




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        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[Any]      )  ->    int:





								'''simple docstring'''




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



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any=0		,	_UpperCAmelCase      :		Any=(4, 4, 64, 64)		,	_UpperCAmelCase      :		List[str]=False      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    Optional[Any]										=      jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase		,	_UpperCAmelCase      )      )		,	dtype=_UpperCAmelCase      )
								return image



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]=False		,	_UpperCAmelCase      :		List[str]="CompVis/stable-diffusion-v1-4"      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    str										=      """bf16""" if fpaa else None

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		List[str]=0		,	_UpperCAmelCase      :		Optional[int]=(4, 77, 768)		,	_UpperCAmelCase      :		str=False      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      self.get_latents(_UpperCAmelCase		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      self.get_encoder_hidden_states(_UpperCAmelCase		,	fpaa=_UpperCAmelCase      )

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

								assert sample.shape == latents.shape

								_lowerCAmelCase    :    Dict										=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )		,	dtype=jnp.floataa      )
								_lowerCAmelCase    :    Optional[Any]										=      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       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    List[Any]										=      self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      self.get_latents(_UpperCAmelCase		,	shape=(4, 4, 96, 96)		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_encoder_hidden_states(_UpperCAmelCase		,	shape=(4, 77, 1024)		,	fpaa=_UpperCAmelCase      )

								_lowerCAmelCase    :    List[str]										=      model.apply(
								    {"""params""": params}		,	_UpperCAmelCase		,	jnp.array(_UpperCAmelCase		,	dtype=jnp.intaa      )		,	encoder_hidden_states=_UpperCAmelCase		,	).sample

								assert sample.shape == latents.shape

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







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      [0] * len(UpperCamelCase_       )

				for i in range(1      ,    len(UpperCamelCase_       )       ):
								# use last results for better performance - dynamic programming
								_lowerCAmelCase    :    Union[str, Any]										=      prefix_result[i - 1]
								while j > 0 and input_string[i] != input_string[j]:
												_lowerCAmelCase    :    str										=      prefix_result[j - 1]

								if input_string[i] == input_string[j]:
												j += 1
								_lowerCAmelCase    :    Optional[int]										=      j

				return prefix_result




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return max(prefix_function(UpperCamelCase_       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

