


"""simple docstring"""


import functools
import operator

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


a_											=     logging.get_logger(__name__)

a_											=     {
    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      'hubert'







		def __init__(       self   :							str     , a__   :							str=32     , a__   :							Any=7_68     , a__   :							Optional[int]=12     , a__   :							Optional[Any]=12     , a__   :							Tuple=30_72     , a__   :							Any="gelu"     , a__   :							List[str]=0.1     , a__   :							Optional[int]=0.1     , a__   :							Union[str, Any]=0.1     , a__   :							Optional[int]=True     , a__   :							List[Any]=0.0     , a__   :							int=0.1     , a__   :							Union[str, Any]=0.1     , a__   :							str=0.0_2     , a__   :							List[Any]=1E-5     , a__   :							Optional[Any]="group"     , a__   :							List[str]="gelu"     , a__   :							int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     , a__   :							Dict=(5, 2, 2, 2, 2, 2, 2)     , a__   :							int=(10, 3, 3, 3, 3, 2, 2)     , a__   :							int=False     , a__   :							Dict=1_28     , a__   :							int=16     , a__   :							List[Any]=False     , a__   :							int=True     , a__   :							Dict=0.0_5     , a__   :							Union[str, Any]=10     , a__   :							Optional[Any]=2     , a__   :							Dict=0.0     , a__   :							List[Any]=10     , a__   :							Dict=0     , a__   :							List[str]="sum"     , a__   :							Optional[Any]=False     , a__   :							Union[str, Any]=False     , a__   :							Tuple=2_56     , a__   :							Optional[int]=0     , a__   :							Union[str, Any]=1     , a__   :							Union[str, Any]=2     , **a__   :							Optional[int]     , )							->    int:





						'''simple docstring'''







						super().__init__(**a__     , pad_token_id=a__     , bos_token_id=a__     , eos_token_id=a__		)
						_A       	=      hidden_size
						_A       	=      feat_extract_norm
						_A       	=      feat_extract_activation
						_A       	=      list(a__		)
						_A       	=      list(a__		)
						_A       	=      list(a__		)
						_A       	=      conv_bias
						_A       	=      num_conv_pos_embeddings
						_A       	=      num_conv_pos_embedding_groups
						_A       	=      len(self.conv_dim		)
						_A       	=      num_hidden_layers
						_A       	=      intermediate_size
						_A       	=      hidden_act
						_A       	=      num_attention_heads
						_A       	=      hidden_dropout
						_A       	=      attention_dropout
						_A       	=      activation_dropout
						_A       	=      feat_proj_layer_norm
						_A       	=      feat_proj_dropout
						_A       	=      final_dropout
						_A       	=      layerdrop
						_A       	=      layer_norm_eps
						_A       	=      initializer_range
						_A       	=      vocab_size
						_A       	=      do_stable_layer_norm
						_A       	=      use_weighted_layer_sum
						_A       	=      classifier_proj_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
										    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
										    F""" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"""
										    F""" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A       	=      apply_spec_augment
						_A       	=      mask_time_prob
						_A       	=      mask_time_length
						_A       	=      mask_time_min_masks
						_A       	=      mask_feature_prob
						_A       	=      mask_feature_length
						_A       	=      mask_feature_min_masks

						# ctc loss
						_A       	=      ctc_loss_reduction
						_A       	=      ctc_zero_infinity







		@property
		def 			a_			(       self   :							str		)							->    Union[str, Any]:





						'''simple docstring'''







						return functools.reduce(operator.mul     , self.conv_stride     , 1		)



"""simple docstring"""


def   a__			(					__lowercase )						->						bool:
				# must be
				_A       	=      [list(__lowercase ) for row in matrix]
				if matrix and isinstance(__lowercase  ,      __lowercase ):
								if isinstance(matrix[0]  ,      __lowercase ):
												_A       	=      0
												for row in matrix:
																if prev_len == 0:
																				_A       	=      len(__lowercase )
																				_A       	=      True
																else:
																				_A       	=      prev_len == len(__lowercase )
								else:
												_A       	=      True
				else:
								_A       	=      False

				return result






def   a__			(					__lowercase )						->						None:
				if check_matrix(__lowercase ) and len(__lowercase ) > 0:
								_A       	=      [list(__lowercase ) for row in a]
								_A       	=      len(__lowercase )
								if isinstance(a[0]  ,      __lowercase ):
												_A       	=      len(a[0] )
								else:
												for dat in a:
																print(__lowercase )
												return

								# horizotal printing increasing
								for i in range(0  ,      __lowercase ):
												print(a[0][i] )
								# vertical printing down
								for i in range(1  ,      __lowercase ):
												print(a[i][mat_col - 1] )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2  ,      -1  ,      -1 ):
																print(a[mat_row - 1][i] )
        # vertical printing up
								for i in range(mat_row - 2  ,      0  ,      -1 ):
												print(a[i][0] )
								_A       	=      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(__lowercase ) > 0:
												spiral_print_clockwise(__lowercase )
								else:
												return
				else:
								print("Not a valid matrix" )
								return






def   a__			(					__lowercase )						->						list[int]:
				if matrix:
								return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__lowercase ) )[::-1] )
				else:
								return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a_											=     [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)