





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

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[int]:
         '''simple docstring'''

         torch.manual_seed(0       )
         UpperCAmelCase_											=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		,  )
         UpperCAmelCase_											=DDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  clip_sample=_lowerCAmelCase		,  set_alpha_to_one=_lowerCAmelCase		,  )
         torch.manual_seed(0       )
         UpperCAmelCase_											=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       )
         UpperCAmelCase_											=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		,  )
         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 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						List[Any]=0       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=floats_tensor((1, 3, 3, 32, 32)		,  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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         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 == (32, 32, 3)
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         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 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         pass



      @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."       )
      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Optional[int]:
         '''simple docstring'''

         pass



      @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."       )
      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         pass






      def 	lowerCAmelCase__    (	self:						int       )						->    str:
         '''simple docstring'''

         return super().test_progress_bar()

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



      def 	lowerCAmelCase__    (	self:						int       )						->    List[Any]:
         '''simple docstring'''

         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, 10, 3, 1024, 576)		,  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_45_89_84, -1.1_27_92_97, -0.9_66_30_86, -0.91_50_39_06, -0.75_09_76_56]       )
         assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1e-2











from ....configuration_utils import PretrainedConfig
from ....utils import logging


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)

__lowercase  :   Union[str, Any]						      ={
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}

class      A       (	__lowercase						):
      _snake_case							     ='''van'''



      def __init__(	self:						str		,  _lowerCAmelCase:						List[Any]=224		,  _lowerCAmelCase:						Any=3		,  _lowerCAmelCase:						Tuple=[7, 3, 3, 3]		,  _lowerCAmelCase:						Union[str, Any]=[4, 2, 2, 2]		,  _lowerCAmelCase:						List[str]=[64, 128, 320, 512]		,  _lowerCAmelCase:						Tuple=[3, 3, 12, 3]		,  _lowerCAmelCase:						Union[str, Any]=[8, 8, 4, 4]		,  _lowerCAmelCase:						Any="gelu"		,  _lowerCAmelCase:						Optional[int]=0.02		,  _lowerCAmelCase:						List[str]=1e-6		,  _lowerCAmelCase:						Any=1e-2		,  _lowerCAmelCase:						Any=0.0		,  _lowerCAmelCase:						Optional[Any]=0.0		,  **_lowerCAmelCase:						List[str]		,  )						->    Dict:
         '''simple docstring'''

         super().__init__(**_lowerCAmelCase       )
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=patch_sizes
         UpperCAmelCase_											=strides
         UpperCAmelCase_											=hidden_sizes
         UpperCAmelCase_											=depths
         UpperCAmelCase_											=mlp_ratios
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=layer_scale_init_value
         UpperCAmelCase_											=drop_path_rate
         UpperCAmelCase_											=dropout_rate




