


'''simple docstring'''
from sklearn.metrics import fa_score

import datasets


__snake_case				 =	'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


__snake_case				 =	'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


__snake_case				 =	'''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,						_KWARGS_DESCRIPTION   )
class   lowercase      (				datasets.Metric   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Sequence(datasets.Value('''int32'''				)				),
							            '''references''': datasets.Sequence(datasets.Value('''int32'''				)				),
							        }
							        if self.config_name == '''multilabel'''
							        else {
							            '''predictions''': datasets.Value('''int32'''				),
							            '''references''': datasets.Value('''int32'''				),
							        }				)       ,  reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html''']       ,  )




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_=None       ,  UpperCamelCase_=1       ,  UpperCamelCase_="binary"       ,  UpperCamelCase_=None				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					fa_score(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  labels=UpperCamelCase_       ,  pos_label=UpperCamelCase_       ,  average=UpperCamelCase_       ,  sample_weight=UpperCamelCase_				)
							return {"f1": float(UpperCamelCase_				) if score.size == 1 else score}



'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class   lowercase      (				A__      ,						A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							StableDiffusionInstructPixaPixPipeline
		_a       					=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		_a       					=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a       					=							IMAGE_TO_IMAGE_IMAGE_PARAMS




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.manual_seed(0				)
							UpperCamelCase__  :Optional[Any]				   =					UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=8       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=32       ,  )
							UpperCamelCase__  :List[str]				   =					PNDMScheduler(skip_prk_steps=UpperCamelCase_				)
							torch.manual_seed(0				)
							UpperCamelCase__  :Dict				   =					AutoencoderKL(
							    block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
							torch.manual_seed(0				)
							UpperCamelCase__  :int				   =					CLIPTextConfig(
							    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
							UpperCamelCase__  :Tuple				   =					CLIPTextModel(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

							UpperCamelCase__  :Dict				   =					{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					floats_tensor((1, 3, 32, 32)       ,  rng=random.Random(UpperCamelCase_				)				).to(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
							UpperCamelCase__  :List[Any]				   =					Image.fromarray(np.uinta(UpperCamelCase_				)				).convert('''RGB'''				)
							if str(UpperCamelCase_				).startswith('''mps'''				):
												UpperCamelCase__  :Tuple				   =					torch.manual_seed(UpperCamelCase_				)
							else:
												UpperCamelCase__  :List[Any]				   =					torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''image_guidance_scale''': 1,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :str				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :List[str]				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_components()
							UpperCamelCase__  :Any				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					'''french fries'''
							UpperCamelCase__  :Any				   =					sd_pipe(**UpperCamelCase_       ,  negative_prompt=UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					output.images
							UpperCamelCase__  :Any				   =					image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :List[str]				   =					np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Dict				   =					self.get_dummy_components()
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Dict				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					[inputs['''prompt''']] * 2

							UpperCamelCase__  :str				   =					np.array(inputs['''image''']				).astype(np.floataa				) / 255.0
							UpperCamelCase__  :int				   =					torch.from_numpy(UpperCamelCase_				).unsqueeze(0				).to(UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					image / 2 + 0.5
							UpperCamelCase__  :Union[str, Any]				   =					image.permute(0       ,  3       ,  1       ,  2				)
							UpperCamelCase__  :Union[str, Any]				   =					image.repeat(2       ,  1       ,  1       ,  1				)

							UpperCamelCase__  :Optional[Any]				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Any				   =					image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__  :List[Any]				   =					np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Any				   =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__  :Tuple				   =					self.get_dummy_components()
							UpperCamelCase__  :Union[str, Any]				   =					EulerAncestralDiscreteScheduler(
							    beta_start=0.00085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''				)
							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					sd_pipe.to(UpperCamelCase_				)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :str				   =					self.get_dummy_inputs(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					sd_pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Tuple				   =					image[0, -3:, -3:, -1]

							UpperCamelCase__  :Dict				   =					[round(UpperCamelCase_       ,  4				) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_				) for x in slice]				)				)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__  :Union[str, Any]				   =					np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().test_inference_batch_single_identical(expected_max_diff=3e-3				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_dummy_components()
							UpperCamelCase__  :Optional[Any]				   =					StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					VaeImageProcessor(do_resize=UpperCamelCase_       ,  do_normalize=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)				)[0]

							UpperCamelCase__  :List[Any]				   =					components['''vae''']
							UpperCamelCase__  :Optional[Any]				   =					self.get_dummy_inputs_by_type(UpperCamelCase_       ,  input_image_type='''pt'''				)

							for image_param in self.image_latents_params:
												if image_param in inputs.keys():
																	UpperCamelCase__  :List[str]				   =					vae.encode(inputs[image_param]				).latent_dist.mode()

							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				)[0]

							UpperCamelCase__  :Optional[int]				   =					np.abs(out - out_latents_inputs				).max()
							self.assertLess(UpperCamelCase_       ,  1e-4       ,  '''passing latents as image input generate different result from passing image'''				)



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

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=0				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					torch.manual_seed(UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
							UpperCamelCase__  :Dict				   =					{
							    '''prompt''': '''turn him into a cyborg''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''image_guidance_scale''': 1.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							UpperCamelCase__  :str				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[Any]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :str				   =					np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :int				   =					self.get_inputs()
							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :str				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Optional[int]				   =					np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_				)
							UpperCamelCase__  :str				   =					DDIMScheduler.from_config(pipe.scheduler.config				)
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Union[str, Any]				   =					self.get_inputs()
							UpperCamelCase__  :Dict				   =					pipe(**UpperCamelCase_				).images
							UpperCamelCase__  :Optional[int]				   =					image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__  :Tuple				   =					np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]				)

							assert np.abs(expected_slice - image_slice				).max() < 1e-3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					0

							def callback_fn(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				) -> None:
												UpperCamelCase__  :Optional[Any]				   =					True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Optional[Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[str]				   =					np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]				)

																	assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2
												elif step == 2:
																	UpperCamelCase__  :Union[str, Any]				   =					latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__  :Union[str, Any]				   =					latents[0, -3:, -3:, -1]
																	UpperCamelCase__  :List[Any]				   =					np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]				)

																	assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2

							UpperCamelCase__  :Union[str, Any]				   =					False

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :Dict				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :Dict				   =					self.get_inputs()
							pipe(**UpperCamelCase_       ,  callback=UpperCamelCase_       ,  callback_steps=1				)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCamelCase__  :Union[str, Any]				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''       ,  safety_checker=UpperCamelCase_       ,  torch_dtype=torch.floataa				)
							UpperCamelCase__  :List[str]				   =					pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing(1				)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__  :Any				   =					self.get_inputs()
							UpperCamelCase__  :Tuple				   =					pipe(**UpperCamelCase_				)

							UpperCamelCase__  :str				   =					torch.cuda.max_memory_allocated()
							# make sure that less than 2.2 GB is allocated
							assert mem_bytes < 2.2 * 10**9




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__  :int				   =					inputs['''image'''].resize((504, 504)				)

							UpperCamelCase__  :Union[str, Any]				   =					'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__  :str				   =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_       ,  safety_checker=UpperCamelCase_       ,  )
							pipe.to(UpperCamelCase_				)
							pipe.set_progress_bar_config(disable=UpperCamelCase_				)
							pipe.enable_attention_slicing()

							UpperCamelCase__  :List[str]				   =					pipe(**UpperCamelCase_				)
							UpperCamelCase__  :str				   =					output.images[0]

							UpperCamelCase__  :str				   =					image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__  :Dict				   =					np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 5e-3