'''simple docstring'''




import functools
import operator

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


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[Any]							="unispeech-sat"






     def __init__(			self		:		Dict  ,    a		:		str=32  ,    a		:		Any=7_68  ,    a		:		Optional[Any]=12  ,    a		:		Optional[int]=12  ,    a		:		int=30_72  ,    a		:		int="gelu"  ,    a		:		Dict=0.1  ,    a		:		Dict=0.1  ,    a		:		List[Any]=0.1  ,    a		:		Tuple=0.0  ,    a		:		Optional[Any]=0.0  ,    a		:		Tuple=0.1  ,    a		:		List[Any]=0.1  ,    a		:		str=0.02  ,    a		:		List[Any]=1e-5  ,    a		:		int="group"  ,    a		:		Union[str, Any]="gelu"  ,    a		:		Optional[int]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)  ,    a		:		List[Any]=(5, 2, 2, 2, 2, 2, 2)  ,    a		:		int=(10, 3, 3, 3, 3, 2, 2)  ,    a		:		Optional[Any]=False  ,    a		:		Any=1_28  ,    a		:		Tuple=16  ,    a		:		str=False  ,    a		:		Optional[Any]=True  ,    a		:		Dict=0.05  ,    a		:		List[Any]=10  ,    a		:		Any=2  ,    a		:		Optional[Any]=0.0  ,    a		:		Optional[Any]=10  ,    a		:		Any=0  ,    a		:		Any=3_20  ,    a		:		str=2  ,    a		:		List[str]=0.1  ,    a		:		List[str]=1_00  ,    a		:		List[str]=2_56  ,    a		:		str=2_56  ,    a		:		Dict=0.1  ,    a		:		Optional[Any]="mean"  ,    a		:		str=False  ,    a		:		Tuple=False  ,    a		:		Optional[Any]=2_56  ,    a		:		int=(5_12, 5_12, 5_12, 5_12, 15_00)  ,    a		:		int=(5, 3, 3, 1, 1)  ,    a		:		Any=(1, 2, 3, 1, 1)  ,    a		:		Union[str, Any]=5_12  ,    a		:		Optional[int]=0  ,    a		:		Optional[int]=1  ,    a		:		Optional[int]=2  ,    a		:		int=5_04  ,    **a		:		Dict  ,    ):





        """simple docstring"""



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

        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
        __lowerCamelCase          =  apply_spec_augment
        __lowerCamelCase          =  mask_time_prob
        __lowerCamelCase          =  mask_time_length
        __lowerCamelCase          =  mask_time_min_masks
        __lowerCamelCase          =  mask_feature_prob
        __lowerCamelCase          =  mask_feature_length
        __lowerCamelCase          =  mask_feature_min_masks

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

        # ctc loss
        __lowerCamelCase          =  ctc_loss_reduction
        __lowerCamelCase          =  ctc_zero_infinity

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

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






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





        """simple docstring"""



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

'''simple docstring'''




from __future__ import annotations






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   list[list[int]]:
    __lowerCamelCase          =  []
    create_all_state(1    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  []    ,  UpperCamelCase__    )
    return result






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  )     ->   None:
    if level == 0:
        total_list.append(current_list[:]    )
        return

    for i in range(UpperCamelCase__    ,  total_number - level + 2    ):
        current_list.append(UpperCamelCase__    )
        create_all_state(i + 1    ,  UpperCamelCase__    ,  level - 1    ,  UpperCamelCase__    ,  UpperCamelCase__    )
        current_list.pop()






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   None:
    for i in total_list:
        print(*UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=4
  __UpperCAmelCase												=2
  __UpperCAmelCase												=generate_all_combinations(n, k)
  print_all_state(total_list)
