






import functools
import operator

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


_a      	=  logging.get_logger(__name__)

_a      	=  {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "unispeech"

	def __init__(							self       ,    UpperCAmelCase=32       ,    UpperCAmelCase=768       ,    UpperCAmelCase=12       ,    UpperCAmelCase=12       ,    UpperCAmelCase=3072       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=1e-5       ,    UpperCAmelCase="group"       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)       ,    UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)       ,    UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)       ,    UpperCAmelCase=False       ,    UpperCAmelCase=128       ,    UpperCAmelCase=16       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=0.05       ,    UpperCAmelCase=10       ,    UpperCAmelCase=2       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=10       ,    UpperCAmelCase=0       ,    UpperCAmelCase=320       ,    UpperCAmelCase=2       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=100       ,    UpperCAmelCase=256       ,    UpperCAmelCase=256       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase="mean"       ,    UpperCAmelCase=False       ,    UpperCAmelCase=False       ,    UpperCAmelCase=256       ,    UpperCAmelCase=80       ,    UpperCAmelCase=0       ,    UpperCAmelCase=1       ,    UpperCAmelCase=2       ,    UpperCAmelCase=0.5       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase       ,    pad_token_id=UpperCAmelCase       ,    bos_token_id=UpperCAmelCase       ,    eos_token_id=UpperCAmelCase    )
							_UpperCAmelCase									=			hidden_size
							_UpperCAmelCase									=			feat_extract_norm
							_UpperCAmelCase									=			feat_extract_activation
							_UpperCAmelCase									=			list(UpperCAmelCase    )
							_UpperCAmelCase									=			list(UpperCAmelCase    )
							_UpperCAmelCase									=			list(UpperCAmelCase    )
							_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									=			num_ctc_classes
							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			do_stable_layer_norm
							_UpperCAmelCase									=			use_weighted_layer_sum
							_UpperCAmelCase									=			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
							_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

							# pretraining loss
							_UpperCAmelCase									=			replace_prob

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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








def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


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