


"""simple docstring"""







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 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       List[Any]    ,    UpperCAmelCase__    :       Tuple     ) ->			str:
						return F"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCAmelCase__     ) for s in shape]     )}.npy"""




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       Optional[int]=0    ,    UpperCAmelCase__    :       List[str]=(4, 4, 6_4, 6_4)    ,    UpperCAmelCase__    :       Union[str, Any]=False     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       jnp.bfloataa if fpaa else jnp.floataa
						__SCREAMING_SNAKE_CASE		     =       jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase__    ,    UpperCAmelCase__     )     )    ,    dtype=UpperCAmelCase__     )
						return image




	def 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Any="CompVis/stable-diffusion-v1-4"     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       jnp.bfloataa if fpaa else jnp.floataa
						__SCREAMING_SNAKE_CASE		     =       "bf16" if fpaa else None

						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       FlaxUNetaDConditionModel.from_pretrained(
						    UpperCAmelCase__    ,    subfolder="unet"    ,    dtype=UpperCAmelCase__    ,    revision=UpperCAmelCase__     )
						return model, params




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       Union[str, Any]=0    ,    UpperCAmelCase__    :       List[str]=(4, 7_7, 7_6_8)    ,    UpperCAmelCase__    :       Optional[Any]=False     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       jnp.bfloataa if fpaa else jnp.floataa
						__SCREAMING_SNAKE_CASE		     =       jnp.array(load_hf_numpy(self.get_file_format(UpperCAmelCase__    ,    UpperCAmelCase__     )     )    ,    dtype=UpperCAmelCase__     )
						return hidden_states




	@parameterized.expand(
	    [
	        # fmt: off
	        [8_3, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
	        [1_7, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
	        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
	        [3, 1_0_0_0, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
	        # fmt: on
	    ]     )
	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Dict:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,    fpaa=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.get_latents(UpperCAmelCase__    ,    fpaa=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.get_encoder_hidden_states(UpperCAmelCase__    ,    fpaa=UpperCAmelCase__     )

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

						assert sample.shape == latents.shape

						__SCREAMING_SNAKE_CASE		     =       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )    ,    dtype=jnp.floataa     )
						__SCREAMING_SNAKE_CASE		     =       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
	        [8_3, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
	        [1_7, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
	        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
	        [3, 1_0_0_0, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
	        # fmt: on
	    ]     )
	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str]     ) ->			str:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,    fpaa=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.get_latents(UpperCAmelCase__    ,    shape=(4, 4, 9_6, 9_6)    ,    fpaa=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.get_encoder_hidden_states(UpperCAmelCase__    ,    shape=(4, 7_7, 1_0_2_4)    ,    fpaa=UpperCAmelCase__     )

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

						assert sample.shape == latents.shape

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




"""simple docstring"""







from __future__ import annotations




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if len(lowerCAmelCase_     ) < 2:
										raise ValueError("Monogons and Digons are not polygons in the Euclidean space"     )
					if any(i <= 0 for i in nums     ):
										raise ValueError("All values must be greater than 0"     )
					__SCREAMING_SNAKE_CASE		     =       nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
