





'''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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :int							)->							Optional[int]:
     with tempfile.TemporaryDirectory() as tmpdirname:
         # pipeline has Flax weights
         A__				=			FlaxDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"						,       safety_checker=lowercase_						,       cache_dir=lowercase_							)

         A__				=			[t[-1] for t in os.walk(os.path.join(lowercase_						,       os.listdir(lowercase_							)[0]						,       "snapshots"							)							)]
         A__				=			[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				):
 def      UpperCAmelCase_							(				self      :List[str]							)->							Union[str, Any]:
     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-pipe"						,       safety_checker=lowercase_							)

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

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

     A__				=			jax.device_count()
     A__				=			num_samples * [prompt]
     A__				=			pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__				=			replicate(lowercase_							)
     A__				=			jax.random.split(lowercase_						,       lowercase_							)
     A__				=			shard(lowercase_							)

     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).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(lowercase_						,       dtype=np.floataa							).sum() - 4_9_9_4_7.8_7_5							) < 5E-1

     A__				=			pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]							)							)							)
     assert len(lowercase_							) == num_samples
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[int]:
     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,       revision="flax"						,       safety_checker=lowercase_							)

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

     A__				=			jax.random.PRNGKey(0							)
     A__				=			50

     A__				=			jax.device_count()
     A__				=			num_samples * [prompt]
     A__				=			pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__				=			replicate(lowercase_							)
     A__				=			jax.random.split(lowercase_						,       lowercase_							)
     A__				=			shard(lowercase_							)

     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 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(lowercase_						,       dtype=np.floataa							).sum() - 2_3_8_3_8_0_8.2)							) < 5E-1
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Any:
     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,       revision="bf16"						,       dtype=jnp.bfloataa						,       safety_checker=lowercase_							)

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

     A__				=			jax.random.PRNGKey(0							)
     A__				=			50

     A__				=			jax.device_count()
     A__				=			num_samples * [prompt]
     A__				=			pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__				=			replicate(lowercase_							)
     A__				=			jax.random.split(lowercase_						,       lowercase_							)
     A__				=			shard(lowercase_							)

     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 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(lowercase_						,       dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1
 def      UpperCAmelCase_							(				self      :Dict							)->							Optional[int]:
     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,       revision="bf16"						,       dtype=jnp.bfloataa							)

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

     A__				=			jax.random.PRNGKey(0							)
     A__				=			50

     A__				=			jax.device_count()
     A__				=			num_samples * [prompt]
     A__				=			pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__				=			replicate(lowercase_							)
     A__				=			jax.random.split(lowercase_						,       lowercase_							)
     A__				=			shard(lowercase_							)

     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 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(lowercase_						,       dtype=np.floataa							).sum() - 2_3_7_3_5_1_6.7_5)							) < 5E-1
 def      UpperCAmelCase_							(				self      :str							)->							Tuple:
     A__				=			FlaxDDIMScheduler(
         beta_start=0.0_0_0_8_5						,       beta_end=0.0_1_2						,       beta_schedule="scaled_linear"						,       set_alpha_to_one=lowercase_						,       steps_offset=1						,       )

     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,       revision="bf16"						,       dtype=jnp.bfloataa						,       scheduler=lowercase_						,       safety_checker=lowercase_						,       )
     A__				=			scheduler.create_state()

     A__				=			scheduler_state

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

     A__				=			jax.random.PRNGKey(0							)
     A__				=			50

     A__				=			jax.device_count()
     A__				=			num_samples * [prompt]
     A__				=			pipeline.prepare_inputs(lowercase_							)

     # shard inputs and rng
     A__				=			replicate(lowercase_							)
     A__				=			jax.random.split(lowercase_						,       lowercase_							)
     A__				=			shard(lowercase_							)

     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images

     assert images.shape == (num_samples, 1, 5_12, 5_12, 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(lowercase_						,       dtype=np.floataa							).sum() - 2_3_4_7_6_9_3.5)							) < 5E-1



 def      UpperCAmelCase_							(				self      :Any							)->							List[str]:
     A__				=			(
         "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
         " field, close up, split lighting, cinematic"
     )

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

     A__,	A__				=			FlaxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"						,       revision="bf16"						,       dtype=jnp.bfloataa						,       safety_checker=lowercase_						,       )

     A__				=			replicate(lowercase_							)
     A__				=			pipeline.prepare_inputs(lowercase_							)
     A__				=			shard(lowercase_							)
     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images
     assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
     A__				=			images[2, 0, 2_56, 10:17, 1]

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

     A__				=			replicate(lowercase_							)
     A__				=			pipeline.prepare_inputs(lowercase_							)
     A__				=			shard(lowercase_							)
     A__				=			pipeline(lowercase_						,       lowercase_						,       lowercase_						,       jit=lowercase_							).images
     assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
     A__				=			images[2, 0, 2_56, 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'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



