




"""simple docstring"""






import datasets

from .evaluate import evaluate


A 			=  """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

A 			=  """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

A 			=  """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,      _KWARGS_DESCRIPTION							)
class 				__lowercase			(			datasets.Metric							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION	,			citation=_CITATION	,			inputs_description=_KWARGS_DESCRIPTION	,			features=datasets.Features(
									        {
									            '''predictions''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''     )     ),
									            },
									            '''references''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''answers''': datasets.features.Sequence(
									                    {
									                        '''text''': datasets.Value('''string'''     ),
									                        '''answer_start''': datasets.Value('''int32'''     ),
									                    }     ),
									            },
									        }     )	,			codebase_urls=['''https://www.atticusprojectai.org/cuad''']	,			reference_urls=['''https://www.atticusprojectai.org/cuad''']	,			)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									__a :						Union[str, Any]			       =  [
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									__a :						List[Any]			       =  evaluate(dataset=_UpperCAmelCase	,			predictions=_UpperCAmelCase     )
									return 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			(			_UpperCamelCase   ,      _UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




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







					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						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	,			)
									__a :						List[str]			       =  PNDMScheduler(skip_prk_steps=_UpperCAmelCase     )
									torch.manual_seed(0     )
									__a :						int			       =  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     )
									__a :						str			       =  CLIPTextConfig(
									    bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			)
									__a :						Optional[int]			       =  CLIPTextModel(_UpperCAmelCase     )
									__a :						str			       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

									__a :						Union[str, Any]			       =  {
									    '''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     ):
									__a :						Any			       =  floats_tensor((1, 3, 32, 32)	,			rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  image.cpu().permute(0	,			2	,			3	,			1     )[0]
									__a :						Tuple			       =  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert('''RGB'''     )
									if str(_UpperCAmelCase     ).startswith('''mps'''     ):
													__a :						Union[str, Any]			       =  torch.manual_seed(_UpperCAmelCase     )
									else:
													__a :						Union[str, Any]			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )
									__a :						Union[str, 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     ):
									__a :						List[Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						int			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						str			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						List[Any]			       =  image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									__a :						Optional[int]			       =  np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Optional[Any]			       =  self.get_dummy_components()
									__a :						Dict			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[Any]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  '''french fries'''
									__a :						str			       =  sd_pipe(**_UpperCAmelCase	,			negative_prompt=_UpperCAmelCase     )
									__a :						Dict			       =  output.images
									__a :						Optional[int]			       =  image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a :						Tuple			       =  np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						List[str]			       =  [inputs['''prompt''']] * 2

									__a :						Optional[Any]			       =  np.array(inputs['''image''']     ).astype(np.floataa     ) / 2_5_5.0
									__a :						Optional[Any]			       =  torch.from_numpy(_UpperCAmelCase     ).unsqueeze(0     ).to(_UpperCAmelCase     )
									__a :						Tuple			       =  image / 2 + 0.5
									__a :						str			       =  image.permute(0	,			3	,			1	,			2     )
									__a :						List[str]			       =  image.repeat(2	,			1	,			1	,			1     )

									__a :						int			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						Optional[Any]			       =  image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									__a :						List[str]			       =  np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						List[str]			       =  self.get_dummy_components()
									__a :						Union[str, Any]			       =  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule='''scaled_linear'''     )
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[str]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Any			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						Dict			       =  image[0, -3:, -3:, -1]

									__a :						Optional[int]			       =  [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)
									__a :						int			       =  np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]     )

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







					def  _lowerCamelCase		(						self     ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.get_dummy_components()
									__a :						Optional[Any]			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Any			       =  VaeImageProcessor(do_resize=_UpperCAmelCase	,			do_normalize=_UpperCAmelCase     )
									__a :						Dict			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						str			       =  pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase	,			input_image_type='''pt'''     )     )[0]

									__a :						List[Any]			       =  components['''vae''']
									__a :						List[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():
																	__a :						Optional[Any]			       =  vae.encode(inputs[image_param]     ).latent_dist.mode()

									__a :						str			       =  pipe(**_UpperCAmelCase     )[0]

									__a :						Union[str, Any]			       =  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     ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=0     ):
									__a :						List[str]			       =  torch.manual_seed(_UpperCAmelCase     )
									__a :						str			       =  load_image(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''     )
									__a :						List[str]			       =  {
									    '''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     ):
									__a :						List[str]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									__a :						Optional[Any]			       =  pipe(**_UpperCAmelCase     ).images
									__a :						List[str]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  DDIMScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									__a :						Any			       =  np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  0

									def callback_fn(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ) -> None:
													__a :						Optional[Any]			       =  True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																	__a :						Optional[int]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						int			       =  latents[0, -3:, -3:, -1]
																	__a :						int			       =  np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]     )

																	assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
													elif step == 2:
																	__a :						Union[str, Any]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						List[str]			       =  latents[0, -3:, -3:, -1]
																	__a :						Tuple			       =  np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]     )

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

									__a :						Union[str, Any]			       =  False

									__a :						Optional[int]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									pipe(**_UpperCAmelCase	,			callback=_UpperCAmelCase	,			callback_steps=1     )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







					def  _lowerCamelCase		(						self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing(1     )
									pipe.enable_sequential_cpu_offload()

									__a :						List[Any]			       =  self.get_inputs()
									__a :						Tuple			       =  pipe(**_UpperCAmelCase     )

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

									__a :						Tuple			       =  '''timbrooks/instruct-pix2pix'''
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    _UpperCAmelCase	,			safety_checker=_UpperCAmelCase	,			)
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[Any]			       =  pipe(**_UpperCAmelCase     )
									__a :						int			       =  output.images[0]

									__a :						Optional[int]			       =  image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									__a :						Union[str, Any]			       =  np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]     )

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