


'''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   __snake_case			(  _SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   TextToVideoSDPipeline
    lowercase  					=   TEXT_TO_IMAGE_PARAMS
    lowercase  					=   TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    lowercase  					=   frozenset(
        [
            'num_inference_steps',
            'generator',
            'latents',
            'return_dict',
            'callback',
            'callback_steps',
        ])

    def __lowercase  (  self       :    str						)  ->    List[str]:
      torch.manual_seed(0						)
      lowerCAmelCase_     : Dict     =  UNetaDConditionModel(
          block_out_channels=(32, 64, 64, 64)       ,		layers_per_block=2       ,		sample_size=32       ,		in_channels=4       ,		out_channels=4       ,		down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")       ,		up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")       ,		cross_attention_dim=32       ,		attention_head_dim=4       ,		)
      lowerCAmelCase_     : Union[str, Any]     =  DDIMScheduler(
          beta_start=0.00_085       ,		beta_end=0.012       ,		beta_schedule="""scaled_linear"""       ,		clip_sample=lowerCamelCase       ,		set_alpha_to_one=lowerCamelCase       ,		)
      torch.manual_seed(0						)
      lowerCAmelCase_     : str     =  AutoencoderKL(
          block_out_channels=[32, 64]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,		latent_channels=4       ,		sample_size=1_28       ,		)
      torch.manual_seed(0						)
      lowerCAmelCase_     : Optional[int]     =  CLIPTextConfig(
          bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=10_00       ,		hidden_act="""gelu"""       ,		projection_dim=5_12       ,		)
      lowerCAmelCase_     : int     =  CLIPTextModel(lowerCamelCase						)
      lowerCAmelCase_     : str     =  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 __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    Any       ,		lowerCamelCase       :    Dict=0						)  ->    Dict:
      if str(lowerCamelCase						).startswith("""mps"""						):
        lowerCAmelCase_     : Dict     =  torch.manual_seed(lowerCamelCase						)
      else:
        lowerCAmelCase_     : str     =  torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
      lowerCAmelCase_     : Union[str, 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 __lowercase  (  self       :    Any						)  ->    Optional[int]:
      lowerCAmelCase_     : Union[str, Any]     =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_     : Optional[int]     =  self.get_dummy_components()
      lowerCAmelCase_     : List[str]     =  TextToVideoSDPipeline(**lowerCamelCase						)
      lowerCAmelCase_     : int     =  sd_pipe.to(lowerCamelCase						)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

      lowerCAmelCase_     : List[str]     =  self.get_dummy_inputs(lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  """np"""
      lowerCAmelCase_     : List[str]     =  sd_pipe(**lowerCamelCase						).frames
      lowerCAmelCase_     : Any     =  frames[0][-3:, -3:, -1]

      assert frames[0].shape == (64, 64, 3)
      lowerCAmelCase_     : Any     =  np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]						)

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

    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[Any]:
      self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase       ,		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 __lowercase  (  self       :    Dict						)  ->    Tuple:
      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase       ,		expected_max_diff=1E-2						)

    @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""						)
    def __lowercase  (  self       :    Union[str, Any]						)  ->    List[Any]:
      pass

    @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""						)
    def __lowercase  (  self       :    Any						)  ->    Optional[Any]:
      pass

    @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""						)
    def __lowercase  (  self       :    Tuple						)  ->    Tuple:
      pass





    def __lowercase  (  self       :    Dict						)  ->    Any:
      return super().test_progress_bar()







@slow
@skip_mps
class   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    def __lowercase  (  self       :    str						)  ->    Any:
      lowerCAmelCase_     : Tuple     =  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_     : Any     =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
      lowerCAmelCase_     : str     =  pipe.to("""cuda"""						)

      lowerCAmelCase_     : Any     =  """Spiderman is surfing"""
      lowerCAmelCase_     : List[str]     =  torch.Generator(device="""cpu"""						).manual_seed(0						)

      lowerCAmelCase_     : Any     =  pipe(lowerCamelCase       ,		generator=lowerCamelCase       ,		num_inference_steps=25       ,		output_type="""pt"""						).frames
      lowerCAmelCase_     : Dict     =  video_frames.cpu().numpy()

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





    def __lowercase  (  self       :    List[str]						)  ->    List[str]:
      lowerCAmelCase_     : Dict     =  load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""						)

      lowerCAmelCase_     : List[Any]     =  TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""						)
      lowerCAmelCase_     : Optional[int]     =  pipe.to("""cuda"""						)

      lowerCAmelCase_     : Tuple     =  """Spiderman is surfing"""
      lowerCAmelCase_     : List[Any]     =  torch.Generator(device="""cpu"""						).manual_seed(0						)

      lowerCAmelCase_     : Optional[Any]     =  pipe(lowerCamelCase       ,		generator=lowerCamelCase       ,		num_inference_steps=2       ,		output_type="""pt"""						).frames
      lowerCAmelCase_     : List[Any]     =  video_frames.cpu().numpy()

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







'''simple docstring'''






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def 							UpperCamelCase_     (       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  prime_factors(A__   )
  if is_square_free(A__   ):
    return -1 if len(A__   ) % 2 else 1
  return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()



