



import functools
import operator

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


__snake_case		:Optional[Any]   	=  logging.get_logger(__name__)

__snake_case		:Any   	=  {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					List[Any]		      =		'''wavlm'''



       def __init__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  int=32  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=768  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=12  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=12  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=3_072  ,	__SCREAMING_SNAKE_CASE							:  Tuple="gelu"  ,	__SCREAMING_SNAKE_CASE							:  List[str]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Any=0.1  ,	__SCREAMING_SNAKE_CASE							:  int=0.1  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.0  ,	__SCREAMING_SNAKE_CASE							:  Any=0.1  ,	__SCREAMING_SNAKE_CASE							:  str=0.1  ,	__SCREAMING_SNAKE_CASE							:  Any=0.02  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=1E-5  ,	__SCREAMING_SNAKE_CASE							:  int="group"  ,	__SCREAMING_SNAKE_CASE							:  List[str]="gelu"  ,	__SCREAMING_SNAKE_CASE							:  int=(512, 512, 512, 512, 512, 512, 512)  ,	__SCREAMING_SNAKE_CASE							:  int=(5, 2, 2, 2, 2, 2, 2)  ,	__SCREAMING_SNAKE_CASE							:  Any=(10, 3, 3, 3, 3, 2, 2)  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	__SCREAMING_SNAKE_CASE							:  Any=128  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=16  ,	__SCREAMING_SNAKE_CASE							:  Dict=320  ,	__SCREAMING_SNAKE_CASE							:  str=800  ,	__SCREAMING_SNAKE_CASE							:  int=False  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  str=0.05  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=10  ,	__SCREAMING_SNAKE_CASE							:  Dict=2  ,	__SCREAMING_SNAKE_CASE							:  Dict=0.0  ,	__SCREAMING_SNAKE_CASE							:  Tuple=10  ,	__SCREAMING_SNAKE_CASE							:  str=320  ,	__SCREAMING_SNAKE_CASE							:  Dict=2  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=100  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=256  ,	__SCREAMING_SNAKE_CASE							:  List[str]=256  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.1  ,	__SCREAMING_SNAKE_CASE							:  List[str]="mean"  ,	__SCREAMING_SNAKE_CASE							:  Tuple=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,	__SCREAMING_SNAKE_CASE							:  Dict=256  ,	__SCREAMING_SNAKE_CASE							:  Any=(512, 512, 512, 512, 1_500)  ,	__SCREAMING_SNAKE_CASE							:  List[str]=(5, 3, 3, 1, 1)  ,	__SCREAMING_SNAKE_CASE							:  List[str]=(1, 2, 3, 1, 1)  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=512  ,	__SCREAMING_SNAKE_CASE							:  Any=80  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=0  ,	__SCREAMING_SNAKE_CASE							:  int=1  ,	__SCREAMING_SNAKE_CASE							:  List[str]=2  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=3  ,	__SCREAMING_SNAKE_CASE							:  Dict=2  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	**__SCREAMING_SNAKE_CASE							:  int  ,	):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE  ,	pad_token_id=__SCREAMING_SNAKE_CASE  ,	bos_token_id=__SCREAMING_SNAKE_CASE  ,	eos_token_id=__SCREAMING_SNAKE_CASE)
           __a		 =       hidden_size
           __a		 =       feat_extract_norm
           __a		 =       feat_extract_activation
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       conv_bias
           __a		 =       num_buckets
           __a		 =       max_bucket_distance
           __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_dropout
           __a		 =       final_dropout
           __a		 =       layerdrop
           __a		 =       layer_norm_eps
           __a		 =       initializer_range
           __a		 =       num_ctc_classes
           __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

           # parameters for pretraining with codevector quantized representations
           __a		 =       num_codevectors_per_group
           __a		 =       num_codevector_groups
           __a		 =       contrastive_logits_temperature
           __a		 =       num_negatives
           __a		 =       codevector_dim
           __a		 =       proj_codevector_dim
           __a		 =       diversity_loss_weight

           # ctc loss
           __a		 =       ctc_loss_reduction
           __a		 =       ctc_zero_infinity

           # adapter
           __a		 =       add_adapter
           __a		 =       adapter_kernel_size
           __a		 =       adapter_stride
           __a		 =       num_adapter_layers
           __a		 =       output_hidden_size or hidden_size

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           __a		 =       classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       list(__SCREAMING_SNAKE_CASE)
           __a		 =       xvector_output_dim





       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

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










from __future__ import annotations

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase       )       ):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				-1     ,				-1       )       ):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				len(_UpperCAmelCase       )       )       ):
        if board[i][j] == 1:
            return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





