



'''simple docstring'''





import functools
import operator

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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """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 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Union[str, Any]     		="""unispeech-sat"""




				def __init__(						self					:						Union[str, Any],					__lowercase					:						str=32,					__lowercase					:						Any=768,					__lowercase					:						Tuple=12,					__lowercase					:						List[str]=12,					__lowercase					:						int=3072,					__lowercase					:						Optional[Any]="gelu",					__lowercase					:						Tuple=0.1,					__lowercase					:						List[Any]=0.1,					__lowercase					:						Optional[int]=0.1,					__lowercase					:						Optional[Any]=0.0,					__lowercase					:						Optional[Any]=0.0,					__lowercase					:						List[str]=0.1,					__lowercase					:						str=0.1,					__lowercase					:						Optional[int]=0.02,					__lowercase					:						Optional[int]=1e-5,					__lowercase					:						Any="group",					__lowercase					:						Dict="gelu",					__lowercase					:						List[Any]=(512, 512, 512, 512, 512, 512, 512),					__lowercase					:						Dict=(5, 2, 2, 2, 2, 2, 2),					__lowercase					:						Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__lowercase					:						List[Any]=False,					__lowercase					:						Union[str, Any]=128,					__lowercase					:						Optional[int]=16,					__lowercase					:						Union[str, Any]=False,					__lowercase					:						Optional[int]=True,					__lowercase					:						List[Any]=0.05,					__lowercase					:						Any=10,					__lowercase					:						Tuple=2,					__lowercase					:						Optional[Any]=0.0,					__lowercase					:						str=10,					__lowercase					:						List[Any]=0,					__lowercase					:						Dict=320,					__lowercase					:						str=2,					__lowercase					:						Optional[int]=0.1,					__lowercase					:						int=100,					__lowercase					:						Any=256,					__lowercase					:						str=256,					__lowercase					:						Tuple=0.1,					__lowercase					:						Dict="mean",					__lowercase					:						Optional[Any]=False,					__lowercase					:						Optional[Any]=False,					__lowercase					:						Optional[Any]=256,					__lowercase					:						Optional[int]=(512, 512, 512, 512, 1500),					__lowercase					:						Optional[Any]=(5, 3, 3, 1, 1),					__lowercase					:						Optional[int]=(1, 2, 3, 1, 1),					__lowercase					:						Tuple=512,					__lowercase					:						Tuple=0,					__lowercase					:						str=1,					__lowercase					:						Optional[Any]=2,					__lowercase					:						List[str]=504,					**__lowercase					:						Optional[int],					):
							super().__init__(**__lowercase,					pad_token_id=__lowercase,					bos_token_id=__lowercase,					eos_token_id=__lowercase						)
							lowercase__					  =				hidden_size
							lowercase__					  =				feat_extract_norm
							lowercase__					  =				feat_extract_activation
							lowercase__					  =				list(__lowercase						)
							lowercase__					  =				list(__lowercase						)
							lowercase__					  =				list(__lowercase						)
							lowercase__					  =				conv_bias
							lowercase__					  =				num_conv_pos_embeddings
							lowercase__					  =				num_conv_pos_embedding_groups
							lowercase__					  =				len(self.conv_dim						)
							lowercase__					  =				num_hidden_layers
							lowercase__					  =				intermediate_size
							lowercase__					  =				hidden_act
							lowercase__					  =				num_attention_heads
							lowercase__					  =				hidden_dropout
							lowercase__					  =				attention_dropout
							lowercase__					  =				activation_dropout
							lowercase__					  =				feat_proj_dropout
							lowercase__					  =				final_dropout
							lowercase__					  =				layerdrop
							lowercase__					  =				layer_norm_eps
							lowercase__					  =				initializer_range
							lowercase__					  =				vocab_size
							lowercase__					  =				num_clusters
							lowercase__					  =				do_stable_layer_norm
							lowercase__					  =				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
							lowercase__					  =				apply_spec_augment
							lowercase__					  =				mask_time_prob
							lowercase__					  =				mask_time_length
							lowercase__					  =				mask_time_min_masks
							lowercase__					  =				mask_feature_prob
							lowercase__					  =				mask_feature_length
							lowercase__					  =				mask_feature_min_masks

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

							# ctc loss
							lowercase__					  =				ctc_loss_reduction
							lowercase__					  =				ctc_zero_infinity

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

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




				@property
				def 							A__      (						self					:						List[str]						):
							return functools.reduce(operator.mul,					self.conv_stride,					1						)









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				len(SCREAMING_SNAKE_CASE_  )
			for i in range(SCREAMING_SNAKE_CASE_  ):
						for j in range(i + 1						,  SCREAMING_SNAKE_CASE_  ):
									if numbers[j] < numbers[i]:
												lowercase__  ,       lowercase__					  =				numbers[j], numbers[i]
			return numbers


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




