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      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  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       __A   (							self       :				Tuple      )     ->		int:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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_      =     DDIMScheduler(
            beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule="scaled_linear"     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   )
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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_      =     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=1_000     ,   hidden_act="gelu"     ,   projection_dim=512     ,   )
        SCREAMING_SNAKE_CASE_      =     CLIPTextModel(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

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







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

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

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

        assert frames[0].shape == (32, 32, 3)
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				int      )     ->		Optional[int]:
        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__magic_name__     ,   expected_max_diff=5e-3      )







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







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







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







  def       __A   (							self       :				List[Any]      )     ->		int:
        return super().test_progress_bar()



@slow
@skip_mps
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







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

        # 10 frames
        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cpu"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     torch.randn((1, 10, 3, 1_024, 576)     ,   generator=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     video.to("cuda"      )

        SCREAMING_SNAKE_CASE_      =     "Spiderman is surfing"

        SCREAMING_SNAKE_CASE_      =     pipe(__magic_name__     ,   video=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=3     ,   output_type="pt"      ).frames

        SCREAMING_SNAKE_CASE_      =     np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656]      )
        assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array      ).sum() < 1e-2



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


