

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 						__magic_name__    (       _snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						VideoToVideoSDPipeline
 UpperCAmelCase							    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}    ) - {"""image""", """width""", """height"""}
 UpperCAmelCase							    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}    ) - {"""image"""}
 UpperCAmelCase							    =						PipelineTesterMixin.required_optional_params - {"""latents"""}
 UpperCAmelCase							    =						False

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


 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[str]:
   torch.manual_seed(0    )
   UpperCAmelCase					      =  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			,						)
   UpperCAmelCase					      =  DDIMScheduler(
       beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule="scaled_linear"			,						clip_sample=lowercase_			,						set_alpha_to_one=lowercase_			,						)
   torch.manual_seed(0    )
   UpperCAmelCase					      =  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    )
   UpperCAmelCase					      =  CLIPTextConfig(
       bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						intermediate_size=3_7			,						layer_norm_eps=1e-0_5			,						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			,						)
   UpperCAmelCase					      =  CLIPTextModel(lowercase_    )
   UpperCAmelCase					      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

   UpperCAmelCase					      =  {
       "unet": unet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
   }
   return components


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Union[str, Any]=0    )    ->      Optional[int]:
   UpperCAmelCase					      =  floats_tensor((1, 3, 3, 3_2, 3_2)			,						rng=random.Random(lowercase_    )    ).to(lowercase_    )

   if str(lowercase_    ).startswith("mps"    ):
     UpperCAmelCase					      =  torch.manual_seed(lowercase_    )
   else:
     UpperCAmelCase					      =  torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
   UpperCAmelCase					      =  {
       "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 _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   UpperCAmelCase					      =  "cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase					      =  self.get_dummy_components()
   UpperCAmelCase					      =  VideoToVideoSDPipeline(**lowercase_    )
   UpperCAmelCase					      =  sd_pipe.to(lowercase_    )
   sd_pipe.set_progress_bar_config(disable=lowercase_    )

   UpperCAmelCase					      =  self.get_dummy_inputs(lowercase_    )
   UpperCAmelCase					      =  "np"
   UpperCAmelCase					      =  sd_pipe(**lowercase_    ).frames
   UpperCAmelCase					      =  frames[0][-3:, -3:, -1]

   assert frames[0].shape == (3_2, 3_2, 3)
   UpperCAmelCase					      =  np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1]    )

   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 _UpperCamelCase	(						self		: Tuple    )    ->      Any:
   self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_			,						expected_max_diff=5e-3    )


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


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


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


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Union[str, Any]:
   return super().test_progress_bar()






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


 def _UpperCamelCase	(						self		: Any    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"			,						torch_dtype=torch.floataa    )
   pipe.enable_model_cpu_offload()

   # 10 frames
   UpperCAmelCase					      =  torch.Generator(device="cpu"    ).manual_seed(0    )
   UpperCAmelCase					      =  torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6)			,						generator=lowercase_    )
   UpperCAmelCase					      =  video.to("cuda"    )

   UpperCAmelCase					      =  "Spiderman is surfing"

   UpperCAmelCase					      =  pipe(lowercase_			,						video=lowercase_			,						generator=lowercase_			,						num_inference_steps=3			,						output_type="pt"    ).frames

   UpperCAmelCase					      =  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      _lowerCAmelCase(	__A			):
  assert column_title.isupper()
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  len(__A			) - 1
  UpperCAmelCase					      =  0

  while index >= 0:
    UpperCAmelCase					      =  (ord(column_title[index]			) - 64) * pow(26   ,       __A			)
    answer += value
    power += 1
    index -= 1

  return answer


if __name__ == "__main__":
      from doctest import testmod

      testmod()



