"""simple docstring"""


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Dict   							=							KandinskyVaaPriorPipeline
    _snake_case   :     List[Any]   							=							['prompt']
    _snake_case   :     Optional[Any]   							=							['prompt', 'negative_prompt']
    _snake_case   :     Union[str, Any]   							=							[
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    _snake_case   :     Union[str, Any]   							=							False


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      return 32


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return 32


    @property
    def    lowerCAmelCase_     (    self		:     str    ):
      return self.time_input_dim


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.time_input_dim * 4


    @property
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return 100


    @property
    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )
      return tokenizer


    @property
    def    lowerCAmelCase_     (    self		:     Dict    ):
      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextConfig(
          bos_token_id=0      , eos_token_id=2      , hidden_size=self.text_embedder_hidden_size      , projection_dim=self.text_embedder_hidden_size      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )
      return CLIPTextModelWithProjection(__lowerCAmelCase    )


    @property
    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      torch.manual_seed(0    )

      _UpperCAmelCase         =					{
          """num_attention_heads""": 2,
          """attention_head_dim""": 12,
          """embedding_dim""": self.text_embedder_hidden_size,
          """num_layers""": 1,
      }

      _UpperCAmelCase         =					PriorTransformer(**__lowerCAmelCase    )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _UpperCAmelCase         =					nn.Parameter(torch.ones(model.clip_std.shape    )    )
      return model


    @property
    def    lowerCAmelCase_     (    self		:     List[str]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size      , image_size=224      , projection_dim=self.text_embedder_hidden_size      , intermediate_size=37      , num_attention_heads=4      , num_channels=3      , num_hidden_layers=5      , patch_size=14      , )

      _UpperCAmelCase         =					CLIPVisionModelWithProjection(__lowerCAmelCase    )
      return model


    @property
    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					CLIPImageProcessor(
          crop_size=224      , do_center_crop=__lowerCAmelCase      , do_normalize=__lowerCAmelCase      , do_resize=__lowerCAmelCase      , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]      , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]      , resample=3      , size=224      , )

      return image_processor


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.dummy_prior
      _UpperCAmelCase         =					self.dummy_image_encoder
      _UpperCAmelCase         =					self.dummy_text_encoder
      _UpperCAmelCase         =					self.dummy_tokenizer
      _UpperCAmelCase         =					self.dummy_image_processor

      _UpperCAmelCase         =					UnCLIPScheduler(
          variance_type="""fixed_small_log"""      , prediction_type="""sample"""      , num_train_timesteps=1000      , clip_sample=__lowerCAmelCase      , clip_sample_range=10.0      , )

      _UpperCAmelCase         =					{
          """prior""": prior,
          """image_encoder""": image_encoder,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """scheduler""": scheduler,
          """image_processor""": image_processor,
      }

      return components


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Tuple=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
      _UpperCAmelCase         =					{
          """prompt""": """horse""",
          """generator""": generator,
          """guidance_scale""": 4.0,
          """num_inference_steps""": 2,
          """output_type""": """np""",
      }
      return inputs


    def    lowerCAmelCase_     (    self		:     Any    ):
      _UpperCAmelCase         =					"""cpu"""

      _UpperCAmelCase         =					self.get_dummy_components()

      _UpperCAmelCase         =					self.pipeline_class(**__lowerCAmelCase    )
      _UpperCAmelCase         =					pipe.to(__lowerCAmelCase    )

      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )

      _UpperCAmelCase         =					pipe(**self.get_dummy_inputs(__lowerCAmelCase    )    )
      _UpperCAmelCase         =					output.image_embeds

      _UpperCAmelCase         =					pipe(
          **self.get_dummy_inputs(__lowerCAmelCase    )      , return_dict=__lowerCAmelCase      , )[0]

      _UpperCAmelCase         =					image[0, -10:]
      _UpperCAmelCase         =					image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _UpperCAmelCase         =					np.array(
          [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]    )

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


    @skip_mps
    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					torch_device == """cpu"""
      _UpperCAmelCase         =					True
      _UpperCAmelCase         =					False

      self._test_inference_batch_single_identical(
          test_max_difference=__lowerCAmelCase      , relax_max_difference=__lowerCAmelCase      , test_mean_pixel_difference=__lowerCAmelCase      , )


    @skip_mps
    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""
      _UpperCAmelCase         =					False

      self._test_attention_slicing_forward_pass(
          test_max_difference=__lowerCAmelCase      , test_mean_pixel_difference=__lowerCAmelCase      , )

"""simple docstring"""


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

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

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

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

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

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

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
