

def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int					)	->					list:




				'''simple docstring'''





				A__						=	int(SCREAMING_SNAKE_CASE_					)
				if n_element < 1:
								A__						=	ValueError("a should be a positive number"					)
								raise my_error

				A__						=	[1]
				A__			, A__			, A__						=	(0, 0, 0)
				A__						=	1
				while index < n_element:
								while hamming_list[i] * 2 <= hamming_list[-1]:
												i += 1
								while hamming_list[j] * 3 <= hamming_list[-1]:
												j += 1
								while hamming_list[k] * 5 <= hamming_list[-1]:
												k += 1
								hamming_list.append(
								    min(hamming_list[i] * 2		,				hamming_list[j] * 3		,				hamming_list[k] * 5					)					)
								index += 1
				return hamming_list


if __name__ == "__main__":
	lowerCAmelCase__		      =       input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	lowerCAmelCase__		      =       hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f"""The list with nth numbers is: {hamming_numbers}""")
	print("""-----------------------------------------------------""")


"""simple docstring"""


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     int   							=							StableUnCLIPPipeline
    _snake_case   :     str   							=							TEXT_TO_IMAGE_PARAMS
    _snake_case   :     Any   							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case   :     Optional[Any]   							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case   :     str   							=							TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _snake_case   :     str   							=							False


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					32
      _UpperCAmelCase         =					embedder_hidden_size

      # prior components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=__lowerCAmelCase      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					PriorTransformer(
          num_attention_heads=2      , attention_head_dim=12      , embedding_dim=__lowerCAmelCase      , num_layers=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDPMScheduler(
          variance_type="""fixed_small_log"""      , prediction_type="""sample"""      , num_train_timesteps=1000      , clip_sample=__lowerCAmelCase      , clip_sample_range=5.0      , beta_schedule="""squaredcos_cap_v2"""      , )

      # regular denoising components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase    )
      _UpperCAmelCase         =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=32      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					UNetaDConditionModel(
          sample_size=32      , in_channels=4      , out_channels=4      , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      , block_out_channels=(32, 64)      , attention_head_dim=(2, 4)      , class_embed_type="""projection"""      , projection_class_embeddings_input_dim=embedder_projection_dim * 2      , cross_attention_dim=__lowerCAmelCase      , layers_per_block=1      , upcast_attention=__lowerCAmelCase      , use_linear_projection=__lowerCAmelCase      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDIMScheduler(
          beta_schedule="""scaled_linear"""      , beta_start=0.00_085      , beta_end=0.012      , prediction_type="""v_prediction"""      , set_alpha_to_one=__lowerCAmelCase      , steps_offset=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					AutoencoderKL()

      _UpperCAmelCase         =					{
          # prior components
          """prior_tokenizer""": prior_tokenizer,
          """prior_text_encoder""": prior_text_encoder,
          """prior""": prior,
          """prior_scheduler""": prior_scheduler,
          # image noising components
          """image_normalizer""": image_normalizer,
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder,
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
      }

      return components


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     str=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
      _UpperCAmelCase         =					{
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """prior_num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase    )



@slow
@require_torch_gpu
class 				a (						unittest.TestCase      ):


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


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""    )

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					torch.Generator(device="""cpu"""    ).manual_seed(0    )
      _UpperCAmelCase         =					pipe("""anime turle"""      , generator=__lowerCAmelCase      , output_type="""np"""    )

      _UpperCAmelCase         =					output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any    ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      _UpperCAmelCase         =					pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					pipe(
          """anime turtle"""      , prior_num_inference_steps=2      , num_inference_steps=2      , output_type="""np"""      , )

      _UpperCAmelCase         =					torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
