




'''simple docstring'''

import enum
import shutil
import sys


a							, a       :						List[str]								=		shutil.get_terminal_size()

a       :						str								=		{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}



class 		SCREAMING_SNAKE_CASE__  (			enum.Enum	):
			__SCREAMING_SNAKE_CASE    =			0
			__SCREAMING_SNAKE_CASE    =			1







def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   Tuple=""						)      ->      List[Any]:
			sys.stdout.write(str(_UpperCAmelCase						) + end						)
			sys.stdout.flush()


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   int		,				_UpperCAmelCase		:   List[str]=""						)      ->      Optional[int]:
			forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''		,				_UpperCAmelCase						)


def __UpperCAmelCase	(   )      ->      Union[str, Any]:
			forceWrite("\r"						)


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   str						)      ->      Optional[Any]:
			forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''						)


def __UpperCAmelCase	(   )      ->      int:
			forceWrite(" " * TERMINAL_WIDTH						)
			reset_cursor()






def __UpperCAmelCase	(   )      ->      Optional[int]:
			reset_cursor()
			forceWrite("-" * TERMINAL_WIDTH						)











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 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:int						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								return F'gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__							) for s in shape]							)}.npy'
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=0						, __magic_name__		:List[Any]=(4, 4, 64, 64)						, __magic_name__		:Optional[int]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return image
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=False						, __magic_name__		:List[str]="CompVis/stable-diffusion-v1-4"							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				"""bf16""" if fpaa else None

								a							,					a									=				FlaxUNetaDConditionModel.from_pretrained(
								    __magic_name__						, subfolder="""unet"""						, dtype=__magic_name__						, revision=__magic_name__							)
								return model, params
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Union[str, Any]=0						, __magic_name__		:Any=(4, 77, 768)						, __magic_name__		:Optional[Any]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								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  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, 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  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[Any]						, __magic_name__		:int						, __magic_name__		:str							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, shape=(4, 4, 96, 96)						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, shape=(4, 77, 1024)						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

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

