






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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return F'''gaussian_noise_s={seed}_shape={"_".join([str(__lowerCAmelCase  ) for s in shape]  )}.npy'''







				def       A						(    self  ):
								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()







				def       A						(    self							,       __lowerCAmelCase=0							,       __lowerCAmelCase=(4, 4, 6_4, 6_4)							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   jnp.bfloataa if fpaa else jnp.floataa
								__magic_name__		:Any   =   jnp.array(load_hf_numpy(self.get_file_format(__lowerCAmelCase							,       __lowerCAmelCase  )  )							,       dtype=__lowerCAmelCase  )
								return image







				def       A						(    self							,       __lowerCAmelCase=False							,       __lowerCAmelCase="CompVis/stable-diffusion-v1-4"  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   jnp.bfloataa if fpaa else jnp.floataa
								__magic_name__		:List[str]   =   '''bf16''' if fpaa else None

								__magic_name__  ,					__magic_name__		:Optional[Any]   =   FlaxUNetaDConditionModel.from_pretrained(
								    __lowerCAmelCase							,       subfolder='''unet'''							,       dtype=__lowerCAmelCase							,       revision=__lowerCAmelCase  )
								return model, params







				def       A						(    self							,       __lowerCAmelCase=0							,       __lowerCAmelCase=(4, 7_7, 7_6_8)							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								__magic_name__		:int   =   jnp.bfloataa if fpaa else jnp.floataa
								__magic_name__		:str   =   jnp.array(load_hf_numpy(self.get_file_format(__lowerCAmelCase							,       __lowerCAmelCase  )  )							,       dtype=__lowerCAmelCase  )
								return hidden_states







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
				        [1_7, 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_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
				        # fmt: on
				    ]  )
				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''							,       fpaa=__lowerCAmelCase  )
								__magic_name__		:int   =   self.get_latents(__lowerCAmelCase							,       fpaa=__lowerCAmelCase  )
								__magic_name__		:Tuple   =   self.get_encoder_hidden_states(__lowerCAmelCase							,       fpaa=__lowerCAmelCase  )

								__magic_name__		:List[Any]   =   model.apply(
								    {'''params''': params}							,       __lowerCAmelCase							,       jnp.array(__lowerCAmelCase							,       dtype=jnp.intaa  )							,       encoder_hidden_states=__lowerCAmelCase							,       ).sample

								assert sample.shape == latents.shape

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

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







				@parameterized.expand(
				    [
				        # fmt: off
				        [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
				        [1_7, 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_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
				        # fmt: on
				    ]  )
				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__  ,					__magic_name__		:str   =   self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''							,       fpaa=__lowerCAmelCase  )
								__magic_name__		:List[str]   =   self.get_latents(__lowerCAmelCase							,       shape=(4, 4, 9_6, 9_6)							,       fpaa=__lowerCAmelCase  )
								__magic_name__		:List[Any]   =   self.get_encoder_hidden_states(__lowerCAmelCase							,       shape=(4, 7_7, 1_0_2_4)							,       fpaa=__lowerCAmelCase  )

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

								assert sample.shape == latents.shape

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

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













import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






@nightly
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

														assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




