




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_						="""▁"""

UpperCAmelCase_						=get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Union[str, Any]										=BigBirdTokenizer
				__a							: Any										=BigBirdTokenizerFast
				__a							: str										=True
				__a							: Optional[Any]										=True



				def       __snake_case		( self						):
											super().setUp()

											lowerCAmelCase   =		self.tokenizer_class(UpperCAmelCase_       , keep_accents=UpperCAmelCase_						)
											tokenizer.save_pretrained(self.tmpdirname						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		'''<s>'''
											lowerCAmelCase   =		1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_						)       , UpperCAmelCase_						)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_						)       , UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		list(self.get_tokenizer().get_vocab().keys()						)

											self.assertEqual(vocab_keys[0]       , '''<unk>'''						)
											self.assertEqual(vocab_keys[1]       , '''<s>'''						)
											self.assertEqual(vocab_keys[-1]       , '''[MASK]'''						)
											self.assertEqual(len(UpperCAmelCase_						)       , 10_04						)



				def       __snake_case		( self						):
											self.assertEqual(self.get_tokenizer().vocab_size       , 10_00						)



				def       __snake_case		( self						):
											if not self.test_rust_tokenizer:
																		return

											lowerCAmelCase   =		self.get_tokenizer()
											lowerCAmelCase   =		self.get_rust_tokenizer()

											lowerCAmelCase   =		'''I was born in 92000, and this is falsé.'''

											lowerCAmelCase   =		tokenizer.tokenize(UpperCAmelCase_						)
											lowerCAmelCase   =		rust_tokenizer.tokenize(UpperCAmelCase_						)
											self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_						)

											lowerCAmelCase   =		tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_						)
											lowerCAmelCase   =		rust_tokenizer.encode(UpperCAmelCase_       , add_special_tokens=UpperCAmelCase_						)
											self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_rust_tokenizer()
											lowerCAmelCase   =		tokenizer.encode(UpperCAmelCase_						)
											lowerCAmelCase   =		rust_tokenizer.encode(UpperCAmelCase_						)
											self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		BigBirdTokenizer(UpperCAmelCase_       , keep_accents=UpperCAmelCase_						)

											lowerCAmelCase   =		tokenizer.tokenize('''This is a test'''						)
											self.assertListEqual(UpperCAmelCase_       , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(UpperCAmelCase_						)       , [2_85, 46, 10, 1_70, 3_82]       , )

											lowerCAmelCase   =		tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
											self.assertListEqual(
											    UpperCAmelCase_       , [
											        SPIECE_UNDERLINE + '''I''',
											        SPIECE_UNDERLINE + '''was''',
											        SPIECE_UNDERLINE + '''b''',
											        '''or''',
											        '''n''',
											        SPIECE_UNDERLINE + '''in''',
											        SPIECE_UNDERLINE + '''''',
											        '''9''',
											        '''2''',
											        '''0''',
											        '''0''',
											        '''0''',
											        ''',''',
											        SPIECE_UNDERLINE + '''and''',
											        SPIECE_UNDERLINE + '''this''',
											        SPIECE_UNDERLINE + '''is''',
											        SPIECE_UNDERLINE + '''f''',
											        '''al''',
											        '''s''',
											        '''é''',
											        '''.''',
											    ]       , )
											lowerCAmelCase   =		tokenizer.convert_tokens_to_ids(UpperCAmelCase_						)
											self.assertListEqual(
											    UpperCAmelCase_       , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       , )

											lowerCAmelCase   =		tokenizer.convert_ids_to_tokens(UpperCAmelCase_						)
											self.assertListEqual(
											    UpperCAmelCase_       , [
											        SPIECE_UNDERLINE + '''I''',
											        SPIECE_UNDERLINE + '''was''',
											        SPIECE_UNDERLINE + '''b''',
											        '''or''',
											        '''n''',
											        SPIECE_UNDERLINE + '''in''',
											        SPIECE_UNDERLINE + '''''',
											        '''<unk>''',
											        '''2''',
											        '''0''',
											        '''0''',
											        '''0''',
											        ''',''',
											        SPIECE_UNDERLINE + '''and''',
											        SPIECE_UNDERLINE + '''this''',
											        SPIECE_UNDERLINE + '''is''',
											        SPIECE_UNDERLINE + '''f''',
											        '''al''',
											        '''s''',
											        '''<unk>''',
											        '''.''',
											    ]       , )



				@cached_property
				def       __snake_case		( self						):
											return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''						)



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		'''Hello World!'''
											lowerCAmelCase   =		[65, 1_85_36, 22_60, 1_01, 66]

											self.assertListEqual(UpperCAmelCase_       , self.big_tokenizer.encode(UpperCAmelCase_						)						)



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		(
											    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
											    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
											)
											# fmt: off
											lowerCAmelCase   =		[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
											# fmt: on
											self.assertListEqual(UpperCAmelCase_       , self.big_tokenizer.encode(UpperCAmelCase_						)						)



				@require_torch
				@slow
				def       __snake_case		( self						):
											import torch

											from transformers import BigBirdConfig, BigBirdModel

											# Build sequence
											lowerCAmelCase   =		list(self.big_tokenizer.get_vocab().keys()						)[:10]
											lowerCAmelCase   =		''' '''.join(UpperCAmelCase_						)
											lowerCAmelCase   =		self.big_tokenizer.encode_plus(UpperCAmelCase_       , return_tensors='''pt'''       , return_token_type_ids=UpperCAmelCase_						)
											lowerCAmelCase   =		self.big_tokenizer.batch_encode_plus(
											    [sequence + ''' ''' + sequence]       , return_tensors='''pt'''       , return_token_type_ids=UpperCAmelCase_						)

											lowerCAmelCase   =		BigBirdConfig(attention_type='''original_full'''						)
											lowerCAmelCase   =		BigBirdModel(UpperCAmelCase_						)

											assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

											with torch.no_grad():
																		model(**UpperCAmelCase_						)
																		model(**UpperCAmelCase_						)



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''						)
											lowerCAmelCase   =		tokenizer.decode(tokenizer('''Paris is the [MASK].'''						).input_ids						)

											self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''						)






				@slow
				def       __snake_case		( self						):
											# fmt: off
											lowerCAmelCase   =		{'''input_ids''': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCAmelCase_       , model_name='''google/bigbird-roberta-base'''       , revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''       , )









import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort


class 							__UpperCamelCase			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Any										="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



				def       __snake_case		( self       , UpperCAmelCase_=0						):
											lowerCAmelCase   =		floats_tensor((1, 3, 1_28, 1_28)       , rng=random.Random(UpperCAmelCase_						)						)
											lowerCAmelCase   =		np.random.RandomState(UpperCAmelCase_						)
											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''strength''': 0.75,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]						)
											assert np.abs(image_slice - expected_slice						).max() < 1E-1



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		PNDMScheduler.from_config(pipe.scheduler.config       , skip_prk_steps=UpperCAmelCase_						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											# warmup pass to apply optimizations
											lowerCAmelCase   =		pipe(**self.get_dummy_inputs()						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]						)

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






				def       __snake_case		( self						):
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       , provider='''CPUExecutionProvider'''						)
											lowerCAmelCase   =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		self.get_dummy_inputs()
											lowerCAmelCase   =		pipe(**UpperCAmelCase_						).images
											lowerCAmelCase   =		image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_28, 1_28, 3)
											lowerCAmelCase   =		np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]						)

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




@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@property
				def       __snake_case		( self						):
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		ort.SessionOptions()
											lowerCAmelCase   =		False
											return options



				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											# using the PNDM scheduler by default
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''CompVis/stable-diffusion-v1-4'''       , revision='''onnx'''       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=10       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






				def       __snake_case		( self						):
											lowerCAmelCase   =		load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/img2img/sketch-mountains-input.jpg'''						)
											lowerCAmelCase   =		init_image.resize((7_68, 5_12)						)
											lowerCAmelCase   =		LMSDiscreteScheduler.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , subfolder='''scheduler'''       , revision='''onnx'''						)
											lowerCAmelCase   =		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''       , revision='''onnx'''       , scheduler=UpperCAmelCase_       , safety_checker=UpperCAmelCase_       , feature_extractor=UpperCAmelCase_       , provider=self.gpu_provider       , sess_options=self.gpu_options       , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

											lowerCAmelCase   =		'''A fantasy landscape, trending on artstation'''

											lowerCAmelCase   =		np.random.RandomState(0						)
											lowerCAmelCase   =		pipe(
											    prompt=UpperCAmelCase_       , image=UpperCAmelCase_       , strength=0.75       , guidance_scale=7.5       , num_inference_steps=20       , generator=UpperCAmelCase_       , output_type='''np'''       , )
											lowerCAmelCase   =		output.images
											lowerCAmelCase   =		images[0, 2_55:2_58, 3_83:3_86, -1]

											assert images.shape == (1, 5_12, 7_68, 3)
											lowerCAmelCase   =		np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]						)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



