
"""simple docstring"""


import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class 				lowerCAmelCase_				(      __A		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    BertTokenizer
						_lowerCamelCase:    Dict      =    BertTokenizerFast
						_lowerCamelCase:    Optional[int]      =    True
						_lowerCamelCase:    Optional[Any]      =    True
						_lowerCamelCase:    Any      =    filter_non_english


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   int:
							super().setUp()

							A						   =   [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							A						   =   os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES['vocab_file']      )
							with open(self.vocab_file  ,'w'  ,encoding='utf-8'      ) as vocab_writer:
								vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[Any]      )     ->   List[Any]:
							A						   =   'UNwant\u00E9d,running'
							A						   =   'unwanted, running'
							return input_text, output_text


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   self.tokenizer_class(self.vocab_file      )

							A						   =   tokenizer.tokenize('UNwant\u00E9d,running'      )
							self.assertListEqual(A_  ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_      )  ,[9, 6, 7, 12, 10, 11]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[str]:





							if not self.test_rust_tokenizer:
								return

							A						   =   self.get_tokenizer()
							A						   =   self.get_rust_tokenizer()

							A						   =   'UNwant\u00E9d,running'

							A						   =   tokenizer.tokenize(A_      )
							A						   =   rust_tokenizer.tokenize(A_      )
							self.assertListEqual(A_  ,A_      )

							A						   =   tokenizer.encode(A_  ,add_special_tokens=A_      )
							A						   =   rust_tokenizer.encode(A_  ,add_special_tokens=A_      )
							self.assertListEqual(A_  ,A_      )

							A						   =   self.get_rust_tokenizer()
							A						   =   tokenizer.encode(A_      )
							A						   =   rust_tokenizer.encode(A_      )
							self.assertListEqual(A_  ,A_      )

							# With lower casing
							A						   =   self.get_tokenizer(do_lower_case=A_      )
							A						   =   self.get_rust_tokenizer(do_lower_case=A_      )

							A						   =   'UNwant\u00E9d,running'

							A						   =   tokenizer.tokenize(A_      )
							A						   =   rust_tokenizer.tokenize(A_      )
							self.assertListEqual(A_  ,A_      )

							A						   =   tokenizer.encode(A_  ,add_special_tokens=A_      )
							A						   =   rust_tokenizer.encode(A_  ,add_special_tokens=A_      )
							self.assertListEqual(A_  ,A_      )

							A						   =   self.get_rust_tokenizer()
							A						   =   tokenizer.encode(A_      )
							A						   =   rust_tokenizer.encode(A_      )
							self.assertListEqual(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[str]:
							A						   =   BasicTokenizer()

							self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )  ,['ah', '\u535A', '\u63A8', 'zz']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   List[str]:
							A						   =   BasicTokenizer(do_lower_case=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,['hello', '!', 'how', 'are', 'you', '?']      )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Dict:
							A						   =   BasicTokenizer(do_lower_case=A_  ,strip_accents=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hällo', '!', 'how', 'are', 'you', '?']      )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['h\u00E9llo']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							A						   =   BasicTokenizer(do_lower_case=A_  ,strip_accents=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hallo', '!', 'how', 'are', 'you', '?']      )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   BasicTokenizer(do_lower_case=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['hallo', '!', 'how', 'are', 'you', '?']      )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )  ,['hello']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							A						   =   BasicTokenizer(do_lower_case=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )  ,['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							A						   =   BasicTokenizer(do_lower_case=A_  ,strip_accents=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   BasicTokenizer(do_lower_case=A_  ,strip_accents=A_      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )  ,['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[Any]:
							A						   =   BasicTokenizer(do_lower_case=A_  ,never_split=['[UNK]']      )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )  ,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Any:
							A						   =   BasicTokenizer()
							A						   =   'a\n\'ll !!to?\'d of, can\'t.'
							A						   =   ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
							self.assertListEqual(tokenizer.tokenize(A_      )  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							A						   =   ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

							A						   =   {}
							for i, token in enumerate(A_      ):
								A						   =   i
							A						   =   WordpieceTokenizer(vocab=A_  ,unk_token='[UNK]'      )

							self.assertListEqual(tokenizer.tokenize(''      )  ,[]      )

							self.assertListEqual(tokenizer.tokenize('unwanted running'      )  ,['un', '##want', '##ed', 'runn', '##ing']      )

							self.assertListEqual(tokenizer.tokenize('unwantedX running'      )  ,['[UNK]', 'runn', '##ing']      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							self.assertTrue(_is_whitespace(' '      )      )
							self.assertTrue(_is_whitespace('\t'      )      )
							self.assertTrue(_is_whitespace('\r'      )      )
							self.assertTrue(_is_whitespace('\n'      )      )
							self.assertTrue(_is_whitespace('\u00A0'      )      )

							self.assertFalse(_is_whitespace('A'      )      )
							self.assertFalse(_is_whitespace('-'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Tuple:
							self.assertTrue(_is_control('\u0005'      )      )

							self.assertFalse(_is_control('A'      )      )
							self.assertFalse(_is_control(' '      )      )
							self.assertFalse(_is_control('\t'      )      )
							self.assertFalse(_is_control('\r'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   str:
							self.assertTrue(_is_punctuation('-'      )      )
							self.assertTrue(_is_punctuation('$'      )      )
							self.assertTrue(_is_punctuation('`'      )      )
							self.assertTrue(_is_punctuation('.'      )      )

							self.assertFalse(_is_punctuation('A'      )      )
							self.assertFalse(_is_punctuation(' '      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[Any]:
							A						   =   self.get_tokenizer()
							A						   =   self.get_rust_tokenizer()

							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
							self.assertListEqual([tokenizer.tokenize(A_      ) for t in ['Test', '\xad', 'test']]  ,[['[UNK]'], [], ['[UNK]']]      )

							self.assertListEqual(
							    [rust_tokenizer.tokenize(A_      ) for t in ['Test', '\xad', 'test']]  ,[['[UNK]'], [], ['[UNK]']]      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Tuple:
							A						   =   self.tokenizer_class.from_pretrained('bert-base-uncased'      )

							A						   =   tokenizer.encode('sequence builders'  ,add_special_tokens=A_      )
							A						   =   tokenizer.encode('multi-sequence build'  ,add_special_tokens=A_      )

							A						   =   tokenizer.build_inputs_with_special_tokens(A_      )
							A						   =   tokenizer.build_inputs_with_special_tokens(A_  ,A_      )

							assert encoded_sentence == [101] + text + [102]
							assert encoded_pair == [101] + text + [102] + text_a + [102]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[int]:





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
									A						   =   self.rust_tokenizer_class.from_pretrained(A_  ,**A_      )

									A						   =   F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
									A						   =   tokenizer_r.encode_plus(
									    A_  ,return_attention_mask=A_  ,return_token_type_ids=A_  ,return_offsets_mapping=A_  ,add_special_tokens=A_  ,)

									A						   =   tokenizer_r.do_lower_case if hasattr(A_  ,'do_lower_case'      ) else False
									A						   =   (
									    [
									        ((0, 0), tokenizer_r.cls_token),
									        ((0, 1), 'A'),
									        ((1, 2), ','),
									        ((3, 5), 'na'),
									        ((5, 6), '##ï'),
									        ((6, 8), '##ve'),
									        ((9, 15), tokenizer_r.mask_token),
									        ((16, 21), 'Allen'),
									        ((21, 23), '##NL'),
									        ((23, 24), '##P'),
									        ((25, 33), 'sentence'),
									        ((33, 34), '.'),
									        ((0, 0), tokenizer_r.sep_token),
									    ]
									    if not do_lower_case
									    else [
									        ((0, 0), tokenizer_r.cls_token),
									        ((0, 1), 'a'),
									        ((1, 2), ','),
									        ((3, 8), 'naive'),
									        ((9, 15), tokenizer_r.mask_token),
									        ((16, 21), 'allen'),
									        ((21, 23), '##nl'),
									        ((23, 24), '##p'),
									        ((25, 33), 'sentence'),
									        ((33, 34), '.'),
									        ((0, 0), tokenizer_r.sep_token),
									    ]
									)

									self.assertEqual(
									    [e[1] for e in expected_results]  ,tokenizer_r.convert_ids_to_tokens(tokens['input_ids']      )      )
									self.assertEqual([e[0] for e in expected_results]  ,tokens['offset_mapping']      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:
							A						   =   ['的', '人', '有']
							A						   =   ''.join(A_      )
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
									A						   =   True
									A						   =   self.tokenizer_class.from_pretrained(A_  ,**A_      )
									A						   =   self.rust_tokenizer_class.from_pretrained(A_  ,**A_      )

									A						   =   tokenizer_p.encode(A_  ,add_special_tokens=A_      )
									A						   =   tokenizer_r.encode(A_  ,add_special_tokens=A_      )

									A						   =   tokenizer_r.convert_ids_to_tokens(A_      )
									A						   =   tokenizer_p.convert_ids_to_tokens(A_      )

									# it is expected that each Chinese character is not preceded by "##"
									self.assertListEqual(A_  ,A_      )
									self.assertListEqual(A_  ,A_      )

									A						   =   False
									A						   =   self.rust_tokenizer_class.from_pretrained(A_  ,**A_      )
									A						   =   self.tokenizer_class.from_pretrained(A_  ,**A_      )

									A						   =   tokenizer_r.encode(A_  ,add_special_tokens=A_      )
									A						   =   tokenizer_p.encode(A_  ,add_special_tokens=A_      )

									A						   =   tokenizer_r.convert_ids_to_tokens(A_      )
									A						   =   tokenizer_p.convert_ids_to_tokens(A_      )

									# it is expected that only the first Chinese character is not preceded by "##".
									A						   =   [
									    F'##{token}' if idx != 0 else token for idx, token in enumerate(A_      )
									]
									self.assertListEqual(A_  ,A_      )
									self.assertListEqual(A_  ,A_      )






"""simple docstring"""


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 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=0      )     ->   Dict:
							A						   =   floats_tensor((1, 3, 128, 128)  ,rng=random.Random(A_      )      )
							A						   =   np.random.RandomState(A_      )
							A						   =   {
							    '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 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							pipe.set_progress_bar_config(disable=A_      )

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]      )
							assert np.abs(image_slice - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   PNDMScheduler.from_config(pipe.scheduler.config  ,skip_prk_steps=A_      )
							pipe.set_progress_bar_config(disable=A_      )

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

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

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

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

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]      )

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







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



						'''simple docstring'''


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							A						   =   ort.SessionOptions()
							A						   =   False
							return options


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							# using the PNDM scheduler by default
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'  ,revision='onnx'  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Any:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							A						   =   LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,subfolder='scheduler'  ,revision='onnx'      )
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,revision='onnx'  ,scheduler=A_  ,safety_checker=A_  ,feature_extractor=A_  ,provider=self.gpu_provider  ,sess_options=self.gpu_options  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=20  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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