


"""simple docstring"""


def   snake_case		(     UpperCamelCase__       :						Any = 1000000    )   ->      int:
      lowerCamelCase					:					int        =	[i - 1 for i in range(limit + 1    )]

      for i in range(2			,				limit + 1    ):
            if phi[i] == i - 1:
                  for j in range(2 * i			,				limit + 1			,				UpperCamelCase__    ):
                        phi[j] -= phi[j] // i

      return sum(phi[2 : limit + 1]    )


if __name__ == "__main__":
    print(solution())









"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=StableDiffusionXLImgaImgPipeline
     snake_case__	:					Any													=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     snake_case__	:					Optional[int]													=PipelineTesterMixin.required_optional_params - {'''latents'''}
     snake_case__	:					Dict													=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     snake_case__	:					Tuple													=IMAGE_TO_IMAGE_IMAGE_PARAMS
     snake_case__	:					List[str]													=IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 		a__						(	self:						List[str]	)->    int:
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	UNetaDConditionModel(
               block_out_channels=(32, 64)     ,		layers_per_block=2     ,		sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		attention_head_dim=(2, 4)     ,		use_linear_projection=__a     ,		addition_embed_type="""text_time"""     ,		addition_time_embed_dim=8     ,		transformer_layers_per_block=(1, 2)     ,		projection_class_embeddings_input_dim=80     ,		cross_attention_dim=64     ,		)
           lowerCamelCase					:					Any        =	EulerDiscreteScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		steps_offset=1     ,		beta_schedule="""scaled_linear"""     ,		timestep_spacing="""leading"""     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	AutoencoderKL(
               block_out_channels=[32, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,		latent_channels=4     ,		sample_size=128     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Optional[Any]        =	CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-0_5     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_000     ,		hidden_act="""gelu"""     ,		projection_dim=32     ,		)
           lowerCamelCase					:					Dict        =	CLIPTextModel(__a	)
           lowerCamelCase					:					Union[str, Any]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					Dict        =	CLIPTextModelWithProjection(__a	)
           lowerCamelCase					:					Optional[int]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					str        =	{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """text_encoder_2""": text_encoder_a,
               """tokenizer_2""": tokenizer_a,
               # "safety_checker": None,
               # "feature_extractor": None,
           }
           return components

     def 		a__						(	self:						Any     ,		__a:						str     ,		__a:						Tuple=0	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					Any        =	image / 2 + 0.5
           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Dict        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					Tuple        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Tuple        =	{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 5.0,
               """output_type""": """numpy""",
               """strength""": 0.75,
           }
           return inputs

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase					:					Union[str, Any]        =	self.get_dummy_components()
           lowerCamelCase					:					Optional[int]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					int        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[Any]        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Optional[int]        =	sd_pipe(**__a	).images
           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)

           lowerCamelCase					:					Any        =	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]	)

           assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)

     def 		a__						(	self:						Optional[Any]	)->    str:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)

     def 		a__						(	self:						List[str]	)->    Optional[Any]:
           pass







     def 		a__						(	self:						List[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	self.get_dummy_components()
           lowerCamelCase					:					Union[str, Any]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					str        =	sd_pipe.to(__a	)
           lowerCamelCase					:					Any        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           # forward without prompt embeds
           lowerCamelCase					:					Dict        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Any        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Optional[int]        =	negative_prompt
           lowerCamelCase					:					Tuple        =	3 * [inputs["""prompt"""]]

           lowerCamelCase					:					List[Any]        =	sd_pipe(**__a	)
           lowerCamelCase					:					Optional[int]        =	output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           lowerCamelCase					:					Tuple        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					List[Any]        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Tuple        =	3 * [inputs.pop("""prompt"""	)]

           (
               (
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			
           )					:					Union[str, Any]        =	sd_pipe.encode_prompt(__a     ,		negative_prompt=__a	)

           lowerCamelCase					:					int        =	sd_pipe(
               **__a     ,		prompt_embeds=__a     ,		negative_prompt_embeds=__a     ,		pooled_prompt_embeds=__a     ,		negative_pooled_prompt_embeds=__a     ,		)
           lowerCamelCase					:					Union[str, Any]        =	output.images[0, -3:, -3:, -1]

           # make sure that it's equal
           assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4


@slow
@require_torch_gpu
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Dict	)->    str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Any="cpu"     ,		__a:						str=torch.floataa     ,		__a:						Any=0	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					List[Any]        =	np.random.RandomState(__a	).standard_normal((1, 4, 64, 64)	)
           lowerCamelCase					:					List[str]        =	torch.from_numpy(__a	).to(device=__a     ,		dtype=__a	)
           lowerCamelCase					:					int        =	{
               """prompt""": """a photograph of an astronaut riding a horse""",
               """latents""": latents,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs







     def 		a__						(	self:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""	)
           pipe.to(__a	)
           pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[int]        =	self.get_inputs(__a	)
           lowerCamelCase					:					Optional[Any]        =	pipe(**__a	).images
           lowerCamelCase					:					Dict        =	image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowerCamelCase					:					List[str]        =	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]	)
           assert np.abs(image_slice - expected_slice	).max() < 7e-3




