



'''simple docstring'''





def   a_   (		_UpperCAmelCase    :      int				)    ->  Dict:
      __snake_case							,     __snake_case :   Optional[int]													=    [], []
      while len(_UpperCAmelCase				) > 1:
            __snake_case							,     __snake_case :   Dict													=    min(_UpperCAmelCase				), max(_UpperCAmelCase				)
            start.append(_UpperCAmelCase				)
            end.append(_UpperCAmelCase				)
            collection.remove(_UpperCAmelCase				)
            collection.remove(_UpperCAmelCase				)
      end.reverse()
      return start + collection + end


if __name__ == "__main__":
    A__    :   Union[str, Any]           =					input('''Enter numbers separated by a comma:\n''').strip()
    A__    :   Dict           =					[int(item) for item in user_input.split(''',''')]
    print(*merge_sort(unsorted), sep=''',''')





'''simple docstring'''
import functools
import operator

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


lowerCAmelCase					:    int           = logging.get_logger(__name__)

lowerCAmelCase					:    Tuple           = {
    '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 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""unispeech-sat"""







				def __init__(       self						,				A_=32						,				A_=768						,				A_=12						,				A_=12						,				A_=3072						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=0.1						,				A_=0.0						,				A_=0.0						,				A_=0.1						,				A_=0.1						,				A_=0.02						,				A_=1e-5						,				A_="group"						,				A_="gelu"						,				A_=(512, 512, 512, 512, 512, 512, 512)						,				A_=(5, 2, 2, 2, 2, 2, 2)						,				A_=(10, 3, 3, 3, 3, 2, 2)						,				A_=False						,				A_=128						,				A_=16						,				A_=False						,				A_=True						,				A_=0.05						,				A_=10						,				A_=2						,				A_=0.0						,				A_=10						,				A_=0						,				A_=320						,				A_=2						,				A_=0.1						,				A_=100						,				A_=256						,				A_=256						,				A_=0.1						,				A_="mean"						,				A_=False						,				A_=False						,				A_=256						,				A_=(512, 512, 512, 512, 1500)						,				A_=(5, 3, 3, 1, 1)						,				A_=(1, 2, 3, 1, 1)						,				A_=512						,				A_=0						,				A_=1						,				A_=2						,				A_=504						,				**A_						,				)->		Tuple:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     feat_extract_norm
											UpperCamelCase   			=     feat_extract_activation
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     conv_bias
											UpperCamelCase   			=     num_conv_pos_embeddings
											UpperCamelCase   			=     num_conv_pos_embedding_groups
											UpperCamelCase   			=     len(self.conv_dim     )
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     hidden_dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     feat_proj_dropout
											UpperCamelCase   			=     final_dropout
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     num_clusters
											UpperCamelCase   			=     do_stable_layer_norm
											UpperCamelCase   			=     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
											UpperCamelCase   			=     apply_spec_augment
											UpperCamelCase   			=     mask_time_prob
											UpperCamelCase   			=     mask_time_length
											UpperCamelCase   			=     mask_time_min_masks
											UpperCamelCase   			=     mask_feature_prob
											UpperCamelCase   			=     mask_feature_length
											UpperCamelCase   			=     mask_feature_min_masks

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

											# ctc loss
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

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

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







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






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




