

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


_a									=  logging.get_logger(__name__)

_a									=  {
    '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'tvlt'


     def __init__(				self     :						List[Any]     ,    UpperCAmelCase     :						Tuple=2_2_4     ,    UpperCAmelCase     :						Dict=2_0_4_8     ,    UpperCAmelCase     :						str=1_2_8     ,    UpperCAmelCase     :						List[str]=[1_6, 1_6]     ,    UpperCAmelCase     :						Dict=[1_6, 1_6]     ,    UpperCAmelCase     :						Dict=3     ,    UpperCAmelCase     :						int=1     ,    UpperCAmelCase     :						str=8     ,    UpperCAmelCase     :						int=7_6_8     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						Optional[Any]=1_2     ,    UpperCAmelCase     :						Dict=3_0_7_2     ,    UpperCAmelCase     :						Dict="gelu"     ,    UpperCAmelCase     :						Tuple=0.0     ,    UpperCAmelCase     :						Any=0.0     ,    UpperCAmelCase     :						Dict=0.02     ,    UpperCAmelCase     :						Optional[int]=1E-6     ,    UpperCAmelCase     :						Any=True     ,    UpperCAmelCase     :						str=False     ,    UpperCAmelCase     :						int=1_6     ,    UpperCAmelCase     :						Dict=5_1_2     ,    UpperCAmelCase     :						int=8     ,    UpperCAmelCase     :						Union[str, Any]=2_0_4_8     ,    UpperCAmelCase     :						Dict=0.75     ,    UpperCAmelCase     :						str=0.15     ,    UpperCAmelCase     :						List[Any]="frame-level"     ,    UpperCAmelCase     :						Any=True     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						List[str]="classification"     ,    **UpperCAmelCase     :						Dict     ,    )				->      Dict:
      super().__init__(**UpperCAmelCase   )

      if audio_mask_type not in ("frame-level", "patch_level"):
       raise ValueError(
           'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
           F'''got {audio_mask_type}'''   )

      __lowerCAmelCase:  Optional[int]        = image_size
      __lowerCAmelCase:  List[str]        = spectrogram_length
      __lowerCAmelCase:  Any        = frequency_length
      __lowerCAmelCase:  List[str]        = image_patch_size
      __lowerCAmelCase:  Tuple        = audio_patch_size
      __lowerCAmelCase:  List[Any]        = num_image_channels
      __lowerCAmelCase:  Optional[Any]        = num_audio_channels
      __lowerCAmelCase:  List[str]        = num_frames

      __lowerCAmelCase:  Optional[Any]        = hidden_size
      __lowerCAmelCase:  Dict        = num_hidden_layers
      __lowerCAmelCase:  Dict        = num_attention_heads
      __lowerCAmelCase:  List[str]        = intermediate_size
      __lowerCAmelCase:  Union[str, Any]        = hidden_act
      __lowerCAmelCase:  int        = hidden_dropout_prob
      __lowerCAmelCase:  Optional[Any]        = attention_probs_dropout_prob
      __lowerCAmelCase:  Union[str, Any]        = initializer_range
      __lowerCAmelCase:  List[Any]        = layer_norm_eps
      __lowerCAmelCase:  int        = qkv_bias
      __lowerCAmelCase:  List[str]        = use_mean_pooling

      __lowerCAmelCase:  Union[str, Any]        = decoder_num_attention_heads
      __lowerCAmelCase:  int        = decoder_hidden_size
      __lowerCAmelCase:  str        = decoder_num_hidden_layers
      __lowerCAmelCase:  Optional[Any]        = decoder_intermediate_size
      __lowerCAmelCase:  List[Any]        = pixel_mask_ratio
      __lowerCAmelCase:  List[str]        = audio_mask_ratio
      __lowerCAmelCase:  Tuple        = audio_mask_type

      __lowerCAmelCase:  Optional[int]        = task_matching
      __lowerCAmelCase:  Tuple        = task_mae
      __lowerCAmelCase:  Any        = loss_type








from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       np.ndarray:

 """simple docstring"""






 __lowerCAmelCase:  Any        = np.stack((horizontal_flow, vertical_flow)       ,   2						)

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

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

 __lowerCAmelCase:  List[str]        = image[grid[:, :, 1], grid[:, :, 0]]

 # Set pixels at invalid locations to 0
 __lowerCAmelCase:  List[str]        = 0

 return warped







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     SupportsIndex       ,   SCREAMING_SNAKE_CASE						:     float | None = None       ,   )       ->       tuple[np.ndarray, np.ndarray]:

 """simple docstring"""






 if alpha is None:
  __lowerCAmelCase:  Tuple        = 0.1

 # Initialize flow
 __lowerCAmelCase:  Optional[int]        = np.zeros_like(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = np.zeros_like(SCREAMING_SNAKE_CASE						)

 # Prepare kernels for the calculation of the derivatives and the average velocity
 __lowerCAmelCase:  List[Any]        = np.array([[-1, 1], [-1, 1]]						) * 0.2_5
 __lowerCAmelCase:  int        = np.array([[-1, -1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Tuple        = np.array([[1, 1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Dict        = 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(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Optional[int]        = warp(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[str]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[Any]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   -kernel_t						)

  __lowerCAmelCase:  Any        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Dict        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  # This updates the flow as proposed in the paper (Step 12)
  __lowerCAmelCase:  Tuple        = (
      derivative_x * avg_horizontal_velocity
      + derivative_y * avg_vertical_velocity
      + derivative_t
  )
  __lowerCAmelCase:  int        = update / (alpha**2 + derivative_x**2 + derivative_y**2)

  __lowerCAmelCase:  List[Any]        = avg_horizontal_velocity - derivative_x * update
  __lowerCAmelCase:  Union[str, Any]        = avg_vertical_velocity - derivative_y * update

 return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()





