


'''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 	a__       (							a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Dict					=   "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0     ) ->							Dict:
														lowerCAmelCase__      =			floats_tensor((1, 3, 1_28, 1_28)      ,					rng=random.Random(lowerCamelCase_     )     )
														lowerCAmelCase__      =			np.random.RandomState(lowerCamelCase_     )
														lowerCAmelCase__      =			{
														    '''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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087]     )
														assert np.abs(image_slice - expected_slice     ).max() < 1e-1







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														lowerCAmelCase__      =			PNDMScheduler.from_config(pipe.scheduler.config      ,					skip_prk_steps=lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154]     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														lowerCAmelCase__      =			LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203]     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														lowerCAmelCase__      =			EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														lowerCAmelCase__      =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304]     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='''CPUExecutionProvider'''     )
														lowerCAmelCase__      =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

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

														assert image.shape == (1, 1_28, 1_28, 3)
														lowerCAmelCase__      =			np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552]     )

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




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





							'''simple docstring'''






							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														return (
														    "CUDAExecutionProvider",
														    {
														        "gpu_mem_limit": "15000000000",  # 15GB
														        "arena_extend_strategy": "kSameAsRequested",
														    },
														)







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			ort.SessionOptions()
														lowerCAmelCase__      =			False
														return options







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''     )
														lowerCAmelCase__      =			init_image.resize((7_68, 5_12)     )
														# using the PNDM scheduler by default
														lowerCAmelCase__      =			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_     )

														lowerCAmelCase__      =			'''A fantasy landscape, trending on artstation'''

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

														assert images.shape == (1, 5_12, 7_68, 3)
														lowerCAmelCase__      =			np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019]     )
														# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/img2img/sketch-mountains-input.jpg'''     )
														lowerCAmelCase__      =			init_image.resize((7_68, 5_12)     )
														lowerCAmelCase__      =			LMSDiscreteScheduler.from_pretrained(
														    '''runwayml/stable-diffusion-v1-5'''      ,					subfolder='''scheduler'''      ,					revision='''onnx'''     )
														lowerCAmelCase__      =			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_     )

														lowerCAmelCase__      =			'''A fantasy landscape, trending on artstation'''

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

														assert images.shape == (1, 5_12, 7_68, 3)
														lowerCAmelCase__      =			np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431]     )
														# 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 collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			0

							for character_count in character_freq_dict.values():
														if character_count % 2:
																					odd_char += 1
							if odd_char > 1:
														return False
							return True


def       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)
							print(
							    '''> can_string_be_rearranged_as_palindrome()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


if __name__ == "__main__":
		__UpperCAmelCase					       =							input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__UpperCAmelCase					       =							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")