


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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 		snake_case_(       __UpperCAmelCase							,					unittest.TestCase			):
     pass



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






     @property
     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )






     @property
     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Optional[int]       =   ort.SessionOptions()
           lowerCAmelCase					:	List[Any]       =   False
           return options






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Tuple       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
           lowerCAmelCase					:	Optional[int]       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
           lowerCAmelCase					:	List[Any]       =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
               '''runwayml/stable-diffusion-inpainting'''					,				revision='''onnx'''					,				safety_checker=_lowerCamelCase					,				feature_extractor=_lowerCamelCase					,				provider=self.gpu_provider					,				sess_options=self.gpu_options					,				)
           pipe.set_progress_bar_config(disable=_lowerCamelCase				)

           lowerCAmelCase					:	List[Any]       =   '''A red cat sitting on a park bench'''

           lowerCAmelCase					:	List[Any]       =   np.random.RandomState(0				)
           lowerCAmelCase					:	List[Any]       =   pipe(
               prompt=_lowerCamelCase					,				image=_lowerCamelCase					,				mask_image=_lowerCamelCase					,				guidance_scale=7.5					,				num_inference_steps=1_0					,				generator=_lowerCamelCase					,				output_type='''np'''					,				)
           lowerCAmelCase					:	int       =   output.images
           lowerCAmelCase					:	Optional[int]       =   images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

           assert images.shape == (1, 5_1_2, 5_1_2, 3)
           lowerCAmelCase					:	List[str]       =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]				)

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






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Dict       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
           lowerCAmelCase					:	List[str]       =   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
           lowerCAmelCase					:	Any       =   LMSDiscreteScheduler.from_pretrained(
               '''runwayml/stable-diffusion-inpainting'''					,				subfolder='''scheduler'''					,				revision='''onnx'''				)
           lowerCAmelCase					:	Dict       =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
               '''runwayml/stable-diffusion-inpainting'''					,				revision='''onnx'''					,				scheduler=_lowerCamelCase					,				safety_checker=_lowerCamelCase					,				feature_extractor=_lowerCamelCase					,				provider=self.gpu_provider					,				sess_options=self.gpu_options					,				)
           pipe.set_progress_bar_config(disable=_lowerCamelCase				)

           lowerCAmelCase					:	Dict       =   '''A red cat sitting on a park bench'''

           lowerCAmelCase					:	int       =   np.random.RandomState(0				)
           lowerCAmelCase					:	str       =   pipe(
               prompt=_lowerCamelCase					,				image=_lowerCamelCase					,				mask_image=_lowerCamelCase					,				guidance_scale=7.5					,				num_inference_steps=2_0					,				generator=_lowerCamelCase					,				output_type='''np'''					,				)
           lowerCAmelCase					:	List[str]       =   output.images
           lowerCAmelCase					:	Any       =   images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

           assert images.shape == (1, 5_1_2, 5_1_2, 3)
           lowerCAmelCase					:	Dict       =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]				)

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






"""simple docstring"""





from __future__ import annotations



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   text, pattern
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   len(UpperCamelCase_				), len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					str				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if char == self.pattern[i]:
                       return i
           return -1






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if self.pattern[i] != self.text[current_pos + i]:
                       return current_pos + i
           return -1






     def      lowerCamelCase__     (	self  :					Dict				):
           # searches pattern in text and returns index positions
           lowerCAmelCase					:	Union[str, Any]       =   []
           for i in range(self.textLen - self.patLen + 1				):
                 lowerCAmelCase					:	str       =   self.mismatch_in_text(UpperCamelCase_				)
                 if mismatch_index == -1:
                       positions.append(UpperCamelCase_				)
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.match_in_pattern(self.text[mismatch_index]				)
                       lowerCAmelCase					:	int       =   (
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
           return positions



snake_case__		:     str    				=				'''ABAABA'''
snake_case__		:     List[str]    				=				'''AB'''
snake_case__		:     Union[str, Any]    				=				BoyerMooreSearch(text, pattern)
snake_case__		:     Optional[Any]    				=				bms.bad_character_heuristic()

if len(positions) == 0:
 print('''No match found''')
else:
 print('''Pattern found in following positions: ''')
 print(positions)


