
'''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  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self   ,       _lowercase=0   ,       _lowercase=(4, 4, 64, 64)   ,       _lowercase=False				):

												"""simple docstring"""



												_lowerCAmelCase  						=			jnp.bfloataa if fpaa else jnp.floataa
												_lowerCAmelCase  						=			jnp.array(load_hf_numpy(self.get_file_format(_lowercase   ,       _lowercase				)				)   ,       dtype=_lowercase				)
												return image




						def  _lowercase     ( self   ,       _lowercase=False   ,       _lowercase="CompVis/stable-diffusion-v1-4"				):

												"""simple docstring"""



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

												_lowerCAmelCase						,   _lowerCAmelCase  						=			FlaxUNetaDConditionModel.from_pretrained(
												    _lowercase   ,       subfolder="""unet"""   ,       dtype=_lowercase   ,       revision=_lowercase				)
												return model, params




						def  _lowercase     ( self   ,       _lowercase=0   ,       _lowercase=(4, 77, 768)   ,       _lowercase=False				):

												"""simple docstring"""



												_lowerCAmelCase  						=			jnp.bfloataa if fpaa else jnp.floataa
												_lowerCAmelCase  						=			jnp.array(load_hf_numpy(self.get_file_format(_lowercase   ,       _lowercase				)				)   ,       dtype=_lowercase				)
												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, 1_000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
						        # fmt: on
						    ]				)
						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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

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

												assert sample.shape == latents.shape

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

												# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
												assert jnp.allclose(_lowercase   ,       _lowercase   ,       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, 1_000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
						        # fmt: on
						    ]				)
						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""   ,       fpaa=_lowercase				)
												_lowerCAmelCase  						=			self.get_latents(_lowercase   ,       shape=(4, 4, 96, 96)   ,       fpaa=_lowercase				)
												_lowerCAmelCase  						=			self.get_encoder_hidden_states(_lowercase   ,       shape=(4, 77, 1_024)   ,       fpaa=_lowercase				)

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

												assert sample.shape == latents.shape

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

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


'''simple docstring'''



from ...configuration_utils import PretrainedConfig




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       '''bert-generation'''




						def __init__( self   ,       _lowercase=50_358   ,       _lowercase=1_024   ,       _lowercase=24   ,       _lowercase=16   ,       _lowercase=4_096   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=1   ,       _lowercase="absolute"   ,       _lowercase=True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			position_embedding_type
												_lowerCAmelCase  						=			use_cache
