
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     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







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



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		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      :	Any					=		DDIMScheduler(
           beta_start=0.0_00_85     ,						beta_end=0.0_12     ,						beta_schedule='''scaled_linear'''     ,						clip_sample=_A     ,						set_alpha_to_one=_A     ,						)
       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Dict					=		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      :	List[str]					=		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(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }
       return components



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							List[Any]     ,						_A							:							Tuple=0 ):







       """simple docstring"""




       if str(_A ).startswith('''mps''' ):
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torch.manual_seed(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	Any					=		torch.Generator(device=_A ).manual_seed(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[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  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.get_dummy_inputs(_A )
       __SCREAMING_SNAKE_CASE      :	Any					=		'''np'''
       __SCREAMING_SNAKE_CASE      :	Dict					=		sd_pipe(**_A ).frames
       __SCREAMING_SNAKE_CASE      :	Tuple					=		frames[0][-3:, -3:, -1]

       assert frames[0].shape == (64, 64, 3)
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		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  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_A     ,						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  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_A     ,						expected_max_diff=1e-2 )



     @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       pass



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







       """simple docstring"""




       pass



     @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       pass




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return super().test_progress_bar()






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






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




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

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

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		'''Spiderman is surfing'''
       __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.Generator(device='''cpu''' ).manual_seed(0 )

       __SCREAMING_SNAKE_CASE      :	int					=		pipe(_A     ,						generator=_A     ,						num_inference_steps=25     ,						output_type='''pt''' ).frames
       __SCREAMING_SNAKE_CASE      :	Tuple					=		video_frames.cpu().numpy()

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




     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		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      :	List[str]					=		pipe.to('''cuda''' )

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

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		pipe(_A     ,						generator=_A     ,						num_inference_steps=2     ,						output_type='''pt''' ).frames
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		video_frames.cpu().numpy()

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






'''simple docstring'''


from __future__ import annotations



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[float]				):




     """simple docstring"""


     if len(UpperCamelCase__				) < 2:
          raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""				)
     if any(i <= 0 for i in nums				):
          raise ValueError("""All values must be greater than 0"""				)
     a_     :							int     						=  nums.copy()
     copy_nums.sort()
     return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
