
import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class 			lowercase_		(      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						AutoencoderKL
       __lowerCamelCase				       =						"sample"
       __lowerCamelCase				       =						1E-2


       @property
       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=4
              SCREAMING_SNAKE_CASE_							:				Dict		=3
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(32, 32)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=floats_tensor((batch_size, num_channels) + sizes				).to(__A				)

              return {"sample": image}


       @property
       def 	_snake_case				(    self				)						->    Dict:
              return (3, 32, 32)


       @property
       def 	_snake_case				(    self				)						->    Any:
              return (3, 32, 32)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[str]		={
                  '''block_out_channels''': [32, 64],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_input
              return init_dict, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              pass


       def 	_snake_case				(    self				)						->    Tuple:
              pass


       @unittest.skipIf(torch_device == '''mps'''						,       '''Gradient checkpointing skipped on MPS'''				)
       def 	_snake_case				(    self				)						->    str:
              # enable deterministic behavior for gradient checkpointing
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=self.prepare_init_args_and_inputs_for_common()
              SCREAMING_SNAKE_CASE_							:				Any		=self.model_class(**__A				)
              model.to(__A				)

              assert not model.is_gradient_checkpointing and model.training

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**__A				).sample
              # run the backwards pass on the model. For backwards pass, for simplicity purpose,
              # we won't calculate the loss and rather backprop on out.sum()
              model.zero_grad()

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn_like(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=(out - labels).mean()
              loss.backward()

              # re-instantiate the model now enabling gradient checkpointing
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.model_class(**__A				)
              # clone model
              model_a.load_state_dict(model.state_dict()				)
              model_a.to(__A				)
              model_a.enable_gradient_checkpointing()

              assert model_a.is_gradient_checkpointing and model_a.training

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model_a(**__A				).sample
              # run the backwards pass on the model. For backwards pass, for simplicity purpose,
              # we won't calculate the loss and rather backprop on out.sum()
              model_a.zero_grad()
              SCREAMING_SNAKE_CASE_							:				Dict		=(out_a - labels).mean()
              loss_a.backward()

              # compare the output and parameters gradients
              self.assertTrue((loss - loss_a).abs() < 1e-5				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=dict(model.named_parameters()				)
              SCREAMING_SNAKE_CASE_							:				Dict		=dict(model_a.named_parameters()				)
              for name, param in named_params.items():
                     self.assertTrue(torch_all_close(param.grad.data						,       named_params_a[name].grad.data						,       atol=5e-5				)				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''						,       output_loading_info=__A				)
              self.assertIsNotNone(__A				)
              self.assertEqual(len(loading_info['''missing_keys''']				)						,       0				)

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)

              assert image is not None, "Make sure output is not None"





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model.to(__A				)
              model.eval()

              if torch_device == "mps":
                     SCREAMING_SNAKE_CASE_							:				int		=torch.manual_seed(0				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=torch.Generator(device=__A				).manual_seed(0				)

              SCREAMING_SNAKE_CASE_							:				Dict		=torch.randn(
                  1						,       model.config.in_channels						,       model.config.sample_size						,       model.config.sample_size						,       generator=torch.manual_seed(0				)						,       )
              SCREAMING_SNAKE_CASE_							:				Tuple		=image.to(__A				)
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       sample_posterior=__A						,       generator=__A				).sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=output[0, -1, -3:, -3:].flatten().cpu()

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              if torch_device == "mps":
                     SCREAMING_SNAKE_CASE_							:				str		=torch.tensor(
                         [
                             -4.0_0_7_8e-0_1,
                             -3.8_3_2_3e-0_4,
                             -1.2_6_8_1e-0_1,
                             -1.1_4_6_2e-0_1,
                             2.0_0_9_5e-0_1,
                             1.0_8_9_3e-0_1,
                             -8.8_2_4_7e-0_2,
                             -3.0_3_6_1e-0_1,
                             -9.8_6_4_4e-0_3,
                         ]				)
              elif torch_device == "cpu":
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor(
                         [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor(
                         [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]				)

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






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[str]:
              return F'gaussian_noise_s={seed}_shape={"_".join([str(__A				) for s in shape]				)}.npy'


       def 	_snake_case				(    self				)						->    str:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()


       def 	_snake_case				(    self						,       __A=0						,       __A=(4, 3, 512, 512)						,       __A=False				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=torch.floataa if fpaa else torch.floataa
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.from_numpy(load_hf_numpy(self.get_file_format(__A						,       __A				)				)				).to(__A				).to(__A				)
              return image


       def 	_snake_case				(    self						,       __A="CompVis/stable-diffusion-v1-4"						,       __A=False				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Tuple		='''fp16''' if fpaa else None
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.floataa if fpaa else torch.floataa

              SCREAMING_SNAKE_CASE_							:				List[Any]		=AutoencoderKL.from_pretrained(
                  __A						,       subfolder='''vae'''						,       torch_dtype=__A						,       revision=__A						,       )
              model.to(__A				).eval()

              return model


       def 	_snake_case				(    self						,       __A=0				)						->    Any:
              if torch_device == "mps":
                     return torch.manual_seed(__A				)
              return torch.Generator(device=__A				).manual_seed(__A				)


       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
               [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.get_sd_vae_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_sd_image(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=self.get_generator(__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       generator=__A						,       sample_posterior=__A				).sample

              assert sample.shape == image.shape

              SCREAMING_SNAKE_CASE_							:				List[str]		=sample[-1, -2:, -2:, :2].flatten().float().cpu()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

              assert torch_all_close(__A						,       __A						,       atol=3e-3				)


       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
               [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
               # fmt: on
           ]				)
       @require_torch_gpu
       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_sd_vae_model(fpaa=__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_sd_image(__A						,       fpaa=__A				)
              SCREAMING_SNAKE_CASE_							:				str		=self.get_generator(__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Any		=model(__A						,       generator=__A						,       sample_posterior=__A				).sample

              assert sample.shape == image.shape

              SCREAMING_SNAKE_CASE_							:				str		=sample[-1, -2:, :2, -2:].flatten().float().cpu()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-2				)


       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
               [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_sd_vae_model()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.get_sd_image(__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A				).sample

              assert sample.shape == image.shape

              SCREAMING_SNAKE_CASE_							:				int		=sample[-1, -2:, -2:, :2].flatten().float().cpu()
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

              assert torch_all_close(__A						,       __A						,       atol=3e-3				)


       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
               [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
               # fmt: on
           ]				)
       @require_torch_gpu
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_sd_vae_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_sd_image(__A						,       shape=(3, 4, 64, 64)				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model.decode(__A				).sample

              assert list(sample.shape				) == [3, 3, 512, 512]

              SCREAMING_SNAKE_CASE_							:				List[str]		=sample[-1, -2:, :2, -2:].flatten().cpu()
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)


       @parameterized.expand(
           [
               # fmt: off
               [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
               [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
               # fmt: on
           ]				)
       @require_torch_gpu
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_sd_vae_model(fpaa=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_sd_image(__A						,       shape=(3, 4, 64, 64)						,       fpaa=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.decode(__A				).sample

              assert list(sample.shape				) == [3, 3, 512, 512]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[-1, -2:, :2, -2:].flatten().float().cpu()
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=5e-3				)


       @parameterized.expand([(13,), (16,), (27,)]				)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()						,       reason='''xformers is not required when using PyTorch 2.0.'''				)
       def 	_snake_case				(    self						,       __A				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_sd_vae_model(fpaa=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_sd_image(__A						,       shape=(3, 4, 64, 64)						,       fpaa=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				List[str]		=model.decode(__A				).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model.decode(__A				).sample

              assert list(sample.shape				) == [3, 3, 512, 512]

              assert torch_all_close(__A						,       __A						,       atol=1e-1				)


       @parameterized.expand([(13,), (16,), (37,)]				)
       @require_torch_gpu
       @unittest.skipIf(not is_xformers_available()						,       reason='''xformers is not required when using PyTorch 2.0.'''				)
       def 	_snake_case				(    self						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_sd_vae_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.get_sd_image(__A						,       shape=(3, 4, 64, 64)				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.decode(__A				).sample

              model.enable_xformers_memory_efficient_attention()
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				List[str]		=model.decode(__A				).sample

              assert list(sample.shape				) == [3, 3, 512, 512]

              assert torch_all_close(__A						,       __A						,       atol=1e-2				)





       @parameterized.expand(
           [
               # fmt: off
               [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
               [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_sd_vae_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_sd_image(__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_generator(__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Any		=model.encode(__A				).latent_dist
                     SCREAMING_SNAKE_CASE_							:				Tuple		=dist.sample(generator=__A				)

              assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

              SCREAMING_SNAKE_CASE_							:				int		=sample[0, -1, -3:, -3:].flatten().cpu()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=3e-3 if torch_device != '''mps''' else 1e-2
              assert torch_all_close(__A						,       __A						,       atol=__A				)


from math import isqrt, loga
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						list[int]:
       SCREAMING_SNAKE_CASE_							:				int		=[True] * max_number
       for i in range(2			,							isqrt(max_number - 1				) + 1				):
              if is_prime[i]:
                     for j in range(i**2			,							UpperCAmelCase_			,							UpperCAmelCase_				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=False

       return [i for i in range(2			,							UpperCAmelCase_				) if is_prime[i]]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8_0_0_8_0_0			,							UpperCAmelCase_	:     int = 8_0_0_8_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=degree * loga(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=calculate_prime_numbers(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=0
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0
       SCREAMING_SNAKE_CASE_							:				Any		=len(UpperCAmelCase_				) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]				)
                  + prime_numbers[left] * loga(prime_numbers[right]				)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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