





'''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 (					__lowercase						,					unittest.TestCase		):
		# FIXME: add fast tests
		pass





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

		@property
		def 	lowercase__     (   self					:   str			)					->    Optional[Any]:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   int			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Any			)					->    Tuple:


					"""simple docstring"""

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

					__snake_case					:      str							      =     """A red cat sitting on a park bench"""

					__snake_case					:      List[str]							      =     np.random.RandomState(0			)
					__snake_case					:      Optional[int]							      =     pipe(
					    prompt=__magic_name__  ,      image=__magic_name__  ,      mask_image=__magic_name__  ,      guidance_scale=7.5  ,      num_inference_steps=10  ,      generator=__magic_name__  ,      output_type="""np"""  ,      )
					__snake_case					:      Tuple							      =     output.images
					__snake_case					:      Dict							      =     images[0, 2_55:2_58, 2_55:2_58, -1]

					assert images.shape == (1, 5_12, 5_12, 3)
					__snake_case					:      str							      =     np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]			)

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

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

					__snake_case					:      Union[str, Any]							      =     """A red cat sitting on a park bench"""

					__snake_case					:      Union[str, Any]							      =     np.random.RandomState(0			)
					__snake_case					:      Optional[Any]							      =     pipe(
					    prompt=__magic_name__  ,      image=__magic_name__  ,      mask_image=__magic_name__  ,      guidance_scale=7.5  ,      num_inference_steps=20  ,      generator=__magic_name__  ,      output_type="""np"""  ,      )
					__snake_case					:      Optional[int]							      =     output.images
					__snake_case					:      Dict							      =     images[0, 2_55:2_58, 2_55:2_58, -1]

					assert images.shape == (1, 5_12, 5_12, 3)
					__snake_case					:      Dict							      =     np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]			)

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








'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 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.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

			# since string of zero length match pattern of zero length
			A_									= 1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 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       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															A_									= 1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															A_									= dp[i - 1][j]
												else:
															A_									= 0
									else:
												A_									= 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 :")

							_SCREAMING_SNAKE_CASE			=						"aab"
							_SCREAMING_SNAKE_CASE			=						"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}""")




