

from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def  _snake_case		(snake_case_					:     np.ndarray    ,       snake_case_					:     np.ndarray    ,       snake_case_					:     np.ndarray  )							->		np.ndarray:
  __UpperCAmelCase	:  Dict		=     np.stack((horizontal_flow, vertical_flow)    ,       2  )

  # Create a grid of all pixel coordinates and subtract the flow to get the
  # target pixels coordinates
  __UpperCAmelCase	:  Union[str, Any]		=     np.stack(
      np.meshgrid(np.arange(0    ,       image.shape[1]  )    ,       np.arange(0    ,       image.shape[0]  )  )    ,       2  )
  __UpperCAmelCase	:  Any		=     np.round(grid - flow  ).astype(np.intaa  )

  # Find the locations outside of the original image
  __UpperCAmelCase	:  List[Any]		=     (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]  ))
  __UpperCAmelCase	:  Any		=     0

  __UpperCAmelCase	:  Union[str, Any]		=     image[grid[:, :, 1], grid[:, :, 0]]

  # Set pixels at invalid locations to 0
  __UpperCAmelCase	:  Tuple		=     0

  return warped





def  _snake_case		(snake_case_					:     np.ndarray    ,       snake_case_					:     np.ndarray    ,       snake_case_					:     SupportsIndex    ,       snake_case_					:     float | None = None    ,       )							->		tuple[np.ndarray, np.ndarray]:
  if alpha is None:
    __UpperCAmelCase	:  str		=     0.1

  # Initialize flow
  __UpperCAmelCase	:  Optional[int]		=     np.zeros_like(snake_case_  )
  __UpperCAmelCase	:  Dict		=     np.zeros_like(snake_case_  )

  # Prepare kernels for the calculation of the derivatives and the average velocity
  __UpperCAmelCase	:  Any		=     np.array([[-1, 1], [-1, 1]]  ) * 0.25
  __UpperCAmelCase	:  Dict		=     np.array([[-1, -1], [1, 1]]  ) * 0.25
  __UpperCAmelCase	:  Optional[int]		=     np.array([[1, 1], [1, 1]]  ) * 0.25
  __UpperCAmelCase	:  Optional[int]		=     np.array(
      [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]  )

  # Iteratively refine the flow
  for _ in range(snake_case_  ):
    __UpperCAmelCase	:  Any		=     warp(snake_case_    ,       snake_case_    ,       snake_case_  )
    __UpperCAmelCase	:  List[str]		=     convolve(snake_case_    ,       snake_case_  ) + convolve(snake_case_    ,       snake_case_  )
    __UpperCAmelCase	:  Union[str, Any]		=     convolve(snake_case_    ,       snake_case_  ) + convolve(snake_case_    ,       snake_case_  )
    __UpperCAmelCase	:  List[Any]		=     convolve(snake_case_    ,       snake_case_  ) + convolve(snake_case_    ,       -kernel_t  )

    __UpperCAmelCase	:  str		=     convolve(snake_case_    ,       snake_case_  )
    __UpperCAmelCase	:  Dict		=     convolve(snake_case_    ,       snake_case_  )

    # This updates the flow as proposed in the paper (Step 12)
    __UpperCAmelCase	:  List[str]		=     (
        derivative_x * avg_horizontal_velocity
        + derivative_y * avg_vertical_velocity
        + derivative_t
    )
    __UpperCAmelCase	:  List[Any]		=     update / (alpha**2 + derivative_x**2 + derivative_y**2)

    __UpperCAmelCase	:  Dict		=     avg_horizontal_velocity - derivative_x * update
    __UpperCAmelCase	:  Optional[int]		=     avg_vertical_velocity - derivative_y * update

  return horizontal_flow, vertical_flow


if __name__ == "__main__":
      import doctest

      doctest.testmod()













import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

