
'''simple docstring'''







import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_=0  ):





								"""simple docstring"""






								_snake_case   						=		floats_tensor((1, 3, 1_28, 1_28)    ,   rng=random.Random(lowerCAmelCase_  )  )
								_snake_case   						=		np.random.RandomState(lowerCAmelCase_  )
								_snake_case   						=		{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 3,
								    'strength': 0.75,
								    'guidance_scale': 7.5,
								    'output_type': 'numpy',
								}
								return inputs


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]  )
								assert np.abs(image_slice - expected_slice  ).max() < 1E-1


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								_snake_case   						=		PNDMScheduler.from_config(pipe.scheduler.config    ,   skip_prk_steps=lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]  )

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								_snake_case   						=		LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								# warmup pass to apply optimizations
								_snake_case   						=		pipe(**self.get_dummy_inputs()  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]  )

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								_snake_case   						=		EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]  )

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								_snake_case   						=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]  )

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




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider='CPUExecutionProvider'  )
								_snake_case   						=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		self.get_dummy_inputs()
								_snake_case   						=		pipe(**lowerCAmelCase_  ).images
								_snake_case   						=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 1_28, 1_28, 3)
								_snake_case   						=		np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]  )

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


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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		ort.SessionOptions()
								_snake_case   						=		False
								return options


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/img2img/sketch-mountains-input.jpg'  )
								_snake_case   						=		init_image.resize((7_68, 5_12)  )
								# using the PNDM scheduler by default
								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'    ,   revision='onnx'    ,   safety_checker=lowerCAmelCase_    ,   feature_extractor=lowerCAmelCase_    ,   provider=self.gpu_provider    ,   sess_options=self.gpu_options    ,   )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A fantasy landscape, trending on artstation'

								_snake_case   						=		np.random.RandomState(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   image=lowerCAmelCase_    ,   strength=0.75    ,   guidance_scale=7.5    ,   num_inference_steps=10    ,   generator=lowerCAmelCase_    ,   output_type='np'    ,   )
								_snake_case   						=		output.images
								_snake_case   						=		images[0, 2_55:2_58, 3_83:3_86, -1]

								assert images.shape == (1, 5_12, 7_68, 3)
								_snake_case   						=		np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]  )
								# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2E-2




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/img2img/sketch-mountains-input.jpg'  )
								_snake_case   						=		init_image.resize((7_68, 5_12)  )
								_snake_case   						=		LMSDiscreteScheduler.from_pretrained(
								    'runwayml/stable-diffusion-v1-5'    ,   subfolder='scheduler'    ,   revision='onnx'  )
								_snake_case   						=		OnnxStableDiffusionImgaImgPipeline.from_pretrained(
								    'runwayml/stable-diffusion-v1-5'    ,   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_  )

								_snake_case   						=		'A fantasy landscape, trending on artstation'

								_snake_case   						=		np.random.RandomState(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   image=lowerCAmelCase_    ,   strength=0.75    ,   guidance_scale=7.5    ,   num_inference_steps=20    ,   generator=lowerCAmelCase_    ,   output_type='np'    ,   )
								_snake_case   						=		output.images
								_snake_case   						=		images[0, 2_55:2_58, 3_83:3_86, -1]

								assert images.shape == (1, 5_12, 7_68, 3)
								_snake_case   						=		np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]  )
								# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 2E-2



'''simple docstring'''







from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase    :							str      =					(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase    :							list[int]      =					[ord(letter) for letter in string.ascii_lowercase]
lowercase    :							set[int]      =					{ord(char) for char in VALID_CHARS}

lowercase    :							list[str]      =					["the", "be", "to", "of", "and", "in", "that", "have"]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str | None:
	_snake_case   						=		""
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42

	for keychar, cipherchar in zip(cycle(__A	)			,				__A	):
		_snake_case   						=		cipherchar ^ keychar
		if decodedchar not in VALID_INTS:
			return None
		decoded += chr(__A	)

	return decoded







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	list[str]:
	_snake_case   						=		[]
	for key in product(__A			,				repeat=3	):
		_snake_case   						=		try_key(__A			,				__A	)
		if encoded is not None:
			possibles.append(__A	)
	return possibles







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	list[str]:
	return [possible for possible in possibles if common_word in possible.lower()]







def 				SCREAMING_SNAKE_CASE__	(   __A = "p059_cipher.txt"	)       ->	int:
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		42
	_snake_case   						=		Path(__A	).parent.joinpath(__A	).read_text(encoding='utf-8'	)

	_snake_case   						=		[int(__A	) for number in data.strip().split(','	)]

	_snake_case   						=		filter_valid_chars(__A	)
	for common_word in COMMON_WORDS:
		_snake_case   						=		filter_common_word(__A			,				__A	)
		if len(__A	) == 1:
			break

	_snake_case   						=		possibles[0]
	return sum(ord(__A	) for char in decoded_text	)


if __name__ == "__main__":
					print(F'''{solution() = }''')

