





"""simple docstring"""





import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Dict						    =   TextToVideoSDPipeline
       __magic_name__	:Any						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:Any						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       # No `output_type`.
       __magic_name__	:Union[str, Any]						    =   frozenset(
           [
               """num_inference_steps""",
               """generator""",
               """latents""",
               """return_dict""",
               """callback""",
               """callback_steps""",
           ]			)






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				UNetaDConditionModel(
             block_out_channels=(3_2, 6_4, 6_4, 6_4)       ,					layers_per_block=2       ,					sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')       ,					up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')       ,					cross_attention_dim=3_2       ,					attention_head_dim=4       ,					)
         lowerCAmelCase__     :List[str]											=				DDIMScheduler(
             beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					beta_schedule='scaled_linear'       ,					clip_sample=__UpperCAmelCase       ,					set_alpha_to_one=__UpperCAmelCase       ,					)
         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,					latent_channels=4       ,					sample_size=1_2_8       ,					)
         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				CLIPTextConfig(
             bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					hidden_act='gelu'       ,					projection_dim=5_1_2       ,					)
         lowerCAmelCase__     :Tuple											=				CLIPTextModel(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         lowerCAmelCase__     :int											=				{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
         }
         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :Any											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :List[Any]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'output_type': 'pt',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				'cpu'  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase__     :Any											=				self.get_dummy_components()
         lowerCAmelCase__     :Tuple											=				TextToVideoSDPipeline(**__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				sd_pipe.to(__UpperCAmelCase )
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				self.get_dummy_inputs(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				'np'
         lowerCAmelCase__     :int											=				sd_pipe(**__UpperCAmelCase ).frames
         lowerCAmelCase__     :int											=				frames[0][-3:, -3:, -1]

         assert frames[0].shape == (6_4, 6_4, 3)
         lowerCAmelCase__     :int											=				np.array([1_58.0, 1_60.0, 1_53.0, 1_25.0, 1_00.0, 1_21.0, 1_11.0, 93.0, 1_13.0] )

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__UpperCAmelCase       ,					expected_max_diff=3E-3 )






       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available()       ,					reason='XFormers attention is only available with CUDA and `xformers` installed'       ,					)
       def 	snake_case		(       self ):



         '''simple docstring'''

         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCAmelCase       ,					expected_max_diff=1E-2 )






       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().test_progress_bar()






@slow
@skip_mps
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy' )

         lowerCAmelCase__     :Optional[Any]											=				TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
         lowerCAmelCase__     :Optional[int]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         lowerCAmelCase__     :int											=				pipe.to('cuda' )

         lowerCAmelCase__     :int											=				'Spiderman is surfing'
         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )

         lowerCAmelCase__     :str											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=2_5       ,					output_type='pt' ).frames
         lowerCAmelCase__     :int											=				video_frames.cpu().numpy()

         assert np.abs(expected_video - video ).mean() < 5E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy' )

         lowerCAmelCase__     :List[str]											=				TextToVideoSDPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b' )
         lowerCAmelCase__     :Tuple											=				pipe.to('cuda' )

         lowerCAmelCase__     :Dict											=				'Spiderman is surfing'
         lowerCAmelCase__     :List[Any]											=				torch.Generator(device='cpu' ).manual_seed(0 )

         lowerCAmelCase__     :Tuple											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=2       ,					output_type='pt' ).frames
         lowerCAmelCase__     :List[str]											=				video_frames.cpu().numpy()

         assert np.abs(expected_video - video ).mean() < 5E-2







"""simple docstring"""





from __future__ import annotations

import math
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->bool:





  """simple docstring"""


  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5	,  int(math.sqrt(_SCREAMING_SNAKE_CASE     ) + 1     )	,  6     ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True


__A											=						[num for num in range(3, 10_0001, 2) if not is_prime(num)]
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->list[int]:





  """simple docstring"""


  if not isinstance(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ):
    raise ValueError('n must be an integer'     )
  if n <= 0:
    raise ValueError('n must be >= 0'     )

  lowerCAmelCase__     :Tuple											=				[]
  for num in range(len(_SCREAMING_SNAKE_CASE     )     ):
    lowerCAmelCase__     :int											=				0
    while 2 * i * i <= odd_composites[num]:
      lowerCAmelCase__     :int											=				odd_composites[num] - 2 * i * i
      if is_prime(_SCREAMING_SNAKE_CASE     ):
        break
      i += 1
    else:
      list_nums.append(odd_composites[num]     )
      if len(_SCREAMING_SNAKE_CASE     ) == n:
        return list_nums

  return []
def 					__A  ()   ->int:





  """simple docstring"""


  return compute_nums(1     )[0]


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