





'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
	__lowercase       		=    IFInpaintingSuperResolutionPipeline
	__lowercase       		=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
	__lowercase       		=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}    )
	__lowercase       		=    PipelineTesterMixin.required_optional_params - {'''latents'''}
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Union[str, Any]:
					return self._get_superresolution_dummy_components()
	def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Dict						,	lowercase_				:Any=0							)->							str:
					if str(_a							).startswith("mps"							):
									A__    =      torch.manual_seed(_a							)
					else:
									A__    =      torch.Generator(device=_a							).manual_seed(_a							)

					A__    =      floats_tensor((1, 3, 16, 16)						,	rng=random.Random(_a							)							).to(_a							)
					A__    =      floats_tensor((1, 3, 32, 32)						,	rng=random.Random(_a							)							).to(_a							)
					A__    =      floats_tensor((1, 3, 32, 32)						,	rng=random.Random(_a							)							).to(_a							)

					A__    =      {
					    "prompt": "A painting of a squirrel eating a burger",
					    "image": image,
					    "original_image": original_image,
					    "mask_image": mask_image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "numpy",
					}

					return inputs
	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()						,	reason="XFormers attention is only available with CUDA and `xformers` installed"						,	)
	def 					UpperCAmelCase_							(    self				:int							)->							int:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)
	def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
					self._test_save_load_optional_components()
	@unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"							)
	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
					# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
					super().test_save_load_floataa(expected_max_diff=1E-1							)
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
					self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
	def 					UpperCAmelCase_							(    self				:Tuple							)->							List[Any]:
					self._test_save_load_local()



	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
					self._test_inference_batch_single_identical(
					    expected_max_diff=1E-2						,	)










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


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



