

import functools
import operator

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


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Dict    			=     {
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  int    				=			'''sew-d'''






					def __init__(					self       ,			A__=32       ,			A__=768       ,			A__=12       ,			A__=12       ,			A__=3072       ,			A__=2       ,			A__=512       ,			A__=256       ,			A__=True       ,			A__=True       ,			A__=("p2c", "c2p")       ,			A__="layer_norm"       ,			A__="gelu_python"       ,			A__=0.1       ,			A__=0.1       ,			A__=0.1       ,			A__=0.0       ,			A__=0.1       ,			A__=0.0_2       ,			A__=1e-7       ,			A__=1e-5       ,			A__="group"       ,			A__="gelu"       ,			A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,			A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,			A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,			A__=False       ,			A__=128       ,			A__=16       ,			A__=True       ,			A__=0.0_5       ,			A__=10       ,			A__=2       ,			A__=0.0       ,			A__=10       ,			A__=0       ,			A__="mean"       ,			A__=False       ,			A__=False       ,			A__=256       ,			A__=0       ,			A__=1       ,			A__=2       ,			**A__       ,			):
						super().__init__(**A__       ,			pad_token_id=A__       ,			bos_token_id=A__       ,			eos_token_id=A__     )
						A__				:       Union[str, Any]	  =						hidden_size
						A__				:       Dict	  =						feat_extract_norm
						A__				:       Tuple	  =						feat_extract_activation
						A__				:       str	  =						list(A__     )
						A__				:       str	  =						list(A__     )
						A__				:       List[str]	  =						list(A__     )
						A__				:       Any	  =						conv_bias
						A__				:       Any	  =						num_conv_pos_embeddings
						A__				:       Union[str, Any]	  =						num_conv_pos_embedding_groups
						A__				:       List[str]	  =						len(self.conv_dim     )
						A__				:       Union[str, Any]	  =						num_hidden_layers
						A__				:       List[str]	  =						intermediate_size
						A__				:       str	  =						squeeze_factor
						A__				:       Any	  =						max_position_embeddings
						A__				:       Any	  =						position_buckets
						A__				:       Tuple	  =						share_att_key
						A__				:       Any	  =						relative_attention
						A__				:       List[Any]	  =						norm_rel_ebd
						A__				:       str	  =						list(A__     )
						A__				:       int	  =						hidden_act
						A__				:       Tuple	  =						num_attention_heads
						A__				:       Optional[Any]	  =						hidden_dropout
						A__				:       Tuple	  =						attention_dropout
						A__				:       Tuple	  =						activation_dropout
						A__				:       str	  =						feat_proj_dropout
						A__				:       Any	  =						final_dropout
						A__				:       Union[str, Any]	  =						layer_norm_eps
						A__				:       int	  =						feature_layer_norm_eps
						A__				:       Optional[int]	  =						initializer_range
						A__				:       List[str]	  =						vocab_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)`,"""
							    F"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
							    F"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						A__				:       Dict	  =						apply_spec_augment
						A__				:       List[Any]	  =						mask_time_prob
						A__				:       Dict	  =						mask_time_length
						A__				:       Union[str, Any]	  =						mask_time_min_masks
						A__				:       int	  =						mask_feature_prob
						A__				:       Tuple	  =						mask_feature_length
						A__				:       Dict	  =						mask_feature_min_masks

						# ctc loss
						A__				:       str	  =						ctc_loss_reduction
						A__				:       str	  =						ctc_zero_infinity

						# sequence classification
						A__				:       Union[str, Any]	  =						use_weighted_layer_sum
						A__				:       List[str]	  =						classifier_proj_size






					@property
					def     __A     (					self     ):
						return functools.reduce(operator.mul       ,			self.conv_stride       ,			1     )






def 		UpperCamelCase   (lowercase_:					list   )	-> list:
	A__				:       Union[str, Any]	  =						False
	while is_sorted is False:  # Until all the indices are traversed keep looping
		A__				:       Dict	  =						True
		for i in range(0       , len(lowercase_   ) - 1       , 2   ):  # iterating over all even indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False

		for i in range(1       , len(lowercase_   ) - 1       , 2   ):  # iterating over all odd indices
			if input_list[i] > input_list[i + 1]:
				A__  ,  A__				:       Dict	  =						input_list[i + 1], input_list[i]
				# swapping if elements not in order
				A__				:       Optional[Any]	  =						False
	return input_list


if __name__ == "__main__":
	print('Enter list to be sorted')
	A_			:					Optional[int]    			=     [int(x) for x in input().split()]
	# inputing elements of the list in one line
	A_			:					Optional[int]    			=     odd_even_sort(input_list)
	print('The sorted list is')
	print(sorted_list)



