






"""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__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  TextToVideoSDPipeline
      __UpperCamelCase	 =  TEXT_TO_IMAGE_PARAMS
      __UpperCamelCase	 =  TEXT_TO_IMAGE_BATCH_PARAMS
      # No `output_type`.
      __UpperCamelCase	 =  frozenset(
          [
              "num_inference_steps",
              "generator",
              "latents",
              "return_dict",
              "callback",
              "callback_steps",
          ] )
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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						,	)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		DDIMScheduler(
              beta_start=0.0_00_85						,	beta_end=0.0_12						,	beta_schedule='''scaled_linear'''						,	clip_sample=lowercase_						,	set_alpha_to_one=lowercase_						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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=128						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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=1000						,	hidden_act='''gelu'''						,	projection_dim=512						,	)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		CLIPTextModel(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

          SCREAMING_SNAKE_CASE_      :  str    						=		{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
          }
          return components
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[int]=0):




          '''simple docstring'''


          if str(lowercase_).startswith('''mps'''):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.manual_seed(lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  int    						=		torch.Generator(device=lowercase_).manual_seed(lowercase_)
          SCREAMING_SNAKE_CASE_      :  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 _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_dummy_inputs(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''np'''
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		sd_pipe(**lowercase_).frames
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		frames[0][-3:, -3:, -1]

          assert frames[0].shape == (64, 64, 3)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_						,	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 _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_						,	expected_max_diff=1e-2)
      @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''')
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


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




          '''simple docstring'''


          pass
      @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass



      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          return super().test_progress_bar()




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''')

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

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

          SCREAMING_SNAKE_CASE_      :  str    						=		pipe(lowercase_						,	generator=lowercase_						,	num_inference_steps=25						,	output_type='''pt''').frames
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		video_frames.cpu().numpy()

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



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''')

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

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''Spiderman is surfing'''
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.Generator(device='''cpu''').manual_seed(0)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pipe(lowercase_						,	generator=lowercase_						,	num_inference_steps=2						,	output_type='''pt''').frames
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		video_frames.cpu().numpy()

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










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


