


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


__lowerCAmelCase									=				logging.get_logger(__name__)

__lowerCAmelCase									=				{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}





class     __a     (       __UpperCamelCase ):
 __lowercase					:				List[Any]  	=					'reformer'
 __lowercase					:				Optional[Any]  	=					['past_buckets_states']
 __lowercase					:				Optional[Any]  	=					{}
 def __init__(  self							,					lowerCAmelCase__=64							,					lowerCAmelCase__=["local", "lsh", "local", "lsh", "local", "lsh"]							,					lowerCAmelCase__=1.0							,					lowerCAmelCase__=True							,					lowerCAmelCase__=[64, 64]							,					lowerCAmelCase__=[64, 192]							,					lowerCAmelCase__=0							,					lowerCAmelCase__=2							,					lowerCAmelCase__=512							,					lowerCAmelCase__=None							,					lowerCAmelCase__="relu"							,					lowerCAmelCase__=0.0_5							,					lowerCAmelCase__=256							,					lowerCAmelCase__=0.0_2							,					lowerCAmelCase__=False							,					lowerCAmelCase__=1E-12							,					lowerCAmelCase__=1							,					lowerCAmelCase__=0							,					lowerCAmelCase__=0.0_5							,					lowerCAmelCase__=64							,					lowerCAmelCase__=64							,					lowerCAmelCase__=0.0							,					lowerCAmelCase__=1							,					lowerCAmelCase__=0							,					lowerCAmelCase__=4_096							,					lowerCAmelCase__=12							,					lowerCAmelCase__=None							,					lowerCAmelCase__=1							,					lowerCAmelCase__=0							,					lowerCAmelCase__=320							,					lowerCAmelCase__=False							,					lowerCAmelCase__=True							,					lowerCAmelCase__=None							,					**lowerCAmelCase__							,					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  hash_seed
        lowercase__:  Dict         =  vocab_size
        lowercase__:  Any         =  attention_head_size
        lowercase__:  Optional[int]         =  hidden_size
        lowercase__:  Union[str, Any]         =  num_attention_heads
        lowercase__:  Tuple         =  num_hashes
        lowercase__:  Tuple         =  len(lowerCAmelCase__					)
        lowercase__:  int         =  tuple(lowerCAmelCase__					) if isinstance(lowerCAmelCase__							,					lowerCAmelCase__					) else num_buckets
        lowercase__:  Union[str, Any]         =  lsh_attn_chunk_length
        lowercase__:  Dict         =  local_attn_chunk_length
        lowercase__:  Any         =  lsh_num_chunks_after
        lowercase__:  List[str]         =  lsh_num_chunks_before
        lowercase__:  Any         =  local_num_chunks_after
        lowercase__:  Dict         =  local_num_chunks_before
        lowercase__:  Union[str, Any]         =  hidden_act
        lowercase__:  int         =  feed_forward_size
        lowercase__:  Dict         =  hidden_dropout_prob
        lowercase__:  str         =  lsh_attention_probs_dropout_prob
        lowercase__:  Tuple         =  local_attention_probs_dropout_prob
        lowercase__:  Tuple         =  max_position_embeddings
        lowercase__:  str         =  initializer_range
        lowercase__:  str         =  layer_norm_eps
        lowercase__:  Any         =  axial_pos_embds
        lowercase__:  Optional[Any]         =  tuple(lowerCAmelCase__					)
        lowercase__:  Any         =  tuple(lowerCAmelCase__					)
        lowercase__:  Tuple         =  axial_norm_std
        lowercase__:  Any         =  chunk_size_lm_head
        lowercase__:  Union[str, Any]         =  attn_layers
        lowercase__:  int         =  use_cache
        lowercase__:  List[str]         =  classifier_dropout
        super().__init__(
            pad_token_id=lowerCAmelCase__							,					eos_token_id=lowerCAmelCase__							,					is_decoder=lowerCAmelCase__							,					tie_word_embeddings=lowerCAmelCase__							,					**lowerCAmelCase__							,					)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

 # show result images
 imshow('''sobel filter''', sobel_grad)
 imshow('''sobel theta''', sobel_theta)
 waitKey(0)


