"""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 		UpperCamelCase      (   UpperCAmelCase__				,     unittest.TestCase						):
				UpperCAmelCase    :				Dict   =		UnCLIPImageVariationPipeline
				UpperCAmelCase    :				Union[str, Any]   =		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
				UpperCAmelCase    :				Dict   =		IMAGE_VARIATION_BATCH_PARAMS

				UpperCAmelCase    :				Any   =		[
				    'generator',
				    'return_dict',
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]
				UpperCAmelCase    :				List[Any]   =		False



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     str:
					return 32



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return self.time_input_dim



				@property
				def  _lowercase					(self    :				Any)   ->     int:
					return self.time_input_dim * 4



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Any:
					return 1_00



				@property
				def  _lowercase					(self    :				List[str])   ->     Optional[int]:
					__snake_case      :							List[str]						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					torch.manual_seed(0)
					__snake_case      :							List[str]						=			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-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=10_00     ,  )
					return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     str:
					torch.manual_seed(0)
					__snake_case      :							str						=			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  _lowercase					(self    :				List[Any])   ->     Any:
					torch.manual_seed(0)

					__snake_case      :							Any						=			{
					    """clip_embeddings_dim""": self.text_embedder_hidden_size,
					    """time_embed_dim""": self.time_embed_dim,
					    """cross_attention_dim""": self.cross_attention_dim,
					}

					__snake_case      :							List[str]						=			UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE)
					return model



				@property
				def  _lowercase					(self    :				List[str])   ->     List[str]:
					torch.manual_seed(0)

					__snake_case      :							Optional[int]						=			{
					    """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""",
					}

					__snake_case      :							List[Any]						=			UNetaDConditionModel(**_SCREAMING_SNAKE_CASE)
					return model



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Optional[Any]:
					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  _lowercase					(self    :				str)   ->     str:
					torch.manual_seed(0)

					__snake_case      :							Dict						=			UNetaDModel(**self.dummy_super_res_kwargs)
					return model



				@property
				def  _lowercase					(self    :				Optional[int])   ->     int:
					torch.manual_seed(1)

					__snake_case      :							List[Any]						=			UNetaDModel(**self.dummy_super_res_kwargs)
					return model



				def  _lowercase					(self    :				List[str])   ->     Dict:
					__snake_case      :							Any						=			self.dummy_decoder
					__snake_case      :							Tuple						=			self.dummy_text_proj
					__snake_case      :							Optional[Any]						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Optional[Any]						=			self.dummy_super_res_first
					__snake_case      :							Tuple						=			self.dummy_super_res_last

					__snake_case      :							Tuple						=			UnCLIPScheduler(
					    variance_type='learned_range'     ,  prediction_type='epsilon'     ,  num_train_timesteps=10_00     ,  )

					__snake_case      :							int						=			UnCLIPScheduler(
					    variance_type='fixed_small_log'     ,  prediction_type='epsilon'     ,  num_train_timesteps=10_00     ,  )

					__snake_case      :							List[Any]						=			CLIPImageProcessor(crop_size=32     ,  size=32)

					__snake_case      :							int						=			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  _lowercase					(self    :				Union[str, Any]     ,  _A    :				Optional[Any]     ,  _A    :				Optional[Any]=0     ,  _A    :				str=True)   ->     Optional[int]:
					__snake_case      :							List[str]						=			floats_tensor((1, 3, 32, 32)     ,  rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
					if str(_SCREAMING_SNAKE_CASE).startswith('mps'):
						__snake_case      :							int						=			torch.manual_seed(_SCREAMING_SNAKE_CASE)
					else:
						__snake_case      :							Optional[int]						=			torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)

					if pil_image:
						__snake_case      :							Union[str, Any]						=			input_image * 0.5 + 0.5
						__snake_case      :							List[str]						=			input_image.clamp(0     ,  1)
						__snake_case      :							Optional[Any]						=			input_image.cpu().permute(0     ,  2     ,  3     ,  1).float().numpy()
						__snake_case      :							Union[str, Any]						=			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  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					__snake_case      :							Union[str, Any]						=			"""cpu"""

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

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

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

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

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

					__snake_case      :							Optional[int]						=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,  pil_image=_SCREAMING_SNAKE_CASE)

					__snake_case      :							Any						=			pipe(
					    **_SCREAMING_SNAKE_CASE     ,  return_dict=_SCREAMING_SNAKE_CASE     ,  )[0]

					__snake_case      :							Optional[int]						=			image[0, -3:, -3:, -1]
					__snake_case      :							str						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							int						=			np.array(
					    [
					        0.9_997,
					        0.0_002,
					        0.9_997,
					        0.9_997,
					        0.9_969,
					        0.0_023,
					        0.9_997,
					        0.9_969,
					        0.9_970,
					    ])

					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  _lowercase					(self    :				int)   ->     str:
					__snake_case      :							Optional[Any]						=			"""cpu"""

					__snake_case      :							List[Any]						=			self.get_dummy_components()

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

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

					__snake_case      :							List[str]						=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,  pil_image=_SCREAMING_SNAKE_CASE)

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

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

					__snake_case      :							Tuple						=			pipe(
					    **_SCREAMING_SNAKE_CASE     ,  return_dict=_SCREAMING_SNAKE_CASE     ,  )[0]

					__snake_case      :							Tuple						=			image[0, -3:, -3:, -1]
					__snake_case      :							int						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							Optional[int]						=			np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971])

					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  _lowercase					(self    :				List[Any])   ->     Optional[int]:
					__snake_case      :							List[str]						=			"""cpu"""

					__snake_case      :							str						=			self.get_dummy_components()

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

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

					__snake_case      :							Any						=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,  pil_image=_SCREAMING_SNAKE_CASE)
					__snake_case      :							str						=			[
					    pipeline_inputs["""image"""],
					    pipeline_inputs["""image"""],
					]

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

					__snake_case      :							str						=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,  pil_image=_SCREAMING_SNAKE_CASE)
					__snake_case      :							Dict						=			[
					    tuple_pipeline_inputs["""image"""],
					    tuple_pipeline_inputs["""image"""],
					]

					__snake_case      :							Dict						=			pipe(
					    **_SCREAMING_SNAKE_CASE     ,  return_dict=_SCREAMING_SNAKE_CASE     ,  )[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							List[Any]						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							Any						=			np.array(
					    [
					        0.9_997,
					        0.9_989,
					        0.0_008,
					        0.0_021,
					        0.9_960,
					        0.0_018,
					        0.0_014,
					        0.0_002,
					        0.9_933,
					    ])

					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  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					__snake_case      :							Dict						=			torch.device('cpu')

					class 		UpperCamelCase      :
									UpperCAmelCase    :				Optional[Any]   =		1







					__snake_case      :							List[Any]						=			self.get_dummy_components()

					__snake_case      :							str						=			self.pipeline_class(**_SCREAMING_SNAKE_CASE)
					__snake_case      :							int						=			pipe.to(_SCREAMING_SNAKE_CASE)

					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

					__snake_case      :							List[str]						=			torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
					__snake_case      :							Tuple						=			pipe.decoder.dtype
					__snake_case      :							Any						=			1

					__snake_case      :							List[str]						=			(
					    batch_size,
					    pipe.decoder.config.in_channels,
					    pipe.decoder.config.sample_size,
					    pipe.decoder.config.sample_size,
					)
					__snake_case      :							Dict						=			pipe.prepare_latents(
					    _SCREAMING_SNAKE_CASE     ,  dtype=_SCREAMING_SNAKE_CASE     ,  device=_SCREAMING_SNAKE_CASE     ,  generator=_SCREAMING_SNAKE_CASE     ,  latents=_SCREAMING_SNAKE_CASE     ,  scheduler=DummyScheduler())

					__snake_case      :							List[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,
					)
					__snake_case      :							Optional[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())

					__snake_case      :							str						=			self.get_dummy_inputs(_SCREAMING_SNAKE_CASE     ,  pil_image=_SCREAMING_SNAKE_CASE)

					__snake_case      :							str						=			pipe(
					    **_SCREAMING_SNAKE_CASE     ,  decoder_latents=_SCREAMING_SNAKE_CASE     ,  super_res_latents=_SCREAMING_SNAKE_CASE).images

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

					__snake_case      :							Dict						=			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  _lowercase					(self    :				Optional[Any])   ->     Any:
					__snake_case      :							Tuple						=			torch_device == """cpu"""

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

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



				@skip_mps
				def  _lowercase					(self    :				str)   ->     Any:
					__snake_case      :							int						=			torch_device == """cpu"""
					__snake_case      :							int						=			True
					__snake_case      :							Optional[Any]						=			[
					    """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  _lowercase					(self    :				List[Any])   ->     str:
					__snake_case      :							Union[str, Any]						=			[
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]

					if torch_device == "mps":
						# TODO: MPS errors with larger batch sizes
						__snake_case      :							Optional[int]						=			[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  _lowercase					(self    :				Dict)   ->     List[str]:
					return super().test_dict_tuple_outputs_equivalent()



				@skip_mps
				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					return super().test_save_load_local()






				@skip_mps
				def  _lowercase					(self    :				List[Any])   ->     Tuple:
					return super().test_save_load_optional_components()







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



				def  _lowercase					(self    :				Union[str, Any])   ->     str:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






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

					__snake_case      :							Optional[Any]						=			UnCLIPImageVariationPipeline.from_pretrained(
					    'kakaobrain/karlo-v1-alpha-image-variations'     ,  torch_dtype=torch.floataa)
					__snake_case      :							Dict						=			pipeline.to(_SCREAMING_SNAKE_CASE)
					pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

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

					__snake_case      :							str						=			output.images[0]

					assert image.shape == (2_56, 2_56, 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 VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_a					:					int=			False

class 		UpperCamelCase      (   unittest.TestCase						):
				pass







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



				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def  _lowercase					(self    :				int)   ->     Tuple:
					__snake_case      :							str						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							int						=			load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')

					__snake_case      :							List[Any]						=			torch.manual_seed(0)
					__snake_case      :							Optional[int]						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=2     ,  output_type='numpy'     ,  ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(_A)
						__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained(_A     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			generator.manual_seed(0)
					__snake_case      :							int						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  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  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			'cyberpunk 2077'
					__snake_case      :							int						=			load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg')
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe.dual_guided(
					    prompt=_A     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy'     ,  ).images

					__snake_case      :							Optional[Any]						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Tuple						=			np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])

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

					__snake_case      :							List[str]						=			'A painting of a squirrel eating a burger '
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							str						=			pipe.text_to_image(
					    prompt=_A     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy').images

					__snake_case      :							str						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Any						=			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

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

					__snake_case      :							List[str]						=			pipe.image_variation(_A     ,  generator=_A     ,  output_type='numpy').images

					__snake_case      :							Dict						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							List[Any]						=			np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])

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





