


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 _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		FlaxStableDiffusionPipeline.from_pretrained(
               'stabilityai/stable-diffusion-2',      revision='bf16',      dtype=jnp.bfloataa,      )

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

           _lowercase       :							List[Any]      		=		replicate(a_)
           _lowercase       :							str      		=		shard(a_)

           _lowercase       :							List[Any]      		=		jax.random.PRNGKey(0)
           _lowercase       :							Tuple      		=		jax.random.split(a_,      jax.device_count())

           _lowercase       :							Union[str, Any]      		=		sd_pipe(a_,      a_,      a_,      num_inference_steps=25,      jit=a_)[0]
           assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

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

           _lowercase       :							Optional[Any]      		=		jnp.asarray(jax.device_get(image_slice.flatten()))
           _lowercase       :							Any      		=		jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2])
           print(F'''output_slice: {output_slice}''')
           assert jnp.abs(output_slice - expected_slice).max() < 1E-2




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




           """simple docstring"""

           _lowercase       :							str      		=		"""stabilityai/stable-diffusion-2"""
           _lowercase       :							Dict      		=		FlaxDPMSolverMultistepScheduler.from_pretrained(a_,      subfolder='scheduler')
           _lowercase       :							Any      		=		FlaxStableDiffusionPipeline.from_pretrained(
               a_,      scheduler=a_,      revision='bf16',      dtype=jnp.bfloataa,      )
           _lowercase       :							Tuple      		=		scheduler_params

           _lowercase       :							Dict      		=		"""A painting of a squirrel eating a burger"""
           _lowercase       :							str      		=		jax.device_count()
           _lowercase       :							List[Any]      		=		num_samples * [prompt]
           _lowercase       :							str      		=		sd_pipe.prepare_inputs(a_)

           _lowercase       :							List[str]      		=		replicate(a_)
           _lowercase       :							Tuple      		=		shard(a_)

           _lowercase       :							Union[str, Any]      		=		jax.random.PRNGKey(0)
           _lowercase       :							Dict      		=		jax.random.split(a_,      jax.device_count())

           _lowercase       :							str      		=		sd_pipe(a_,      a_,      a_,      num_inference_steps=25,      jit=a_)[0]
           assert images.shape == (jax.device_count(), 1, 7_68, 7_68, 3)

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

           _lowercase       :							Optional[int]      		=		jnp.asarray(jax.device_get(image_slice.flatten()))
           _lowercase       :							Dict      		=		jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7])
           print(F'''output_slice: {output_slice}''')
           assert jnp.abs(output_slice - expected_slice).max() < 1E-2








"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


