


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 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Any:




											'''simple docstring'''

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

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

											__snake_case				=		replicate(SCREAMING_SNAKE_CASE						)
											__snake_case				=		shard(SCREAMING_SNAKE_CASE						)

											__snake_case				=		jax.random.PRNGKey(0						)
											__snake_case				=		jax.random.split(SCREAMING_SNAKE_CASE     ,    jax.device_count()						)

											__snake_case				=		sd_pipe(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    num_inference_steps=2_5     ,    jit=SCREAMING_SNAKE_CASE						)[0]
											assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

											__snake_case				=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											__snake_case				=		images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											__snake_case				=		jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											__snake_case				=		jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]						)
											print(f'''output_slice: {output_slice}'''						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1e-2






					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Any:




											'''simple docstring'''

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

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

											__snake_case				=		replicate(SCREAMING_SNAKE_CASE						)
											__snake_case				=		shard(SCREAMING_SNAKE_CASE						)

											__snake_case				=		jax.random.PRNGKey(0						)
											__snake_case				=		jax.random.split(SCREAMING_SNAKE_CASE     ,    jax.device_count()						)

											__snake_case				=		sd_pipe(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    num_inference_steps=2_5     ,    jit=SCREAMING_SNAKE_CASE						)[0]
											assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

											__snake_case				=		images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											__snake_case				=		images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											__snake_case				=		jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											__snake_case				=		jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]						)
											print(f'''output_slice: {output_slice}'''						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1e-2









from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





