



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= UnCLIPImageVariationPipeline
	_SCREAMING_SNAKE_CASE	:		Dict  						= IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
	_SCREAMING_SNAKE_CASE	:		int  						= IMAGE_VARIATION_BATCH_PARAMS

	_SCREAMING_SNAKE_CASE	:		Tuple  						= [
	    '''generator''',
	    '''return_dict''',
	    '''decoder_num_inference_steps''',
	    '''super_res_num_inference_steps''',
	]
	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= False





	@property
	def 			_lowerCAmelCase     (      self				:       int    )						->	str:





					"""simple docstring"""


					return 32





	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Union[str, Any]:





					"""simple docstring"""


					return 32





	@property
	def 			_lowerCAmelCase     (      self				:       int    )						->	Tuple:





					"""simple docstring"""


					return self.time_input_dim





	@property
	def 			_lowerCAmelCase     (      self				:       int    )						->	Any:





					"""simple docstring"""


					return self.time_input_dim * 4





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





					"""simple docstring"""


					return 100





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
					return tokenizer





	@property
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	str:





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     CLIPTextConfig(
					    bos_token_id=0     ,       eos_token_id=2     ,       hidden_size=self.text_embedder_hidden_size     ,       projection_dim=self.text_embedder_hidden_size     ,       intermediate_size=37     ,       layer_norm_eps=1E-0_5     ,       num_attention_heads=4     ,       num_hidden_layers=5     ,       pad_token_id=1     ,       vocab_size=1_000     ,       )
					return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     CLIPVisionConfig(
					    hidden_size=self.text_embedder_hidden_size     ,       projection_dim=self.text_embedder_hidden_size     ,       num_hidden_layers=5     ,       num_attention_heads=4     ,       image_size=32     ,       intermediate_size=37     ,       patch_size=1     ,       )
					return CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					torch.manual_seed(0    )

					SCREAMING_SNAKE_CASE						:							Dict							=     {
					    'clip_embeddings_dim': self.text_embedder_hidden_size,
					    'time_embed_dim': self.time_embed_dim,
					    'cross_attention_dim': self.cross_attention_dim,
					}

					SCREAMING_SNAKE_CASE						:							List[Any]							=     UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE    )
					return model





	@property
	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Any:





					"""simple docstring"""


					torch.manual_seed(0    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     {
					    'sample_size': 32,
					    # RGB in channels
					    'in_channels': 3,
					    # Out channels is double in channels because predicts mean and variance
					    'out_channels': 6,
					    '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,
					    'cross_attention_dim': self.cross_attention_dim,
					    'attention_head_dim': 4,
					    'resnet_time_scale_shift': 'scale_shift',
					    'class_embed_type': 'identity',
					}

					SCREAMING_SNAKE_CASE						:							Tuple							=     UNetaDConditionModel(**_SCREAMING_SNAKE_CASE    )
					return model





	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	List[Any]:





					"""simple docstring"""


					return {
					    "sample_size": 64,
					    "layers_per_block": 1,
					    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
					    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
					    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    "in_channels": 6,
					    "out_channels": 3,
					}





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





					"""simple docstring"""


					torch.manual_seed(0    )

					SCREAMING_SNAKE_CASE						:							str							=     UNetaDModel(**self.dummy_super_res_kwargs    )
					return model





	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					torch.manual_seed(1    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     UNetaDModel(**self.dummy_super_res_kwargs    )
					return model





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.dummy_decoder
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_text_proj
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.dummy_text_encoder
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_tokenizer
					SCREAMING_SNAKE_CASE						:							int							=     self.dummy_super_res_first
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.dummy_super_res_last

					SCREAMING_SNAKE_CASE						:							Any							=     UnCLIPScheduler(
					    variance_type='learned_range'     ,       prediction_type='epsilon'     ,       num_train_timesteps=1_000     ,       )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     UnCLIPScheduler(
					    variance_type='fixed_small_log'     ,       prediction_type='epsilon'     ,       num_train_timesteps=1_000     ,       )

					SCREAMING_SNAKE_CASE						:							Tuple							=     CLIPImageProcessor(crop_size=32     ,       size=32    )

					SCREAMING_SNAKE_CASE						:							Any							=     self.dummy_image_encoder

					return {
					    "decoder": decoder,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "text_proj": text_proj,
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder,
					    "super_res_first": super_res_first,
					    "super_res_last": super_res_last,
					    "decoder_scheduler": decoder_scheduler,
					    "super_res_scheduler": super_res_scheduler,
					}





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     floats_tensor((1, 3, 32, 32)     ,       rng=random.Random(_SCREAMING_SNAKE_CASE    )    ).to(_SCREAMING_SNAKE_CASE    )
					if str(_SCREAMING_SNAKE_CASE    ).startswith('mps'    ):
									SCREAMING_SNAKE_CASE						:							int							=     torch.manual_seed(_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							Dict							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(_SCREAMING_SNAKE_CASE    )

					if pil_image:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     input_image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE						:							List[str]							=     input_image.clamp(0     ,       1    )
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_image.cpu().permute(0     ,       2     ,       3     ,       1    ).float().numpy()
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE    )[0]

					return {
					    "image": input_image,
					    "generator": generator,
					    "decoder_num_inference_steps": 2,
					    "super_res_num_inference_steps": 2,
					    "output_type": "np",
					}





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     'cpu'

					SCREAMING_SNAKE_CASE						:							int							=     self.get_dummy_components()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     pipe.to(_SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output.images

					SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     pipe(
					    **_SCREAMING_SNAKE_CASE     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )[0]

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     image_from_tuple[0, -3:, -3:, -1]

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

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.array(
					    [
					        0.9_9_9_7,
					        0.0_0_0_2,
					        0.9_9_9_7,
					        0.9_9_9_7,
					        0.9_9_6_9,
					        0.0_0_2_3,
					        0.9_9_9_7,
					        0.9_9_6_9,
					        0.9_9_7_0,
					    ]    )

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





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     'cpu'

					SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_dummy_components()

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     pipe.to(_SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     output.images

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     pipe(
					    **_SCREAMING_SNAKE_CASE     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )[0]

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE						:							List[Any]							=     image_from_tuple[0, -3:, -3:, -1]

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

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]    )

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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     'cpu'

					SCREAMING_SNAKE_CASE						:							str							=     self.get_dummy_components()

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     pipe.to(_SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     [
					    pipeline_inputs['image'],
					    pipeline_inputs['image'],
					]

					SCREAMING_SNAKE_CASE						:							List[str]							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     output.images

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     [
					    tuple_pipeline_inputs['image'],
					    tuple_pipeline_inputs['image'],
					]

					SCREAMING_SNAKE_CASE						:							str							=     pipe(
					    **_SCREAMING_SNAKE_CASE     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )[0]

					SCREAMING_SNAKE_CASE						:							str							=     image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE						:							str							=     image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.array(
					    [
					        0.9_9_9_7,
					        0.9_9_8_9,
					        0.0_0_0_8,
					        0.0_0_2_1,
					        0.9_9_6_0,
					        0.0_0_1_8,
					        0.0_0_1_4,
					        0.0_0_0_2,
					        0.9_9_3_3,
					    ]    )

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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.device('cpu'    )
					class     lowerCAmelCase__						:


						'''simple docstring'''







						_SCREAMING_SNAKE_CASE	:		Dict  						= 1




					SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_dummy_components()

					SCREAMING_SNAKE_CASE						:							List[str]							=     self.pipeline_class(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pipe.to(_SCREAMING_SNAKE_CASE    )

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     torch.Generator(device=_SCREAMING_SNAKE_CASE    ).manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe.decoder.dtype
					SCREAMING_SNAKE_CASE						:							Dict							=     1

					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					SCREAMING_SNAKE_CASE						:							int							=     pipe.prepare_latents(
					    _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       latents=_SCREAMING_SNAKE_CASE     ,       scheduler=DummyScheduler()    )

					SCREAMING_SNAKE_CASE						:							Any							=     (
					    batch_size,
					    pipe.super_res_first.config.in_channels // 2,
					    pipe.super_res_first.config.sample_size,
					    pipe.super_res_first.config.sample_size,
					)
					SCREAMING_SNAKE_CASE						:							List[str]							=     pipe.prepare_latents(
					    _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       latents=_SCREAMING_SNAKE_CASE     ,       scheduler=DummyScheduler()    )

					SCREAMING_SNAKE_CASE						:							Any							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     pipe(
					    **_SCREAMING_SNAKE_CASE     ,       decoder_latents=_SCREAMING_SNAKE_CASE     ,       super_res_latents=_SCREAMING_SNAKE_CASE    ).images

					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,       pil_image=_SCREAMING_SNAKE_CASE    )
					# Don't pass image, instead pass embedding
					SCREAMING_SNAKE_CASE						:							str							=     pipeline_inputs.pop('image'    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe.image_encoder(_SCREAMING_SNAKE_CASE    ).image_embeds

					SCREAMING_SNAKE_CASE						:							List[str]							=     pipe(
					    **_SCREAMING_SNAKE_CASE     ,       decoder_latents=_SCREAMING_SNAKE_CASE     ,       super_res_latents=_SCREAMING_SNAKE_CASE     ,       image_embeddings=_SCREAMING_SNAKE_CASE     ,       ).images

					# make sure passing text embeddings manually is identical
					assert np.abs(img_out_a - img_out_a    ).max() < 1E-4





	@skip_mps
	def 			_lowerCAmelCase     (      self				:       Any    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     torch_device == 'cpu'

					# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
					SCREAMING_SNAKE_CASE						:							int							=     1E-2

					self._test_attention_slicing_forward_pass(
					    test_max_difference=_SCREAMING_SNAKE_CASE     ,       expected_max_diff=_SCREAMING_SNAKE_CASE    )





	@skip_mps
	def 			_lowerCAmelCase     (      self				:       str    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     torch_device == 'cpu'
					SCREAMING_SNAKE_CASE						:							int							=     True
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     [
					    'decoder_num_inference_steps',
					    'super_res_num_inference_steps',
					]

					self._test_inference_batch_single_identical(
					    test_max_difference=_SCREAMING_SNAKE_CASE     ,       relax_max_difference=_SCREAMING_SNAKE_CASE     ,       additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE     ,       )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     [
					    'decoder_num_inference_steps',
					    'super_res_num_inference_steps',
					]

					if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									SCREAMING_SNAKE_CASE						:							List[Any]							=     [2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=_SCREAMING_SNAKE_CASE     ,       additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE     ,       )
					else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE    )





	@skip_mps
	def 			_lowerCAmelCase     (      self				:       str    )						->	Any:





					"""simple docstring"""


					return super().test_dict_tuple_outputs_equivalent()





	@skip_mps
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Dict:





					"""simple docstring"""


					return super().test_save_load_local()







	@skip_mps
	def 			_lowerCAmelCase     (      self				:       int    )						->	List[str]:





					"""simple docstring"""


					return super().test_save_load_optional_components()




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


	'''simple docstring'''







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





					"""simple docstring"""


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







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     UnCLIPImageVariationPipeline.from_pretrained(
					    'kakaobrain/karlo-v1-alpha-image-variations'     ,       torch_dtype=torch.floataa    )
					SCREAMING_SNAKE_CASE						:							str							=     pipeline.to(_SCREAMING_SNAKE_CASE    )
					pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.Generator(device='cpu'    ).manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipeline(
					    _SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       output_type='np'     ,       )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     output.images[0]

					assert image.shape == (256, 256, 3)

					assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       15    )










"""simple docstring"""

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


A_						:    Union[str, Any]				=      False
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	pass




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


	'''simple docstring'''







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





					"""simple docstring"""


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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       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				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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





