ ,  os.listdir(lowerCAmelCase_					)[0] ,  'snapshots'					)					)]
        __lowerCAmelCase							     =     [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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'hf-internal-testing/tiny-stable-diffusion-pipe' ,  safety_checker=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     4

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
     if jax.device_count() == 8:
        assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 4.1_51_47_45					) < 1e-3
        assert np.abs(np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 4_99_47.8_75					) < 5e-1

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






  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='flax' ,  safety_checker=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.05_65_24_01)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_38_38_08.2)					) < 5e-1






  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase_					)

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

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.04_00_39_06)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_37_35_16.75)					) < 5e-1






  def     lowercase		(    self			:		int					)							->						List[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa					)

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

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.04_00_39_06)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_37_35_16.75)					) < 5e-1






  def     lowercase		(    self			:		Dict					)							->						Dict:
     __lowerCAmelCase							     =     FlaxDDIMScheduler(
         beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  beta_schedule='scaled_linear' ,  set_alpha_to_one=lowerCAmelCase_ ,  steps_offset=1 ,  )

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  scheduler=lowerCAmelCase_ ,  safety_checker=lowerCAmelCase_ ,  )
     __lowerCAmelCase							     =     scheduler.create_state()

     __lowerCAmelCase							     =     scheduler_state

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

     __lowerCAmelCase							     =     jax.random.PRNGKey(0					)
     __lowerCAmelCase							     =     5_0

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)

     # shard inputs and rng
     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     jax.random.split(lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)

     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
        assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,  dtype=np.floataa					).sum() - 0.0_45_04_39_45)					) < 1e-3
        assert np.abs((np.abs(lowerCAmelCase_ ,  dtype=np.floataa					).sum() - 2_34_76_93.5)					) < 5e-1






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

     __lowerCAmelCase							     =     jax.device_count()
     __lowerCAmelCase							     =     num_samples * [prompt]
     __lowerCAmelCase							     =     jax.random.split(jax.random.PRNGKey(0					) ,  lowerCAmelCase_					)

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='bf16' ,  dtype=jnp.bfloataa ,  safety_checker=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images
     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     images[2, 0, 2_5_6, 1_0:1_7, 1]

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

     __lowerCAmelCase							     =     replicate(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline.prepare_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     shard(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipeline(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  jit=lowerCAmelCase_					).images
     assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     images[2, 0, 2_5_6, 1_0:1_7, 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








from functools import lru_cache

@lru_cache
def   a_   (			lowerCAmelCase_				:      int     ):
   if num < 0:
      raise ValueError('Number should not be negative.'     )

   return 1 if num in (0, 1) else num * factorial(num - 1     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

