



import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image

			from transformers.image_utils import load_image
else:

			class        UpperCAmelCase_  :

									'''simple docstring'''




									@staticmethod
									def 	_A     (   *_A				,							**_A		):







																'''simple docstring'''

																pass







			def      __lowercase       (			a__       )    ->			Tuple:
										return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCAmelCase__		:					Union[str, Any]							=(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)

@is_pipeline_test
@require_torch
@require_vision
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING






						@require_pytesseract
						@require_vision
						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model=_A				,							tokenizer=_A				,							image_processor=_A		)

													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= list(zip(*apply_tesseract(load_image(_A		)				,							_A				,							''		)		)		)
													__SCREAMING_SNAKE_CASE									= 'What is the placebo?'
													__SCREAMING_SNAKE_CASE									= [
													    {
													        'image': load_image(_A		),
													        'question': question,
													    },
													    {
													        'image': image,
													        'question': question,
													    },
													    {
													        'image': image,
													        'question': question,
													        'word_boxes': word_boxes,
													    },
													]
													return dqa_pipeline, examples






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(_A				,							top_k=2		)
													self.assertEqual(
													    _A				,							[
													        [
													            {'score': ANY(_A		), 'answer': ANY(_A		), 'start': ANY(_A		), 'end': ANY(_A		)},
													            {'score': ANY(_A		), 'answer': ANY(_A		), 'start': ANY(_A		), 'end': ANY(_A		)},
													        ]
													    ]
													    * 3				,							)






						@require_torch
						@require_detectrona
						@require_pytesseract
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline('document-question-answering'				,							model='hf-internal-testing/tiny-random-layoutlmv2'		)
													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'How many cats are there?'

													__SCREAMING_SNAKE_CASE									= [
													    {'score': 0.0_0_0_1, 'answer': 'oy 2312/2019', 'start': 38, 'end': 39},
													    {'score': 0.0_0_0_1, 'answer': 'oy 2312/2019 DUE', 'start': 38, 'end': 40},
													]
													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(nested_simplify(_A				,							decimals=4		)				,							_A		)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': image, 'question': question}				,							top_k=2		)
													self.assertEqual(nested_simplify(_A				,							decimals=4		)				,							_A		)

													# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
													# Empty answer probably
													__SCREAMING_SNAKE_CASE									= './tests/fixtures/tests_samples/COCO/000000039769.png'
													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(_A				,							[]		)

													# We can optionnally pass directly the words and bounding boxes
													__SCREAMING_SNAKE_CASE									= './tests/fixtures/tests_samples/COCO/000000039769.png'
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= []
													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							words=_A				,							boxes=_A				,							top_k=2		)
													self.assertEqual(_A				,							[]		)






						@slow
						@require_torch
						@require_detectrona
						@require_pytesseract
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'				,							revision='9977165'				,							)
													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'What is the invoice number?'

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': image, 'question': question}				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(
													    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        [
													            {'score': 0.9_9_4_4, 'answer': 'us-001', 'start': 16, 'end': 16},
													            {'score': 0.0_0_0_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													        ],
													    ]
													    * 2				,							)






						@slow
						@require_torch
						@require_detectrona
						@require_pytesseract
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model='tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa'				,							revision='9977165'				,							max_seq_len=50				,							)
													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'What is the invoice number?'

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
													        {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': image, 'question': question}				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
													        {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(
													    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        [
													            {'score': 0.9_9_7_4, 'answer': '1110212019', 'start': 23, 'end': 23},
													            {'score': 0.9_9_4_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													        ]
													    ]
													    * 2				,							)






						@slow
						@require_torch
						@require_pytesseract
						@require_vision
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(
													    'impira/layoutlm-document-qa'				,							revision='3dc6de3'				,							add_prefix_space=_A		)
													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model='impira/layoutlm-document-qa'				,							tokenizer=_A				,							revision='3dc6de3'				,							)
													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'What is the invoice number?'

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': image, 'question': question}				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(
													    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        [
													            {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
													            {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
													        ]
													    ]
													    * 2				,							)

													__SCREAMING_SNAKE_CASE									= list(zip(*apply_tesseract(load_image(_A		)				,							_A				,							''		)		)		)

													# This model should also work if `image` is set to None
													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.4_2_5_1, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.0_8_1_9, 'answer': '1110212019', 'start': 23, 'end': 23},
													    ]				,							)






						@slow
						@require_torch
						@require_pytesseract
						@require_vision
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(
													    'impira/layoutlm-document-qa'				,							revision='3dc6de3'				,							add_prefix_space=_A		)
													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model='impira/layoutlm-document-qa'				,							tokenizer=_A				,							revision='3dc6de3'				,							max_seq_len=50				,							)
													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'What is the invoice number?'

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)

													__SCREAMING_SNAKE_CASE									= dqa_pipeline(
													    [{'image': image, 'question': question}, {'image': image, 'question': question}]				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        [
													            {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													            {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													        ]
													    ]
													    * 2				,							)

													__SCREAMING_SNAKE_CASE									= list(zip(*apply_tesseract(load_image(_A		)				,							_A				,							''		)		)		)

													# This model should also work if `image` is set to None
													__SCREAMING_SNAKE_CASE									= dqa_pipeline({'image': None, 'word_boxes': word_boxes, 'question': question}				,							top_k=2		)
													self.assertEqual(
													    nested_simplify(_A				,							decimals=4		)				,							[
													        {'score': 0.9_9_9_9, 'answer': 'us-001', 'start': 16, 'end': 16},
													        {'score': 0.9_9_9_8, 'answer': 'us-001', 'start': 16, 'end': 16},
													    ]				,							)






						@slow
						@require_torch
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= pipeline(
													    'document-question-answering'				,							model='naver-clova-ix/donut-base-finetuned-docvqa'				,							tokenizer=AutoTokenizer.from_pretrained('naver-clova-ix/donut-base-finetuned-docvqa'		)				,							feature_extractor='naver-clova-ix/donut-base-finetuned-docvqa'				,							)

													__SCREAMING_SNAKE_CASE									= INVOICE_URL
													__SCREAMING_SNAKE_CASE									= 'What is the invoice number?'
													__SCREAMING_SNAKE_CASE									= dqa_pipeline(image=_A				,							question=_A				,							top_k=2		)
													self.assertEqual(nested_simplify(_A				,							decimals=4		)				,							[{'answer': 'us-001'}]		)






						@require_tf
						@unittest.skip('Document question answering not implemented in TF'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass











import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

class        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  CycleDiffusionPipeline
						UpperCamelCase__      :			int      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''negative_prompt''',
						    '''height''',
						    '''width''',
						    '''negative_prompt_embeds''',
						}
						UpperCamelCase__      :			Union[str, Any]      =  PipelineTesterMixin.required_optional_params - {'''latents'''}
						UpperCamelCase__      :			List[str]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}	)
						UpperCamelCase__      :			Optional[int]      =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase__      :			str      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= UNetaDConditionModel(
													    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='scaled_linear'				,							num_train_timesteps=1_000				,							clip_sample=_A				,							set_alpha_to_one=_A				,							)
													torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= 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		)
													__SCREAMING_SNAKE_CASE									= 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=1_000				,							)
													__SCREAMING_SNAKE_CASE									= CLIPTextModel(_A		)
													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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






						def 	_A     (   self				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 32, 32)				,							rng=random.Random(_A		)		).to(_A		)
													__SCREAMING_SNAKE_CASE									= image / 2 + 0.5
													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_SNAKE_CASE									= {
													    'prompt': 'An astronaut riding an elephant',
													    'source_prompt': 'An astronaut riding a horse',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 2,
													    'eta': 0.1,
													    'strength': 0.8,
													    'guidance_scale': 3,
													    'source_guidance_scale': 1,
													    'output_type': 'numpy',
													}
													return inputs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'  # ensure determinism for the device-dependent torch.Generator

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]		)

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






						@unittest.skipIf(torch_device != 'cuda'				,							'This test requires a GPU'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(_A				,							'half'		):
																											__SCREAMING_SNAKE_CASE									= module.half()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

													__SCREAMING_SNAKE_CASE									= images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]		)

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






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_local()






						@unittest.skip('non-deterministic pipeline'		)
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_inference_batch_single_identical()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_optional_components()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_attention_slicing_forward_pass()







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(
													    _A				,							scheduler=_A				,							safety_checker=_A				,							torch_dtype=torch.floataa				,							revision='fp16'		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image		).max() < 5e-1






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'		)
													__SCREAMING_SNAKE_CASE									= load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'		)
													__SCREAMING_SNAKE_CASE									= init_image.resize((512, 512)		)

													__SCREAMING_SNAKE_CASE									= 'CompVis/stable-diffusion-v1-4'
													__SCREAMING_SNAKE_CASE									= DDIMScheduler.from_pretrained(_A				,							subfolder='scheduler'		)
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline.from_pretrained(_A				,							scheduler=_A				,							safety_checker=_A		)

													pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)
													pipe.enable_attention_slicing()

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

													assert np.abs(image - expected_image		).max() < 2e-2






