



'''simple docstring'''

import functools
import operator

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


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '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      __magic_name__						(   lowerCamelCase__       ):
							lowerCAmelCase	:   str           = 'unispeech-sat'







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

									# parameters for pretraining with codevector quantized representations
									_a					:				List[str]				=							num_codevectors_per_group
									_a					:				str				=							num_codevector_groups
									_a					:				Any				=							contrastive_logits_temperature
									_a					:				int				=							feat_quantizer_dropout
									_a					:				Tuple				=							num_negatives
									_a					:				List[str]				=							codevector_dim
									_a					:				Optional[Any]				=							proj_codevector_dim
									_a					:				int				=							diversity_loss_weight

									# ctc loss
									_a					:				List[Any]				=							ctc_loss_reduction
									_a					:				Tuple				=							ctc_zero_infinity

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									_a					:				Tuple				=							classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									_a					:				Dict				=							list(__lowerCamelCase		)
									_a					:				Any				=							list(__lowerCamelCase		)
									_a					:				Optional[int]				=							list(__lowerCamelCase		)
									_a					:				Tuple				=							xvector_output_dim







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









'''simple docstring'''

def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list:
		if any(not isinstance(lowerCAmelCase_						,      lowerCAmelCase_  ) or x < 0 for x in sequence  ):
				raise TypeError('Sequence must be list of non-negative integers'  )
		for _ in range(len(lowerCAmelCase_  )  ):
				for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_						,      sequence[1:]  )  ):
						if rod_upper > rod_lower:
								sequence[i] -= rod_upper - rod_lower
								sequence[i + 1] += rod_upper - rod_lower
		return sequence


if __name__ == "__main__":
							assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




