


'''simple docstring'''

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 						__SCREAMING_SNAKE_CASE					(      SCREAMING_SNAKE_CASE__ ,			unittest.TestCase ):
     pass







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






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






     @property
     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		ort.SessionOptions()
          _a						=		False
          return options






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

          _a						=		"""A red cat sitting on a park bench"""

          _a						=		np.random.RandomState(0		)
          _a						=		pipe(
              prompt=_lowercase     ,							image=_lowercase     ,							mask_image=_lowercase     ,							guidance_scale=7.5     ,							num_inference_steps=10     ,							generator=_lowercase     ,							output_type="np"     ,							)
          _a						=		output.images
          _a						=		images[0, 255:258, 255:258, -1]

          assert images.shape == (1, 512, 512, 3)
          _a						=		np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]		)

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






     def  a_					(			self		)       ->    Any:
          _a						=		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
          _a						=		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
          _a						=		LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting"     ,							subfolder="scheduler"     ,							revision="onnx"		)
          _a						=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting"     ,							revision="onnx"     ,							scheduler=_lowercase     ,							safety_checker=_lowercase     ,							feature_extractor=_lowercase     ,							provider=self.gpu_provider     ,							sess_options=self.gpu_options     ,							)
          pipe.set_progress_bar_config(disable=_lowercase		)

          _a						=		"""A red cat sitting on a park bench"""

          _a						=		np.random.RandomState(0		)
          _a						=		pipe(
              prompt=_lowercase     ,							image=_lowercase     ,							mask_image=_lowercase     ,							guidance_scale=7.5     ,							num_inference_steps=20     ,							generator=_lowercase     ,							output_type="np"     ,							)
          _a						=		output.images
          _a						=		images[0, 255:258, 255:258, -1]

          assert images.shape == (1, 512, 512, 3)
          _a						=		np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]		)

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





'''simple docstring'''

lowercase__				     =		256
# Modulus to hash a string
lowercase__				     =		1_000_003






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     bool:







     '''simple docstring'''






     _a						=		len(__lowerCamelCase				)
     _a						=		len(__lowerCamelCase				)
     if p_len > t_len:
          return False

     _a						=		0
     _a						=		0
     _a						=		1

     # Calculating the hash of pattern and substring of text
     for i in range(__lowerCamelCase				):
          _a						=		(ord(pattern[i]				) + p_hash * alphabet_size) % modulus
          _a						=		(ord(text[i]				) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a						=		(modulus_power * alphabet_size) % modulus

     for i in range(0      ,  t_len - p_len + 1				):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a						=		(
              (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
              + ord(text[i + p_len]				)
          ) % modulus
     return False






def __UpperCamelCase   (			)      ->     None:







     '''simple docstring'''






     _a						=		"abc1abc12"
     _a						=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
     _a						=		"alskfjaldsk23adsfabcabc"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				) and not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 2)
     _a						=		"ABABX"
     _a						=		"ABABZABABYABABX"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 3)
     _a						=		"AAAB"
     _a						=		"ABAAAAAB"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 4)
     _a						=		"abcdabcy"
     _a						=		"abcxabcdabxabcdabcdabcy"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 5)
     _a						=		"Lü"
     _a						=		"Lüsai"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		"Lue"
     assert not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     print("Success."				)


if __name__ == "__main__":
   test_rabin_karp()

