

"""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      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     # FIXME: add fast tests
     pass





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






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )

     @property
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	ort.SessionOptions()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	False
           return options

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


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""A red cat sitting on a park bench"""

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

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE__   :      int      		=	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 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo.png"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	LMSDiscreteScheduler.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""	,    subfolder="""scheduler"""	,    revision="""onnx"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	OnnxStableDiffusionInpaintPipeline.from_pretrained(
               """runwayml/stable-diffusion-inpainting"""	,    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  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	"""A red cat sitting on a park bench"""

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	np.random.RandomState(0  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	pipe(
               prompt=_a	,    image=_a	,    mask_image=_a	,    guidance_scale=7.5	,    num_inference_steps=20	,    generator=_a	,    output_type="""np"""	,    )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.images
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	images[0, 255:258, 255:258, -1]

           assert images.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE__   :      int      		=	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"""


def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      int      		=	len(__lowerCAmelCase							) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      int      		=	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1  ,					__lowerCAmelCase							):
            for j in range(1  ,					__lowerCAmelCase							):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        SCREAMING_SNAKE_CASE__   :      Any      		=	dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	0

      return bool(dp[-1][-1]							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # inputing the strings
  # input_string = input("input a string :")
  # pattern = input("input a pattern :")

  a					:Any    = "aab"
  a					:Optional[Any]    = "c*a*b"

  # using function to check whether given string matches the given pattern
  if match_pattern(input_string, pattern):
    print(f'{input_string} matches the given pattern {pattern}')
  else:
    print(f'{input_string} does not match with the given pattern {pattern}')




