



'''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 					snake_case			(UpperCamelCase		,   UpperCamelCase		,   unittest.TestCase ):
       lowerCAmelCase__	:List[Any]     =       StableDiffusionXLImgaImgPipeline
       lowerCAmelCase__	:str     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       lowerCAmelCase__	:Optional[int]     =       PipelineTesterMixin.required_optional_params - {"latents"}
       lowerCAmelCase__	:int     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCAmelCase__	:Optional[Any]     =       IMAGE_TO_IMAGE_IMAGE_PARAMS
       lowerCAmelCase__	:str     =       IMAGE_TO_IMAGE_IMAGE_PARAMS



       def 				_a   (					self   )	->				List[Any]:
           torch.manual_seed(0   )
           lowercase__									=    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=UpperCAmelCase_  ,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  ,)
           lowercase__									=    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   )
           lowercase__									=    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   )
           lowercase__									=    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  ,)
           lowercase__									=    CLIPTextModel(UpperCAmelCase_   )
           lowercase__									=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,local_files_only=UpperCAmelCase_   )

           lowercase__									=    CLIPTextModelWithProjection(UpperCAmelCase_   )
           lowercase__									=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,local_files_only=UpperCAmelCase_   )

           lowercase__									=    {
               "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  ,UpperCAmelCase_  ,UpperCAmelCase_=0   )	->				Optional[int]:
           lowercase__									=    floats_tensor((1, 3, 32, 32)  ,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
           lowercase__									=    image / 2 + 0.5
           if str(UpperCAmelCase_   ).startswith("mps"   ):
               lowercase__									=    torch.manual_seed(UpperCAmelCase_   )
           else:
               lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    {
               "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   )	->				Optional[Any]:
           lowercase__									=    "cpu"  # ensure determinism for the device-dependent torch.Generator
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionXLImgaImgPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    sd_pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1]

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

           lowercase__									=    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   )	->				Dict:
           super().test_attention_slicing_forward_pass(expected_max_diff=3E-3   )



       def 				_a   (					self   )	->				Optional[Any]:
           super().test_inference_batch_single_identical(expected_max_diff=3E-3   )



       def 				_a   (					self   )	->				List[Any]:
           pass







       def 				_a   (					self   )	->				int:
           lowercase__									=    self.get_dummy_components()
           lowercase__									=    StableDiffusionXLImgaImgPipeline(**UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           lowercase__									=    sd_pipe.to(UpperCAmelCase_   )
           sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           # forward without prompt embeds
           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    3 * ["this is a negative prompt"]
           lowercase__									=    negative_prompt
           lowercase__									=    3 * [inputs["prompt"]]

           lowercase__									=    sd_pipe(**UpperCAmelCase_   )
           lowercase__									=    output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           lowercase__									=    self.get_dummy_inputs(UpperCAmelCase_   )
           lowercase__									=    3 * ["this is a negative prompt"]
           lowercase__									=    3 * [inputs.pop("prompt"   )]

           (
               (
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							(
               lowercase__
           )							,							
           )									=    sd_pipe.encode_prompt(UpperCAmelCase_  ,negative_prompt=UpperCAmelCase_   )

           lowercase__									=    sd_pipe(
               **UpperCAmelCase_  ,prompt_embeds=UpperCAmelCase_  ,negative_prompt_embeds=UpperCAmelCase_  ,pooled_prompt_embeds=UpperCAmelCase_  ,negative_pooled_prompt_embeds=UpperCAmelCase_  ,)
           lowercase__									=    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 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Optional[Any]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_="cpu"  ,UpperCAmelCase_=torch.floataa  ,UpperCAmelCase_=0   )	->				Optional[Any]:
           lowercase__									=    torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
           lowercase__									=    np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 4, 64, 64)   )
           lowercase__									=    torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_  ,dtype=UpperCAmelCase_   )
           lowercase__									=    {
               "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   )	->				Any:
           lowercase__									=    DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"   )
           pipe.to(UpperCAmelCase_   )
           pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

           lowercase__									=    self.get_inputs(UpperCAmelCase_   )
           lowercase__									=    pipe(**UpperCAmelCase_   ).images
           lowercase__									=    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowercase__									=    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









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int = 50_000_000							):





    '''simple docstring'''


    lowercase__									=    set()
    lowercase__									=    int((limit - 24) ** (1 / 2)							)

    lowercase__									=    set(range(3  ,prime_square_limit + 1  ,2							)							)
    primes.add(2							)
    for p in range(3  ,prime_square_limit + 1  ,2							):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p  ,prime_square_limit + 1  ,_snake_case							)							)							)

    for primea in primes:
        lowercase__									=    primea * primea
        for primea in primes:
            lowercase__									=    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowercase__									=    primea * primea * primea * primea
                lowercase__									=    square + cube + tetr
                if total >= limit:
                    break
                ret.add(_snake_case							)

    return len(_snake_case							)


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




