


'''simple docstring'''




import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_a   :   Optional[int]												=   logging.get_logger(__name__)

enable_full_determinism()




class 			_lowercase (	_UpperCamelCase						,					_UpperCamelCase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  int          =	UNetaDModel
   _SCREAMING_SNAKE_CASE  :  List[str]          =	"sample"
   @property
   def a  (						self       :      Dict      ) ->							Any:
        __snake_case           =						4
        __snake_case           =						3
        __snake_case           =						(32, 32)

        __snake_case           =						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
        __snake_case           =						torch.tensor([10]      ).to(UpperCamelCase__      )

        return {"sample": noise, "timestep": time_step}
   @property
   def a  (						self       :      Any      ) ->							Any:
        return (3, 32, 32)
   @property
   def a  (						self       :      Optional[int]      ) ->							List[Any]:
        return (3, 32, 32)





   def a  (						self       :      List[Any]      ) ->							List[str]:
        __snake_case           =						{
            'block_out_channels': (32, 64),
            'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
            'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
            'attention_head_dim': 3,
            'out_channels': 3,
            'in_channels': 3,
            'layers_per_block': 2,
            'sample_size': 32,
        }
        __snake_case           =						self.dummy_input
        return init_dict, inputs_dict




class 			_lowercase (	_UpperCamelCase						,					_UpperCamelCase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  List[str]          =	UNetaDModel
   _SCREAMING_SNAKE_CASE  :  int          =	"sample"
   @property
   def a  (						self       :      Union[str, Any]      ) ->							Optional[int]:
        __snake_case           =						4
        __snake_case           =						4
        __snake_case           =						(32, 32)

        __snake_case           =						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
        __snake_case           =						torch.tensor([10]      ).to(UpperCamelCase__      )

        return {"sample": noise, "timestep": time_step}
   @property
   def a  (						self       :      int      ) ->							Tuple:
        return (4, 32, 32)
   @property
   def a  (						self       :      int      ) ->							List[Any]:
        return (4, 32, 32)
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						{
            'sample_size': 32,
            'in_channels': 4,
            'out_channels': 4,
            'layers_per_block': 2,
            'block_out_channels': (32, 64),
            'attention_head_dim': 32,
            'down_block_types': ('DownBlock2D', 'DownBlock2D'),
            'up_block_types': ('UpBlock2D', 'UpBlock2D'),
        }
        __snake_case           =						self.dummy_input
        return init_dict, inputs_dict
   def a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        __snake_case      ,       __snake_case           =						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,   output_loading_info=UpperCamelCase__      )

        self.assertIsNotNone(UpperCamelCase__      )
        self.assertEqual(len(loading_info['missing_keys']      )       ,   0      )

        model.to(UpperCamelCase__      )
        __snake_case           =						model(**self.dummy_input      ).sample

        assert image is not None, "Make sure output is not None"
   @unittest.skipIf(torch_device != 'cuda'       ,   'This test is supposed to run on GPU'      )
   def a  (						self       :      Tuple      ) ->							List[Any]:
        __snake_case      ,       __snake_case           =						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,   output_loading_info=UpperCamelCase__      )
        model.to(UpperCamelCase__      )
        __snake_case           =						model(**self.dummy_input      ).sample

        assert image is not None, "Make sure output is not None"
   @unittest.skipIf(torch_device != 'cuda'       ,   'This test is supposed to run on GPU'      )
   def a  (						self       :      Dict      ) ->							Union[str, Any]:
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        __snake_case      ,       __snake_case           =						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,   output_loading_info=UpperCamelCase__      )
        model_accelerate.to(UpperCamelCase__      )
        model_accelerate.eval()

        __snake_case           =						torch.randn(
            1       ,   model_accelerate.config.in_channels       ,   model_accelerate.config.sample_size       ,   model_accelerate.config.sample_size       ,   generator=torch.manual_seed(0      )       ,   )
        __snake_case           =						noise.to(UpperCamelCase__      )
        __snake_case           =						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

        __snake_case           =						model_accelerate(UpperCamelCase__       ,   UpperCamelCase__      )['sample']

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        __snake_case      ,       __snake_case           =						UNetaDModel.from_pretrained(
            'fusing/unet-ldm-dummy-update'       ,   output_loading_info=UpperCamelCase__       ,   low_cpu_mem_usage=UpperCamelCase__      )
        model_normal_load.to(UpperCamelCase__      )
        model_normal_load.eval()
        __snake_case           =						model_normal_load(UpperCamelCase__       ,   UpperCamelCase__      )['sample']

        assert torch_all_close(UpperCamelCase__       ,   UpperCamelCase__       ,   rtol=1e-3      )





   def a  (						self       :      List[Any]      ) ->							Any:
        __snake_case           =						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      )
        model.eval()
        model.to(UpperCamelCase__      )

        __snake_case           =						torch.randn(
            1       ,   model.config.in_channels       ,   model.config.sample_size       ,   model.config.sample_size       ,   generator=torch.manual_seed(0      )       ,   )
        __snake_case           =						noise.to(UpperCamelCase__      )
        __snake_case           =						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

        with torch.no_grad():
             __snake_case           =						model(UpperCamelCase__       ,   UpperCamelCase__      ).sample

        __snake_case           =						output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        __snake_case           =						torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]      )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__       ,   UpperCamelCase__       ,   rtol=1e-3      )      )




class 			_lowercase (	_UpperCamelCase						,					_UpperCamelCase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Any          =	UNetaDModel
   _SCREAMING_SNAKE_CASE  :  str          =	"sample"
   @property
   def a  (						self       :      Any       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=(32, 32)      ) ->							Optional[Any]:
        __snake_case           =						4
        __snake_case           =						3

        __snake_case           =						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
        __snake_case           =						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa       ,   device=UpperCamelCase__      )

        return {"sample": noise, "timestep": time_step}
   @property
   def a  (						self       :      Any      ) ->							int:
        return (3, 32, 32)
   @property
   def a  (						self       :      Optional[int]      ) ->							Tuple:
        return (3, 32, 32)
   def a  (						self       :      str      ) ->							Tuple:
        __snake_case           =						{
            'block_out_channels': [32, 64, 64, 64],
            'in_channels': 3,
            'layers_per_block': 1,
            'out_channels': 3,
            'time_embedding_type': 'fourier',
            'norm_eps': 1e-6,
            'mid_block_scale_factor': math.sqrt(2.0      ),
            'norm_num_groups': None,
            'down_block_types': [
                'SkipDownBlock2D',
                'AttnSkipDownBlock2D',
                'SkipDownBlock2D',
                'SkipDownBlock2D',
            ],
            'up_block_types': [
                'SkipUpBlock2D',
                'SkipUpBlock2D',
                'AttnSkipUpBlock2D',
                'SkipUpBlock2D',
            ],
        }
        __snake_case           =						self.dummy_input
        return init_dict, inputs_dict
   @slow
   def a  (						self       :      Union[str, Any]      ) ->							List[Any]:
        __snake_case      ,       __snake_case           =						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'       ,   output_loading_info=UpperCamelCase__      )
        self.assertIsNotNone(UpperCamelCase__      )
        self.assertEqual(len(loading_info['missing_keys']      )       ,   0      )

        model.to(UpperCamelCase__      )
        __snake_case           =						self.dummy_input
        __snake_case           =						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
        __snake_case           =						noise
        __snake_case           =						model(**UpperCamelCase__      )

        assert image is not None, "Make sure output is not None"
   @slow
   def a  (						self       :      str      ) ->							Optional[Any]:
        __snake_case           =						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'      )
        model.to(UpperCamelCase__      )

        __snake_case           =						4
        __snake_case           =						3
        __snake_case           =						(256, 256)

        __snake_case           =						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
        __snake_case           =						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

        with torch.no_grad():
             __snake_case           =						model(UpperCamelCase__       ,   UpperCamelCase__      ).sample

        __snake_case           =						output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        __snake_case           =						torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 5_6_7.7_6_0_8]      )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__       ,   UpperCamelCase__       ,   rtol=1e-2      )      )
   def a  (						self       :      List[str]      ) ->							Dict:
        __snake_case           =						UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'      )
        model.to(UpperCamelCase__      )

        __snake_case           =						4
        __snake_case           =						3
        __snake_case           =						(32, 32)

        __snake_case           =						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
        __snake_case           =						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

        with torch.no_grad():
             __snake_case           =						model(UpperCamelCase__       ,   UpperCamelCase__      ).sample

        __snake_case           =						output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        __snake_case           =						torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]      )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__       ,   UpperCamelCase__       ,   rtol=1e-2      )      )





   def a  (						self       :      str      ) ->							Tuple:
        # not required for this model
        pass





'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



