

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case      ,		snake_case							:		Tuple        =		FlaxStableDiffusionPipeline.from_pretrained(
          """stabilityai/stable-diffusion-2"""   ,revision="""bf16"""   ,dtype=jnp.bfloataa   ,)

      snake_case							:		int        =		"""A painting of a squirrel eating a burger"""
      snake_case							:		Dict        =		jax.device_count()
      snake_case							:		Optional[Any]        =		num_samples * [prompt]
      snake_case							:		Any        =		sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[str]        =		replicate(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		shard(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Any        =		jax.random.PRNGKey(0    )
      snake_case							:		List[Any]        =		jax.random.split(SCREAMING_SNAKE_CASE_   ,jax.device_count()    )

      snake_case							:		Dict        =		sd_pipe(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,num_inference_steps=25   ,jit=SCREAMING_SNAKE_CASE_    )[0]
      assert images.shape == (jax.device_count(), 1, 768, 768, 3)

      snake_case							:		Optional[int]        =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
      snake_case							:		Union[str, Any]        =		images[0, 253:256, 253:256, -1]

      snake_case							:		List[Any]        =		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
      snake_case							:		Tuple        =		jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]    )
      print(F"""output_slice: {output_slice}"""    )
      assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Dict        =		"""stabilityai/stable-diffusion-2"""
      snake_case      ,		snake_case							:		str        =		FlaxDPMSolverMultistepScheduler.from_pretrained(SCREAMING_SNAKE_CASE_   ,subfolder="""scheduler"""    )
      snake_case      ,		snake_case							:		List[Any]        =		FlaxStableDiffusionPipeline.from_pretrained(
          SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_   ,revision="""bf16"""   ,dtype=jnp.bfloataa   ,)
      snake_case							:		int        =		scheduler_params

      snake_case							:		int        =		"""A painting of a squirrel eating a burger"""
      snake_case							:		int        =		jax.device_count()
      snake_case							:		List[str]        =		num_samples * [prompt]
      snake_case							:		Union[str, Any]        =		sd_pipe.prepare_inputs(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		replicate(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		shard(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		jax.random.PRNGKey(0    )
      snake_case							:		List[Any]        =		jax.random.split(SCREAMING_SNAKE_CASE_   ,jax.device_count()    )

      snake_case							:		Tuple        =		sd_pipe(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,num_inference_steps=25   ,jit=SCREAMING_SNAKE_CASE_    )[0]
      assert images.shape == (jax.device_count(), 1, 768, 768, 3)

      snake_case							:		List[Any]        =		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
      snake_case							:		str        =		images[0, 253:256, 253:256, -1]

      snake_case							:		List[Any]        =		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
      snake_case							:		Union[str, Any]        =		jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]    )
      print(F"""output_slice: {output_slice}"""    )
      assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2










def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  [1]
 for i in range(2				,  _lowerCAmelCase	):
  factorials.append(factorials[-1] * i	)
 assert 0 <= k < factorials[-1] * n, "k out of bounds"

 UpperCamelCase      :   Tuple			      =  []
 UpperCamelCase      :   str			      =  list(range(_lowerCAmelCase	)	)

 # Find permutation
 while factorials:
  UpperCamelCase      :   int			      =  factorials.pop()
  UpperCamelCase      ,       UpperCamelCase      :   int			      =  divmod(_lowerCAmelCase				,  _lowerCAmelCase	)
  permutation.append(elements[number]	)
  elements.remove(elements[number]	)
 permutation.append(elements[0]	)

 return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()
