import functools
import operator

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


UpperCAmelCase_           =				logging.get_logger(__name__)

UpperCAmelCase_           =				{
    '''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 			__magic_name__	(		_lowercase     ):





   """simple docstring"""




   lowerCAmelCase   :     str	      = '''unispeech'''



   def __init__(						self       :	Union[str, Any]		,	_lowercase       :	Dict=32		,	_lowercase       :	Optional[Any]=768		,	_lowercase       :	Optional[Any]=12		,	_lowercase       :	int=12		,	_lowercase       :	Optional[int]=3_072		,	_lowercase       :	int="gelu"		,	_lowercase       :	Union[str, Any]=0.1		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Dict=0.1		,	_lowercase       :	List[str]=0.0		,	_lowercase       :	List[Any]=0.0		,	_lowercase       :	List[Any]=0.1		,	_lowercase       :	int=0.1		,	_lowercase       :	Optional[Any]=0.02		,	_lowercase       :	List[Any]=1E-5		,	_lowercase       :	int="group"		,	_lowercase       :	Optional[int]="gelu"		,	_lowercase       :	Any=(512, 512, 512, 512, 512, 512, 512)		,	_lowercase       :	Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)		,	_lowercase       :	Dict=(10, 3, 3, 3, 3, 2, 2)		,	_lowercase       :	List[str]=False		,	_lowercase       :	Any=128		,	_lowercase       :	Tuple=16		,	_lowercase       :	Optional[int]=False		,	_lowercase       :	List[str]=True		,	_lowercase       :	Any=0.05		,	_lowercase       :	Any=10		,	_lowercase       :	Optional[int]=2		,	_lowercase       :	Tuple=0.0		,	_lowercase       :	Any=10		,	_lowercase       :	str=0		,	_lowercase       :	List[str]=320		,	_lowercase       :	int=2		,	_lowercase       :	List[Any]=0.1		,	_lowercase       :	Optional[int]=100		,	_lowercase       :	List[Any]=256		,	_lowercase       :	Optional[Any]=256		,	_lowercase       :	Optional[Any]=0.1		,	_lowercase       :	int="mean"		,	_lowercase       :	Dict=False		,	_lowercase       :	Tuple=False		,	_lowercase       :	Dict=256		,	_lowercase       :	Union[str, Any]=80		,	_lowercase       :	List[str]=0		,	_lowercase       :	List[Any]=1		,	_lowercase       :	List[Any]=2		,	_lowercase       :	Union[str, Any]=0.5		,	**_lowercase       :	Union[str, Any]		,	):



    """simple docstring"""



    super().__init__(**_snake_case		,	pad_token_id=_snake_case		,	bos_token_id=_snake_case		,	eos_token_id=_snake_case				)
    _UpperCamelCase:  List[str]    						=			hidden_size
    _UpperCamelCase:  int    						=			feat_extract_norm
    _UpperCamelCase:  Union[str, Any]    						=			feat_extract_activation
    _UpperCamelCase:  Any    						=			list(_snake_case				)
    _UpperCamelCase:  int    						=			list(_snake_case				)
    _UpperCamelCase:  List[str]    						=			list(_snake_case				)
    _UpperCamelCase:  Dict    						=			conv_bias
    _UpperCamelCase:  Dict    						=			num_conv_pos_embeddings
    _UpperCamelCase:  Any    						=			num_conv_pos_embedding_groups
    _UpperCamelCase:  Dict    						=			len(self.conv_dim				)
    _UpperCamelCase:  Any    						=			num_hidden_layers
    _UpperCamelCase:  str    						=			intermediate_size
    _UpperCamelCase:  Union[str, Any]    						=			hidden_act
    _UpperCamelCase:  Dict    						=			num_attention_heads
    _UpperCamelCase:  Tuple    						=			hidden_dropout
    _UpperCamelCase:  Tuple    						=			attention_dropout
    _UpperCamelCase:  List[Any]    						=			activation_dropout
    _UpperCamelCase:  Optional[int]    						=			feat_proj_dropout
    _UpperCamelCase:  int    						=			final_dropout
    _UpperCamelCase:  Tuple    						=			layerdrop
    _UpperCamelCase:  int    						=			layer_norm_eps
    _UpperCamelCase:  List[str]    						=			initializer_range
    _UpperCamelCase:  int    						=			num_ctc_classes
    _UpperCamelCase:  Tuple    						=			vocab_size
    _UpperCamelCase:  Optional[Any]    						=			do_stable_layer_norm
    _UpperCamelCase:  str    						=			use_weighted_layer_sum
    _UpperCamelCase:  Any    						=			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:  int    						=			apply_spec_augment
    _UpperCamelCase:  Union[str, Any]    						=			mask_time_prob
    _UpperCamelCase:  List[Any]    						=			mask_time_length
    _UpperCamelCase:  List[str]    						=			mask_time_min_masks
    _UpperCamelCase:  Union[str, Any]    						=			mask_feature_prob
    _UpperCamelCase:  List[str]    						=			mask_feature_length
    _UpperCamelCase:  List[Any]    						=			mask_feature_min_masks

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

    # ctc loss
    _UpperCamelCase:  Union[str, Any]    						=			ctc_loss_reduction
    _UpperCamelCase:  List[Any]    						=			ctc_zero_infinity

    # pretraining loss
    _UpperCamelCase:  Dict    						=			replace_prob



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



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

"""simple docstring"""





import operator
def 				A_  (__a					, __a = False					, __a = None					):

					'''simple docstring'''





					A_								=    operator.lt if reverse else operator.gt
					A_								=    solution or []

					if not arr:
										return solution

					A_								=    [arr.pop(0					)]
					for i, item in enumerate(__a					):
										if _operator(__a					, sublist[-1]					):
															sublist.append(__a					)
															arr.pop(__a					)

    #  merging sublist into solution list
					if not solution:
										solution.extend(__a					)
					else:
										while sublist:
															A_								=    sublist.pop(0					)
															for i, xx in enumerate(__a					):
																				if not _operator(__a					, __a					):
																									solution.insert(__a					, __a					)
																									break
															else:
																				solution.append(__a					)

					strand_sort(__a					, __a					, __a					)
					return solution


if __name__ == "__main__":
		assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
		assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]




