





import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class      lowerCAmelCase							(      __UpperCamelCase,						unittest.TestCase    ):
 UpperCAmelCase__									=	DDIMPipeline
 UpperCAmelCase__									=	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 UpperCAmelCase__									=	PipelineTesterMixin.required_optional_params - {
     """num_images_per_prompt""",
     """latents""",
     """callback""",
     """callback_steps""",
 }
 UpperCAmelCase__									=	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 UpperCAmelCase__									=	False
 def   A_			(   self						:   Any			)    ->			List[Any]:
     torch.manual_seed(0			)
     lowerCamelCase__					:   str      				=	UNetaDModel(
         block_out_channels=(32, 64)    ,  layers_per_block=2    ,  sample_size=32    ,  in_channels=3    ,  out_channels=3    ,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')    ,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')    ,  )
     lowerCamelCase__					:   int      				=	DDIMScheduler()
     lowerCamelCase__					:   Union[str, Any]      				=	{'unet': unet, 'scheduler': scheduler}
     return components
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   int=0			)    ->			int:
     if str(UpperCAmelCase			).startswith('mps'			):
         lowerCamelCase__					:   Optional[int]      				=	torch.manual_seed(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   int      				=	torch.Generator(device=UpperCAmelCase			).manual_seed(UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	{
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs
 def   A_			(   self						:   Tuple			)    ->			Dict:
     lowerCamelCase__					:   Tuple      				=	'cpu'

     lowerCamelCase__					:   Tuple      				=	self.get_dummy_components()
     lowerCamelCase__					:   int      				=	self.pipeline_class(**UpperCAmelCase			)
     pipe.to(UpperCAmelCase			)
     pipe.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	self.get_dummy_inputs(UpperCAmelCase			)
     lowerCamelCase__					:   Any      				=	pipe(**UpperCAmelCase			).images
     lowerCamelCase__					:   str      				=	image[0, -3:, -3:, -1]

     self.assertEqual(image.shape    ,  (1, 32, 32, 3)			)
     lowerCamelCase__					:   List[str]      				=	np.array(
         [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]			)
     lowerCamelCase__					:   int      				=	np.abs(image_slice.flatten() - expected_slice			).max()
     self.assertLessEqual(UpperCAmelCase    ,  1e-3			)
 def   A_			(   self						:   Union[str, Any]			)    ->			int:
     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)
 def   A_			(   self						:   List[str]			)    ->			Optional[int]:
     super().test_save_load_local(expected_max_difference=3e-3			)
 def   A_			(   self						:   List[Any]			)    ->			Optional[int]:
     super().test_save_load_optional_components(expected_max_difference=3e-3			)







 def   A_			(   self						:   Union[str, Any]			)    ->			Dict:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3			)


@slow
@require_torch_gpu
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   List[str]      				=	'google/ddpm-cifar10-32'

     lowerCamelCase__					:   int      				=	UNetaDModel.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   str      				=	DDIMScheduler()

     lowerCamelCase__					:   Union[str, Any]      				=	DDIMPipeline(unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase			)
     ddim.to(UpperCAmelCase			)
     ddim.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	torch.manual_seed(0			)
     lowerCamelCase__					:   Any      				=	ddim(generator=UpperCAmelCase    ,  eta=0.0    ,  output_type='numpy'			).images

     lowerCamelCase__					:   Optional[Any]      				=	image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     lowerCamelCase__					:   List[str]      				=	np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]			)

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







 def   A_			(   self						:   Dict			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Tuple      				=	'google/ddpm-ema-bedroom-256'

     lowerCamelCase__					:   Dict      				=	UNetaDModel.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   str      				=	DDIMScheduler.from_pretrained(UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	DDIMPipeline(unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase			)
     ddpm.to(UpperCAmelCase			)
     ddpm.set_progress_bar_config(disable=UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	torch.manual_seed(0			)
     lowerCamelCase__					:   Optional[int]      				=	ddpm(generator=UpperCAmelCase    ,  output_type='numpy'			).images

     lowerCamelCase__					:   Dict      				=	image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     lowerCamelCase__					:   Tuple      				=	np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]			)

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












from __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


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





