





'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    DiTPipeline
 __lowercase       		=    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 __lowercase       		=    PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 __lowercase       		=    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     torch.manual_seed(0							)
     A__    =      TransformeraDModel(
         sample_size=16						,	num_layers=2						,	patch_size=4						,	attention_head_dim=8						,	num_attention_heads=2						,	in_channels=4						,	out_channels=8						,	attention_bias=lowercase_						,	activation_fn="gelu-approximate"						,	num_embeds_ada_norm=10_00						,	norm_type="ada_norm_zero"						,	norm_elementwise_affine=lowercase_						,	)
     A__    =      AutoencoderKL()
     A__    =      DDIMScheduler()
     A__    =      {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
     return components
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Any						,	lowercase_				:Optional[int]=0							)->							Union[str, Any]:
     if str(lowercase_							).startswith("mps"							):
         A__    =      torch.manual_seed(lowercase_							)
     else:
         A__    =      torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__    =      {
         "class_labels": [1],
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Union[str, Any]:
     A__    =      "cpu"

     A__    =      self.get_dummy_components()
     A__    =      self.pipeline_class(**lowercase_							)
     pipe.to(lowercase_							)
     pipe.set_progress_bar_config(disable=lowercase_							)

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

     self.assertEqual(image.shape						,	(1, 16, 16, 3)							)
     A__    =      np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]							)
     A__    =      np.abs(image_slice.flatten() - expected_slice							).max()
     self.assertLessEqual(lowercase_						,	1E-3							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
     self._test_inference_batch_single_identical(relax_max_difference=lowercase_						,	expected_max_diff=1E-3							)



 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available()						,	reason="XFormers attention is only available with CUDA and `xformers` installed"						,	)
 def 					UpperCAmelCase_							(    self				:Dict							)->							List[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)






@require_torch_gpu
@slow
class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__    =      torch.manual_seed(0							)

     A__    =      DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"							)
     pipe.to("cuda"							)

     A__    =      ["vase", "umbrella", "white shark", "white wolf"]
     A__    =      pipe.get_label_ids(lowercase_							)

     A__    =      pipe(lowercase_						,	generator=lowercase_						,	num_inference_steps=40						,	output_type="np"							).images

     for word, image in zip(lowercase_						,	lowercase_							):
         A__    =      load_numpy(
             F"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"							)
         assert np.abs((expected_image - image).max()							) < 1E-2



 def 					UpperCAmelCase_							(    self				:List[str]							)->							Tuple:
     A__    =      DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"							)
     A__    =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
     pipe.to("cuda"							)

     A__    =      ["vase", "umbrella"]
     A__    =      pipe.get_label_ids(lowercase_							)

     A__    =      torch.manual_seed(0							)
     A__    =      pipe(lowercase_						,	generator=lowercase_						,	num_inference_steps=25						,	output_type="np"							).images

     for word, image in zip(lowercase_						,	lowercase_							):
         A__    =      load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             F"/dit/{word}_512.npy"							)

         assert np.abs((expected_image - image).max()							) < 1E-1










'''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() = }""")



