






import functools
import operator

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


__a      :       int				  =   logging.get_logger(__name__)

__a      :       Tuple				  =   {
    "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       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"wavlm"




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


         """simple docstring"""



         super().__init__(**UpperCamelCase_ ,	pad_token_id=UpperCamelCase_ ,	bos_token_id=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_				)
         __A         =	hidden_size
         __A         =	feat_extract_norm
         __A         =	feat_extract_activation
         __A         =	list(UpperCamelCase_				)
         __A         =	list(UpperCamelCase_				)
         __A         =	list(UpperCamelCase_				)
         __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(UpperCamelCase_				)
         __A         =	list(UpperCamelCase_				)
         __A         =	list(UpperCamelCase_				)
         __A         =	xvector_output_dim





     @property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



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












from __future__ import annotations



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int    )						->      list[list[int]]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	0
    __A         =	sum(__lowercase    )
    create_state_space_tree(__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase			,				__lowercase    )
    return result



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list[int]			,				__lowercase   :			int			,				__lowercase   :			int			,				__lowercase   :			list[int]			,				__lowercase   :			list[list[int]]			,				__lowercase   :			int			,				)						->      None:





    """simple docstring"""

    if sum(__lowercase    ) > max_sum or (remaining_nums_sum + sum(__lowercase    )) < max_sum:
        return
    if sum(__lowercase    ) == max_sum:
        result.append(__lowercase    )
        return
    for index in range(__lowercase			,				len(__lowercase    )    ):
        create_state_space_tree(
            __lowercase			,				__lowercase			,				index + 1			,				[*path, nums[index]]			,				__lowercase			,				remaining_nums_sum - nums[index]			,				)
__a      :       str				  =   [3, 34, 4, 12, 5, 2]
__a      :       Optional[Any]				  =   9
__a      :       List[str]				  =   generate_sum_of_subsets_soln(nums, max_sum)






print(*result)




