


"""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      lowerCamelCase__   (     A     ,    unittest.TestCase				):







					"""simple docstring"""


					pass






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







					"""simple docstring"""


					@property
					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






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






					@property
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					ort.SessionOptions()
												__UpperCAmelCase   :						Tuple      							=					False
												return options






					def lowerCamelCase__							(       self    : List[str]  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Optional[Any]      							=					"""A red cat sitting on a park bench"""

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

												assert images.shape == (1, 512, 512, 3)
												__UpperCAmelCase   :						List[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 lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Dict      							=					"""A red cat sitting on a park bench"""

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

												assert images.shape == (1, 512, 512, 3)
												__UpperCAmelCase   :						Optional[Any]      							=					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"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
