




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


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						str  ="tvlt"




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

           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__   	=					image_size
           lowerCamelCase__   	=					spectrogram_length
           lowerCamelCase__   	=					frequency_length
           lowerCamelCase__   	=					image_patch_size
           lowerCamelCase__   	=					audio_patch_size
           lowerCamelCase__   	=					num_image_channels
           lowerCamelCase__   	=					num_audio_channels
           lowerCamelCase__   	=					num_frames

           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias
           lowerCamelCase__   	=					use_mean_pooling

           lowerCamelCase__   	=					decoder_num_attention_heads
           lowerCamelCase__   	=					decoder_hidden_size
           lowerCamelCase__   	=					decoder_num_hidden_layers
           lowerCamelCase__   	=					decoder_intermediate_size
           lowerCamelCase__   	=					pixel_mask_ratio
           lowerCamelCase__   	=					audio_mask_ratio
           lowerCamelCase__   	=					audio_mask_type

           lowerCamelCase__   	=					task_matching
           lowerCamelCase__   	=					task_mae
           lowerCamelCase__   	=					loss_type





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class       A      :
     """simple docstring"""

     def __init__(     self	: int						,	A__	: List[Any]						,	A__	: int      )					->							List[str]:
           lowerCamelCase__   	=					0
           # max greyscale value for #FFFFFF
           lowerCamelCase__   	=					int(self.get_greyscale(2_5_5						,	2_5_5						,	2_5_5      )      )

           if not self.min_threshold < threshold < self.max_threshold:
                 lowerCamelCase__   	=					F"""Factor value should be from 0 to {self.max_threshold}"""
                 raise ValueError(A__      )

           lowerCamelCase__   	=					input_img
           lowerCamelCase__   	=					threshold
           lowerCamelCase__	,		lowerCamelCase__   	=					self.input_img.shape[1], self.input_img.shape[0]

           # error table size (+4 columns and +1 row) greater than input image because of
           # lack of if statements
           lowerCamelCase__   	=					[
               [0 for _ in range(self.height + 4      )] for __ in range(self.width + 1      )
           ]
           lowerCamelCase__   	=					np.ones((self.width, self.height, 3)						,	np.uinta      ) * 2_5_5




     @classmethod
     def 			__snake_case (     cls	: int						,	A__	: int						,	A__	: int						,	A__	: int      )					->							float:
           return 0.114 * blue + 0.587 * green + 0.2126 * red




     def 			__snake_case (     self	: List[Any]      )					->							None:
           for y in range(self.height      ):
                 for x in range(self.width      ):
                       lowerCamelCase__   	=					int(self.get_greyscale(*self.input_img[y][x]      )      )
                       if self.threshold > greyscale + self.error_table[y][x]:
                             lowerCamelCase__   	=					(0, 0, 0)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y]
                       else:
                             lowerCamelCase__   	=					(2_5_5, 2_5_5, 2_5_5)
                             lowerCamelCase__   	=					greyscale + self.error_table[x][y] - 2_5_5
                       self.error_table[y][x + 1] += int(8 / 3_2 * current_error      )
                       self.error_table[y][x + 2] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x] += int(8 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error      )
                       self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error      )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          Burkes(imread("""image_data/lena.jpg""", 1), threshold)
          for threshold in (1, 126, 130, 140)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                F"""Original image with dithering threshold: {burkes.threshold}""",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()