import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
					import jax
					import jax.numpy as jnp





@slow
@require_flax
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return f"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase__		) for s in shape]		)}.npy"""


						def  __A ( self			:       Dict		):





												'''simple docstring'''





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


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=(4, 4, 64, 64)					,	UpperCamelCase__			:       Optional[Any]=False		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					Any           =							jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__					,	UpperCamelCase__		)		)					,	dtype=UpperCamelCase__		)
												return image


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Dict=False					,	UpperCamelCase__			:       List[Any]="CompVis/stable-diffusion-v1-4"		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					Any           =							'''bf16''' if fpaa else None

												SCREAMING_SNAKE_CASE							:					List[Any]           =							FlaxUNetaDConditionModel.from_pretrained(
												    UpperCamelCase__					,	subfolder='''unet'''					,	dtype=UpperCamelCase__					,	revision=UpperCamelCase__		)
												return model, params


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[str]=0					,	UpperCamelCase__			:       Dict=(4, 77, 768)					,	UpperCamelCase__			:       Optional[int]=False		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							jnp.bfloataa if fpaa else jnp.floataa
												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__					,	UpperCamelCase__		)		)					,	dtype=UpperCamelCase__		)
												return hidden_states


						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
						        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
						        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
						        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]		)
						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_latents(UpperCamelCase__					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_encoder_hidden_states(UpperCamelCase__					,	fpaa=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							model.apply(
												    {'''params''': params}					,	UpperCamelCase__					,	jnp.array(UpperCamelCase__					,	dtype=jnp.intaa		)					,	encoder_hidden_states=UpperCamelCase__					,	).sample

												assert sample.shape == latents.shape

												SCREAMING_SNAKE_CASE							:					List[Any]           =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)					,	dtype=jnp.floataa		)
												SCREAMING_SNAKE_CASE							:					str           =							jnp.array(UpperCamelCase__					,	dtype=jnp.floataa		)

												# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
												assert jnp.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-2		)



						@parameterized.expand(
						    [
						        # fmt: off
						        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
						        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
						        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
						        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]		)
						def  __A ( self			:       str					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_latents(UpperCamelCase__					,	shape=(4, 4, 96, 96)					,	fpaa=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.get_encoder_hidden_states(UpperCamelCase__					,	shape=(4, 77, 1024)					,	fpaa=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							model.apply(
												    {'''params''': params}					,	UpperCamelCase__					,	jnp.array(UpperCamelCase__					,	dtype=jnp.intaa		)					,	encoder_hidden_states=UpperCamelCase__					,	).sample

												assert sample.shape == latents.shape

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)					,	dtype=jnp.floataa		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							jnp.array(UpperCamelCase__					,	dtype=jnp.floataa		)

												# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
												assert jnp.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-2		)




import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



def    A			(					_lowercase	,						_lowercase		):
						# index = len(items) // 2 when trying to find the median
						#   (value of index when items is sorted)

						# invalid input
						if index >= len(_lowercase		) or index < 0:
												return None

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

						# index is the pivot
						if m <= index < m + count:
												return pivot
						# must be in smaller
						elif m > index:
												return quick_select(_lowercase	,						_lowercase		)
						# must be in larger
						else:
												return quick_select(_lowercase	,						index - (m + count)		)





