



"""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      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

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

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

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0			,     _SCREAMING_SNAKE_CASE:						str=(4, 4, 64, 64)			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    Union[str, Any]				=			jnp.array(load_hf_numpy(self.get_file_format(__a			,     __a))			,     dtype=__a)
				return image

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="CompVis/stable-diffusion-v1-4")							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    List[Any]				=			"bf16" if fpaa else None

				__lowerCAmelCase		,      __lowerCAmelCase :    Tuple				=			FlaxUNetaDConditionModel.from_pretrained(
				    __a			,     subfolder="unet"			,     dtype=__a			,     revision=__a)
				return model, params

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			jnp.bfloataa if fpaa else jnp.floataa
				__lowerCAmelCase :    Any				=			jnp.array(load_hf_numpy(self.get_file_format(__a			,     __a))			,     dtype=__a)
				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:						str			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			List[str]:

				"""simple docstring"""

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

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

				assert sample.shape == latents.shape

				__lowerCAmelCase :    Dict				=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))			,     dtype=jnp.floataa)
				__lowerCAmelCase :    List[str]				=			jnp.array(__a			,     dtype=jnp.floataa)

				# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
				assert jnp.allclose(__a			,     __a			,     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:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			int:

				"""simple docstring"""

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

				__lowerCAmelCase :    Optional[Any]				=			model.apply(
				    {"params": params}			,     __a			,     jnp.array(__a			,     dtype=jnp.intaa)			,     encoder_hidden_states=__a			,     ).sample

				assert sample.shape == latents.shape

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

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


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import (
					    ImageTextPipelineOutput,
					    UniDiffuserPipeline,
					)
else:
					from .modeling_text_decoder import UniDiffuserTextDecoder
					from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
					from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
