



import functools
import operator

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


__A   			=logging.get_logger(__name__)

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

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       """sew"""




			def __init__(   self							: Any       ,       a_							: Optional[int]=32       ,       a_							: Dict=7_68       ,       a_							: List[str]=12       ,       a_							: Any=12       ,       a_							: List[str]=30_72       ,       a_							: Any=2       ,       a_							: Union[str, Any]="gelu"       ,       a_							: List[Any]=0.1       ,       a_							: Optional[int]=0.1       ,       a_							: Optional[int]=0.1       ,       a_							: int=0.0       ,       a_							: Tuple=0.1       ,       a_							: Union[str, Any]=0.1       ,       a_							: int=0.0_2       ,       a_							: List[str]=1e-5       ,       a_							: Dict="group"       ,       a_							: Any="gelu"       ,       a_							: Optional[Any]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)       ,       a_							: Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,       a_							: List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,       a_							: Optional[Any]=False       ,       a_							: int=1_28       ,       a_							: List[str]=16       ,       a_							: List[str]=True       ,       a_							: Any=0.0_5       ,       a_							: List[str]=10       ,       a_							: Dict=2       ,       a_							: str=0.0       ,       a_							: int=10       ,       a_							: List[str]=0       ,       a_							: Dict="mean"       ,       a_							: List[str]=False       ,       a_							: Optional[Any]=False       ,       a_							: Optional[int]=2_56       ,       a_							: Tuple=0       ,       a_							: List[str]=1       ,       a_							: Any=2       ,       **a_							: Optional[Any]       ,       ):
					'''simple docstring'''





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

					# ctc loss
					__UpperCAmelCase    :     List[Any]													=	ctc_loss_reduction
					__UpperCAmelCase    :     Optional[Any]													=	ctc_zero_infinity

					# sequence classification
					__UpperCAmelCase    :     Optional[int]													=	use_weighted_layer_sum
					__UpperCAmelCase    :     Any													=	classifier_proj_size




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





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










def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     str													=	len(_UpperCAmelCase						)

		for i in range(n - 1						):
				for j in range(i + 1 ,   _UpperCAmelCase						):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) <= 1:
				return arr, 0
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						) // 2
		__UpperCAmelCase    :     Union[str, Any]													=	arr[0:mid]
		__UpperCAmelCase    :     Optional[Any]													=	arr[mid:]

		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	_count_cross_inversions(_UpperCAmelCase ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	inversion_p + inversions_q + cross_inversions
		return c, num_inversions

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Any													=	0
		while i < len(_UpperCAmelCase						) and j < len(_UpperCAmelCase						):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(_UpperCAmelCase						) - i
						r.append(q[j]						)
						j += 1
				else:
						r.append(p[i]						)
						i += 1

		if i < len(_UpperCAmelCase						):
				r.extend(p[i:]						)
		else:
				r.extend(q[j:]						)

		return r, num_inversion

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[10, 2, 1, 5, 5, 2, 11]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		__UpperCAmelCase    :     List[str]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		__UpperCAmelCase    :     Any													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# an empty list should also have zero inversions
		__UpperCAmelCase    :     Tuple													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





