

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		: str    )    ->      Dict:
   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=lowerCAmelCase__			,						set_alpha_to_one=lowerCAmelCase__			,						)
   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(lowerCAmelCase__    )
   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		: Tuple			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Dict=0    )    ->      Optional[int]:
   # 3 frames
   UpperCAmelCase					      =  floats_tensor((1, 3, 3, 3_2, 3_2)			,						rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )

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

   UpperCAmelCase					      =  self.get_dummy_inputs(lowerCAmelCase__    )
   UpperCAmelCase					      =  "np"
   UpperCAmelCase					      =  sd_pipe(**lowerCAmelCase__    ).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    )    ->      List[Any]:
   self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase__			,						expected_max_diff=5e-3    )


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


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


 @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."    )
 def _UpperCamelCase	(						self		: List[str]    )    ->      Dict:
   pass


 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   return super().test_progress_bar()






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


 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[int]:
   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=lowerCAmelCase__    )
   UpperCAmelCase					      =  video.to("cuda"    )

   UpperCAmelCase					      =  "Spiderman is surfing"

   UpperCAmelCase					      =  pipe(lowerCAmelCase__			,						video=lowerCAmelCase__			,						generator=lowerCAmelCase__			,						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






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = ""
lowerCAmelCase__       = 1  # (0 is vertical, 1 is horizontal)
def      _lowerCAmelCase(	):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_dataset(__A   ,       __A			)
  print("Processing..."			)
  UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  update_image_and_anno(__A   ,       __A   ,       __A			)

  for index, image in enumerate(__A			):
    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    UpperCAmelCase					      =  random_chars(32			)
    UpperCAmelCase					      =  paths[index].split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    UpperCAmelCase					      =  F"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
    cva.imwrite(F"/{file_root}.jpg"   ,       __A   ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
    print(F"Success {index+1}/{len(__A			)} with {file_name}"			)
    UpperCAmelCase					      =  []
    for anno in new_annos[index]:
      UpperCAmelCase					      =  F"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
      annos_list.append(__A			)
    with open(F"/{file_root}.txt"   ,       "w"			) as outfile:
      outfile.write("\n".join(line for line in annos_list			)			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for label_file in glob.glob(os.path.join(__A   ,       "*.txt"			)			):
    UpperCAmelCase					      =  label_file.split(os.sep			)[-1].rsplit("."   ,       1			)[0]
    with open(__A			) as in_file:
      UpperCAmelCase					      =  in_file.readlines()
    UpperCAmelCase					      =  os.path.join(__A   ,       F"{label_name}.jpg"			)

    UpperCAmelCase					      =  []
    for obj_list in obj_lists:
      UpperCAmelCase					      =  obj_list.rstrip("\n"			).split(" "			)
      boxes.append(
          [
              int(obj[0]			),
              float(obj[1]			),
              float(obj[2]			),
              float(obj[3]			),
              float(obj[4]			),
          ]			)
    if not boxes:
      continue
    img_paths.append(__A			)
    labels.append(__A			)
  return img_paths, labels
def      _lowerCAmelCase(	__A   ,       __A   ,       __A = 1			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  []
  for idx in range(len(__A			)			):
    UpperCAmelCase					      =  []
    UpperCAmelCase					      =  img_list[idx]
    path_list.append(__A			)
    UpperCAmelCase					      =  anno_list[idx]
    UpperCAmelCase					      =  cva.imread(__A			)
    if flip_type == 1:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
    elif flip_type == 0:
      UpperCAmelCase					      =  cva.flip(__A   ,       __A			)
      for bbox in img_annos:
        UpperCAmelCase					      =  1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
    new_annos_lists.append(__A			)
    new_imgs_list.append(__A			)
  return new_imgs_list, new_annos_lists, path_list
def      _lowerCAmelCase(	__A = 32			):
  assert number_char > 1, "The number of character should greater than 1"
  UpperCAmelCase					      =  ascii_lowercase + digits
  return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
      main()
      print("DONE ✅")



