
"""simple docstring"""







import functools
import operator

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   'wavlm'





					def __init__(				self   :							Optional[Any]							,						snake_case_   :							Optional[int]=32							,						snake_case_   :							List[Any]=768							,						snake_case_   :							str=12							,						snake_case_   :							str=12							,						snake_case_   :							Dict=3072							,						snake_case_   :							Optional[int]="gelu"							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							List[Any]=0.1							,						snake_case_   :							List[Any]=0.0							,						snake_case_   :							Tuple=0.1							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							List[Any]=0.02							,						snake_case_   :							Union[str, Any]=1e-5							,						snake_case_   :							List[str]="group"							,						snake_case_   :							Dict="gelu"							,						snake_case_   :							Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Optional[int]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							List[str]=128							,						snake_case_   :							Tuple=16							,						snake_case_   :							str=320							,						snake_case_   :							List[str]=800							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							List[str]=True							,						snake_case_   :							str=0.05							,						snake_case_   :							Tuple=10							,						snake_case_   :							int=2							,						snake_case_   :							int=0.0							,						snake_case_   :							List[str]=10							,						snake_case_   :							List[Any]=320							,						snake_case_   :							List[str]=2							,						snake_case_   :							List[str]=0.1							,						snake_case_   :							Optional[int]=100							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							Tuple=256							,						snake_case_   :							str=0.1							,						snake_case_   :							Dict="mean"							,						snake_case_   :							List[str]=False							,						snake_case_   :							str=False							,						snake_case_   :							List[Any]=256							,						snake_case_   :							str=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[str]=(5, 3, 3, 1, 1)							,						snake_case_   :							Dict=(1, 2, 3, 1, 1)							,						snake_case_   :							List[str]=512							,						snake_case_   :							Any=80							,						snake_case_   :							Dict=0							,						snake_case_   :							Union[str, Any]=1							,						snake_case_   :							List[Any]=2							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							Union[str, Any]=3							,						snake_case_   :							Optional[Any]=2							,						snake_case_   :							str=3							,						snake_case_   :							str=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_norm
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_buckets
												__snake_case     			=       max_bucket_distance
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       num_ctc_classes
												__snake_case     			=       vocab_size
												__snake_case     			=       do_stable_layer_norm
												__snake_case     			=       use_weighted_layer_sum
												__snake_case     			=       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
												__snake_case     			=       apply_spec_augment
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length

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

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

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

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

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





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return functools.reduce(operator.mul							,						self.conv_stride							,						1    )


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     list:







							"""simple docstring"""


							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(1							,    SCREAMING_SNAKE_CASE ):
														__snake_case     			=       collection[i]
														__snake_case     			=       0
														__snake_case     			=       i - 1

														while low <= high:
																					__snake_case     			=       (low + high) // 2
																					if val < collection[mid]:
																												__snake_case     			=       mid - 1
																					else:
																												__snake_case     			=       mid + 1
														for j in range(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    -1 ):
																					__snake_case     			=       collection[j - 1]
														__snake_case     			=       val
							return collection


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      input("""Enter numbers separated by a comma:\n""").strip()
		_SCREAMING_SNAKE_CASE	       =      [int(item) for item in user_input.split(""",""")]
		print(binary_insertion_sort(unsorted))
