
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


@skip_mps
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						VideoToVideoSDPipeline
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}       ) - {"image", "width", "height"}
       __lowerCamelCase				       =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}       ) - {"image"}
       __lowerCamelCase				       =						PipelineTesterMixin.required_optional_params - {"latents"}
       __lowerCamelCase				       =						False

       # No `output_type`.
       __lowerCamelCase				       =						frozenset(
           [
               "num_inference_steps",
               "generator",
               "latents",
               "return_dict",
               "callback",
               "callback_steps",
           ]       )


       def 	_snake_case				(    self				)						->    Optional[Any]:
              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_							:				List[str]		=DDIMScheduler(
                  beta_start=0.00_085						,       beta_end=0.012						,       beta_schedule='''scaled_linear'''						,       clip_sample=__A						,       set_alpha_to_one=__A						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				List[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=128						,       )
              torch.manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=CLIPTextConfig(
                  bos_token_id=0						,       eos_token_id=2						,       hidden_size=32						,       intermediate_size=37						,       layer_norm_eps=1e-0_5						,       num_attention_heads=4						,       num_hidden_layers=5						,       pad_token_id=1						,       vocab_size=1_000						,       hidden_act='''gelu'''						,       projection_dim=512						,       )
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=CLIPTextModel(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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


       def 	_snake_case				(    self						,       __A						,       __A=0				)						->    Dict:
              # 3 frames
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((1, 3, 3, 32, 32)						,       rng=random.Random(__A				)				).to(__A				)

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


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_dummy_components()
              SCREAMING_SNAKE_CASE_							:				int		=VideoToVideoSDPipeline(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=sd_pipe.to(__A				)
              sd_pipe.set_progress_bar_config(disable=__A				)

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

              assert frames[0].shape == (32, 32, 3)
              SCREAMING_SNAKE_CASE_							:				Tuple		=np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]				)

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


       @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				)						->    str:
              self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__A						,       expected_max_diff=5e-3				)


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


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


       @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''				)
       def 	_snake_case				(    self				)						->    Optional[Any]:
              pass





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






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


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL'''						,       torch_dtype=torch.floataa				)
              pipe.enable_model_cpu_offload()

              # 10 frames
              SCREAMING_SNAKE_CASE_							:				Any		=torch.Generator(device='''cpu'''				).manual_seed(0				)
              SCREAMING_SNAKE_CASE_							:				str		=torch.randn((1, 10, 3, 1_024, 576)						,       generator=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=video.to('''cuda'''				)

              SCREAMING_SNAKE_CASE_							:				List[str]		='''Spiderman is surfing'''

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=pipe(__A						,       video=__A						,       generator=__A						,       num_inference_steps=3						,       output_type='''pt'''				).frames

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656]				)
              assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array				).sum() < 1e-2


def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list				)			->						float:
       SCREAMING_SNAKE_CASE_							:				Dict		=0
       while len(UpperCAmelCase_				) > 1:
              SCREAMING_SNAKE_CASE_							:				Tuple		=0
              # Consider two files with minimum cost to be merged
              for _ in range(2				):
                     SCREAMING_SNAKE_CASE_							:				int		=files.index(min(UpperCAmelCase_				)				)
                     temp += files[min_index]
                     files.pop(UpperCAmelCase_				)
              files.append(UpperCAmelCase_				)
              optimal_merge_cost += temp
       return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()
