






"""simple docstring"""







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   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         super().tearDown()
         gc.collect()



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			FlaxStableDiffusionPipeline.from_pretrained(
             """stabilityai/stable-diffusion-2""" ,		revision="""bf16""" ,		dtype=jnp.bfloataa ,		)

         UpperCAmelCase	    =			"""A painting of a squirrel eating a burger"""
         UpperCAmelCase	    =			jax.device_count()
         UpperCAmelCase	    =			num_samples * [prompt]
         UpperCAmelCase	    =			sd_pipe.prepare_inputs(snake_case__				)

         UpperCAmelCase	    =			replicate(snake_case__				)
         UpperCAmelCase	    =			shard(snake_case__				)

         UpperCAmelCase	    =			jax.random.PRNGKey(0				)
         UpperCAmelCase	    =			jax.random.split(snake_case__ ,		jax.device_count()				)

         UpperCAmelCase	    =			sd_pipe(snake_case__ ,		snake_case__ ,		snake_case__ ,		num_inference_steps=25 ,		jit=snake_case__				)[0]
         assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

         UpperCAmelCase	    =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         UpperCAmelCase	    =			images[0, 2_53:2_56, 2_53:2_56, -1]

         UpperCAmelCase	    =			jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         UpperCAmelCase	    =			jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]				)
         print(f'''output_slice: {output_slice}'''				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1e-2



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			"""stabilityai/stable-diffusion-2"""
         UpperCAmelCase    ,       UpperCAmelCase	    =			FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case__ ,		subfolder="""scheduler"""				)
         UpperCAmelCase    ,       UpperCAmelCase	    =			FlaxStableDiffusionPipeline.from_pretrained(
             snake_case__ ,		scheduler=snake_case__ ,		revision="""bf16""" ,		dtype=jnp.bfloataa ,		)
         UpperCAmelCase	    =			scheduler_params

         UpperCAmelCase	    =			"""A painting of a squirrel eating a burger"""
         UpperCAmelCase	    =			jax.device_count()
         UpperCAmelCase	    =			num_samples * [prompt]
         UpperCAmelCase	    =			sd_pipe.prepare_inputs(snake_case__				)

         UpperCAmelCase	    =			replicate(snake_case__				)
         UpperCAmelCase	    =			shard(snake_case__				)

         UpperCAmelCase	    =			jax.random.PRNGKey(0				)
         UpperCAmelCase	    =			jax.random.split(snake_case__ ,		jax.device_count()				)

         UpperCAmelCase	    =			sd_pipe(snake_case__ ,		snake_case__ ,		snake_case__ ,		num_inference_steps=25 ,		jit=snake_case__				)[0]
         assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

         UpperCAmelCase	    =			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         UpperCAmelCase	    =			images[0, 2_53:2_56, 2_53:2_56, -1]

         UpperCAmelCase	    =			jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         UpperCAmelCase	    =			jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]				)
         print(f'''output_slice: {output_slice}'''				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1e-2








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
