




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class       SCREAMING_SNAKE_CASE_							(					unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					int			  =		(ScoreSdeVeScheduler,)
	__magic_name__      :					Union[str, Any]			  =		()






	@property
	def 				lowerCamelCase__    (				self		:	str  )  ->					Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			4
						__UpperCamelCase				:  Dict								=			3
						__UpperCamelCase				:  Dict								=			8
						__UpperCamelCase				:  Tuple								=			8

						__UpperCamelCase				:  int								=			torch.rand((batch_size, num_channels, height, width)  )

						return sample






	@property
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			4
						__UpperCamelCase				:  List[Any]								=			3
						__UpperCamelCase				:  Dict								=			8
						__UpperCamelCase				:  List[str]								=			8

						__UpperCamelCase				:  Tuple								=			batch_size * num_channels * height * width
						__UpperCamelCase				:  Any								=			torch.arange(lowerCAmelCase  )
						__UpperCamelCase				:  Dict								=			sample.reshape(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )
						__UpperCamelCase				:  str								=			sample / num_elems
						__UpperCamelCase				:  List[str]								=			sample.permute(3				,    0				,    1				,    2  )

						return sample






	def 				lowerCamelCase__    (				self		:	int  )  ->					Any:







						"""simple docstring"""







						def model(lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Any				,    *lowerCAmelCase		:	str  ):
											return sample * t / (t + 1)

						return model






	def 				lowerCamelCase__    (				self		:	Any				,    **lowerCAmelCase		:	int  )  ->					Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			{
						    """num_train_timesteps""": 2000,
						    """snr""": 0.15,
						    """sigma_min""": 0.01,
						    """sigma_max""": 1348,
						    """sampling_eps""": 1E-5,
						}

						config.update(**lowerCAmelCase  )
						return config






	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	Optional[Any]=0				,    **lowerCAmelCase		:	int  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			dict(self.forward_default_kwargs  )

						for scheduler_class in self.scheduler_classes:
											__UpperCamelCase				:  Union[str, Any]								=			self.dummy_sample
											__UpperCamelCase				:  Union[str, Any]								=			0.1 * sample

											__UpperCamelCase				:  Optional[Any]								=			self.get_scheduler_config(**lowerCAmelCase  )
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase  )
																__UpperCamelCase				:  List[str]								=			scheduler_class.from_pretrained(lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  Optional[int]								=			new_scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample

											assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

											__UpperCamelCase				:  List[str]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  Any								=			new_scheduler.step_correct(
											    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample

											assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"






	def 				lowerCamelCase__    (				self		:	Optional[Any]				,    lowerCAmelCase		:	List[Any]=0				,    **lowerCAmelCase		:	List[str]  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			dict(self.forward_default_kwargs  )
						kwargs.update(lowerCAmelCase  )

						for scheduler_class in self.scheduler_classes:
											__UpperCamelCase				:  List[Any]								=			self.dummy_sample
											__UpperCamelCase				:  List[str]								=			0.1 * sample

											__UpperCamelCase				:  Dict								=			self.get_scheduler_config()
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase  )
																__UpperCamelCase				:  Optional[int]								=			scheduler_class.from_pretrained(lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  int								=			new_scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample

											assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

											__UpperCamelCase				:  Union[str, Any]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  List[str]								=			new_scheduler.step_correct(
											    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample

											assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler correction are not identical"






	def 				lowerCamelCase__    (				self		:	Any  )  ->					str:







						"""simple docstring"""







						for timesteps in [10, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					str:







						"""simple docstring"""







						for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				,    [1, 100, 1000]  ):
											self.check_over_configs(sigma_min=lowerCAmelCase				,    sigma_max=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					List[str]:







						"""simple docstring"""







						for t in [0.1, 0.5, 0.75]:
											self.check_over_forward(time_step=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			dict(self.forward_default_kwargs  )

						__UpperCamelCase				:  List[Any]								=			self.scheduler_classes[0]
						__UpperCamelCase				:  Dict								=			self.get_scheduler_config()
						__UpperCamelCase				:  Any								=			scheduler_class(**lowerCAmelCase  )

						__UpperCamelCase				:  Dict								=			3

						__UpperCamelCase				:  List[str]								=			self.dummy_model()
						__UpperCamelCase				:  Union[str, Any]								=			self.dummy_sample_deter

						scheduler.set_sigmas(lowerCAmelCase  )
						scheduler.set_timesteps(lowerCAmelCase  )
						__UpperCamelCase				:  Any								=			torch.manual_seed(0  )

						for i, t in enumerate(scheduler.timesteps  ):
											__UpperCamelCase				:  Union[str, Any]								=			scheduler.sigmas[i]

											for _ in range(scheduler.config.correct_steps  ):
																with torch.no_grad():
																					__UpperCamelCase				:  Union[str, Any]								=			model(lowerCAmelCase				,    lowerCAmelCase  )
																__UpperCamelCase				:  Optional[int]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=lowerCAmelCase				,    **lowerCAmelCase  ).prev_sample

											with torch.no_grad():
																__UpperCamelCase				:  Dict								=			model(lowerCAmelCase				,    lowerCAmelCase  )

											__UpperCamelCase				:  Optional[int]								=			scheduler.step_pred(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=lowerCAmelCase				,    **lowerCAmelCase  )
											__UpperCamelCase				:  Optional[int]								=			output.prev_sample, output.prev_sample_mean

						__UpperCamelCase				:  Any								=			torch.sum(torch.abs(lowerCAmelCase  )  )
						__UpperCamelCase				:  Dict								=			torch.mean(torch.abs(lowerCAmelCase  )  )

						assert np.isclose(result_sum.item()				,    14372758528.0  )
						assert np.isclose(result_mean.item()				,    18714530.0  )






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Tuple:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			dict(self.forward_default_kwargs  )

						__UpperCamelCase				:  List[Any]								=			kwargs.pop("""num_inference_steps"""				,    lowerCAmelCase  )

						for scheduler_class in self.scheduler_classes:
											__UpperCamelCase				:  List[str]								=			self.get_scheduler_config()
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			self.dummy_sample
											__UpperCamelCase				:  Tuple								=			0.1 * sample

											if num_inference_steps is not None and hasattr(lowerCAmelCase				,    """set_timesteps"""  ):
																scheduler.set_timesteps(lowerCAmelCase  )
											elif num_inference_steps is not None and not hasattr(lowerCAmelCase				,    """set_timesteps"""  ):
																__UpperCamelCase				:  Union[str, Any]								=			num_inference_steps

											__UpperCamelCase				:  List[str]								=			scheduler.step_pred(lowerCAmelCase				,    0				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  Tuple								=			scheduler.step_pred(lowerCAmelCase				,    1				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample

											self.assertEqual(output_a.shape				,    sample.shape  )
											self.assertEqual(output_a.shape				,    output_a.shape  )












import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					str			  =		KandinskyPipeline
	__magic_name__      :					Optional[Any]			  =		[
	    'prompt',
	    'image_embeds',
	    'negative_image_embeds',
	]
	__magic_name__      :					int			  =		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
	__magic_name__      :					List[str]			  =		[
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__magic_name__      :					Optional[Any]			  =		False






	@property
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Dict:







						"""simple docstring"""







						return 32






	@property
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Optional[int]:







						"""simple docstring"""







						return 32






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					str:







						"""simple docstring"""







						return self.time_input_dim






	@property
	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					str:







						"""simple docstring"""







						return self.time_input_dim * 4






	@property
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					str:







						"""simple docstring"""







						return 100






	@property
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
						return tokenizer






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					List[str]:







						"""simple docstring"""







						torch.manual_seed(0  )
						__UpperCamelCase				:  Dict								=			MCLIPConfig(
						    numDims=self.cross_attention_dim				,    transformerDimensions=self.text_embedder_hidden_size				,    hidden_size=self.text_embedder_hidden_size				,    intermediate_size=37				,    num_attention_heads=4				,    num_hidden_layers=5				,    vocab_size=1005				,    )

						__UpperCamelCase				:  Union[str, Any]								=			MultilingualCLIP(lowerCAmelCase  )
						__UpperCamelCase				:  Any								=			text_encoder.eval()

						return text_encoder






	@property
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Dict:







						"""simple docstring"""







						torch.manual_seed(0  )

						__UpperCamelCase				:  str								=			{
						    """in_channels""": 4,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						__UpperCamelCase				:  int								=			UNetaDConditionModel(**lowerCAmelCase  )
						return model






	@property
	def 				lowerCamelCase__    (				self		:	str  )  ->					str:







						"""simple docstring"""







						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}






	@property
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					Optional[Any]:







						"""simple docstring"""







						torch.manual_seed(0  )
						__UpperCamelCase				:  Union[str, Any]								=			VQModel(**self.dummy_movq_kwargs  )
						return model






	def 				lowerCamelCase__    (				self		:	Any  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			self.dummy_text_encoder
						__UpperCamelCase				:  Optional[Any]								=			self.dummy_tokenizer
						__UpperCamelCase				:  Optional[Any]								=			self.dummy_unet
						__UpperCamelCase				:  List[str]								=			self.dummy_movq

						__UpperCamelCase				:  Any								=			DDIMScheduler(
						    num_train_timesteps=1000				,    beta_schedule="""linear"""				,    beta_start=0.0_00_85				,    beta_end=0.0_12				,    clip_sample=lowerCAmelCase				,    set_alpha_to_one=lowerCAmelCase				,    steps_offset=1				,    prediction_type="""epsilon"""				,    thresholding=lowerCAmelCase				,    )

						__UpperCamelCase				:  Any								=			{
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}
						return components






	def 				lowerCamelCase__    (				self		:	Optional[int]				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Dict=0  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  str								=			floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(lowerCAmelCase  )  ).to(lowerCAmelCase  )
						__UpperCamelCase				:  Dict								=			floats_tensor((1, self.cross_attention_dim)				,    rng=random.Random(seed + 1  )  ).to(lowerCAmelCase  )
						if str(lowerCAmelCase  ).startswith("""mps"""  ):
											__UpperCamelCase				:  Union[str, Any]								=			torch.manual_seed(lowerCAmelCase  )
						else:
											__UpperCamelCase				:  Dict								=			torch.Generator(device=lowerCAmelCase  ).manual_seed(lowerCAmelCase  )
						__UpperCamelCase				:  int								=			{
						    """prompt""": """horse""",
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """guidance_scale""": 4.0,
						    """num_inference_steps""": 2,
						    """output_type""": """np""",
						}
						return inputs






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					List[str]:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			"""cpu"""

						__UpperCamelCase				:  Optional[Any]								=			self.get_dummy_components()

						__UpperCamelCase				:  Optional[Any]								=			self.pipeline_class(**lowerCAmelCase  )
						__UpperCamelCase				:  Tuple								=			pipe.to(lowerCAmelCase  )

						pipe.set_progress_bar_config(disable=lowerCAmelCase  )

						__UpperCamelCase				:  Dict								=			pipe(**self.get_dummy_inputs(lowerCAmelCase  )  )
						__UpperCamelCase				:  Dict								=			output.images

						__UpperCamelCase				:  Optional[Any]								=			pipe(
						    **self.get_dummy_inputs(lowerCAmelCase  )				,    return_dict=lowerCAmelCase				,    )[0]

						__UpperCamelCase				:  Dict								=			image[0, -3:, -3:, -1]
						__UpperCamelCase				:  Dict								=			image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)

						__UpperCamelCase				:  Tuple								=			np.array(
						    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]  )

						assert (
						    np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class       SCREAMING_SNAKE_CASE_							(					unittest.TestCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Optional[Any]:







						"""simple docstring"""







						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""  )

						__UpperCamelCase				:  str								=			KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""				,    torch_dtype=torch.floataa  )
						pipe_prior.to(lowerCAmelCase  )

						__UpperCamelCase				:  Union[str, Any]								=			KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""				,    torch_dtype=torch.floataa  )
						__UpperCamelCase				:  Union[str, Any]								=			pipeline.to(lowerCAmelCase  )
						pipeline.set_progress_bar_config(disable=lowerCAmelCase  )

						__UpperCamelCase				:  List[Any]								=			"""red cat, 4k photo"""

						__UpperCamelCase				:  List[str]								=			torch.Generator(device="""cuda"""  ).manual_seed(0  )
						__UpperCamelCase			, __UpperCamelCase				:  List[Any]								=			pipe_prior(
						    lowerCAmelCase				,    generator=lowerCAmelCase				,    num_inference_steps=5				,    negative_prompt=""""""				,    ).to_tuple()

						__UpperCamelCase				:  List[Any]								=			torch.Generator(device="""cuda"""  ).manual_seed(0  )
						__UpperCamelCase				:  int								=			pipeline(
						    lowerCAmelCase				,    image_embeds=lowerCAmelCase				,    negative_image_embeds=lowerCAmelCase				,    generator=lowerCAmelCase				,    num_inference_steps=100				,    output_type="""np"""				,    )

						__UpperCamelCase				:  Optional[Any]								=			output.images[0]

						assert image.shape == (512, 512, 3)

						assert_mean_pixel_difference(lowerCAmelCase				,    lowerCAmelCase  )






