'''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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					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, 64, 64, 3)
					if jax.device_count() == 8:
							assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa).sum() - 4.1514745) < 1E-3
							assert np.abs(np.abs(__lowerCAmelCase       ,    dtype=np.floataa).sum() - 49947.875) < 5E-1

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



			def 							a_       (   self):




					"""simple docstring"""


					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   					=  50

					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, 512, 512, 3)
					if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa).sum() - 0.05652401)) < 1E-3
							assert np.abs((np.abs(__lowerCAmelCase       ,    dtype=np.floataa).sum() - 2383808.2)) < 5E-1



			def 							a_       (   self):




					"""simple docstring"""


					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   					=  50

					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, 512, 512, 3)
					if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa).sum() - 0.04003906)) < 1E-3
							assert np.abs((np.abs(__lowerCAmelCase       ,    dtype=np.floataa).sum() - 2373516.75)) < 5E-1



			def 							a_       (   self):




					"""simple docstring"""


					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   					=  50

					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, 512, 512, 3)
					if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa).sum() - 0.04003906)) < 1E-3
							assert np.abs((np.abs(__lowerCAmelCase       ,    dtype=np.floataa).sum() - 2373516.75)) < 5E-1



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  FlaxDDIMScheduler(
					    beta_start=0.00085       ,    beta_end=0.012       ,    beta_schedule="""scaled_linear"""       ,    set_alpha_to_one=__lowerCAmelCase       ,    steps_offset=1       ,    )

					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   					=  50

					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, 512, 512, 3)
					if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,    dtype=np.floataa).sum() - 0.045043945)) < 1E-3
							assert np.abs((np.abs(__lowerCAmelCase       ,    dtype=np.floataa).sum() - 2347693.5)) < 5E-1







			def 							a_       (   self):




					"""simple docstring"""


					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   					=  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, 512, 512, 3)
					lowerCAmelCase   					=  images[2, 0, 256, 10:17, 1]

					# With memory efficient attention
					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, 512, 512, 3)
					lowerCAmelCase   					=  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'''






from __future__ import annotations

from typing import Any






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    pass







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  None:
      lowerCAmelCase_			:      Any			=   data
      lowerCAmelCase_			:      Node | None			=   None

    def __iter__(   self    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Union[str, Any]			=   self
      lowerCAmelCase_			:      Any			=   []
      while node:
        if node in visited:
          raise ContainsLoopError
        visited.append(lowerCamelCase		)
        yield node.data
        lowerCAmelCase_			:      int			=   node.next_node





    @property
    def __lowercase   (   self    :  str		)   ->  bool:
      try:
        list(self		)
        return False
      except ContainsLoopError:
        return True


if __name__ == "__main__":
  __A					:  Dict											=      Node(1)
  __A					:  Optional[Any]											=      Node(2)
  __A					:  int											=      Node(3)
  __A					:  Optional[Any]											=      Node(4)
  print(root_node.has_loop)  # False
  __A					:  Any											=      root_node.next_node
  print(root_node.has_loop)  # True

  __A					:  List[Any]											=      Node(5)
  __A					:  Dict											=      Node(6)
  __A					:  str											=      Node(5)
  __A					:  Dict											=      Node(6)
  print(root_node.has_loop)  # False

  __A					:  Optional[int]											=      Node(1)
  print(root_node.has_loop)  # False



