"""simple docstring"""



def  _A(		lowerCAmelCase							):
							A__       :     Optional[Any]								=			0
							A__       :     str								=			len(lowerCAmelCase							)

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

							return num_inversions

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) <= 1:
														return arr, 0
							A__       :     List[str]								=			len(lowerCAmelCase							) // 2
							A__       :     List[Any]								=			arr[0:mid]
							A__       :     Any								=			arr[mid:]

							A__				,							A__       :     Any								=			count_inversions_recursive(lowerCAmelCase							)
							A__				,							A__       :     Dict								=			count_inversions_recursive(lowerCAmelCase							)
							A__				,							A__       :     Tuple								=			_count_cross_inversions(lowerCAmelCase			,  lowerCAmelCase							)

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

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Union[str, Any]								=			[]
							A__       :     str								=			0
							while i < len(lowerCAmelCase							) and j < len(lowerCAmelCase							):
														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(lowerCAmelCase							) - i
																					r.append(q[j]							)
																					j += 1
														else:
																					r.append(p[i]							)
																					i += 1

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

							return r, num_inversion

def  _A(		):
							A__       :     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)

							A__       :     Optional[int]								=			count_inversions_bf(lowerCAmelCase							)
							A__				,							A__       :     Optional[Any]								=			count_inversions_recursive(lowerCAmelCase							)

							assert num_inversions_bf == num_inversions_recursive == 8

							print("""number of inversions = """			,  lowerCAmelCase							)

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

							arr_a.sort()
							A__       :     List[str]								=			count_inversions_bf(lowerCAmelCase							)
							A__				,							A__       :     List[str]								=			count_inversions_recursive(lowerCAmelCase							)

							assert num_inversions_bf == num_inversions_recursive == 0
							print("""number of inversions = """			,  lowerCAmelCase							)

							# an empty list should also have zero inversions
							A__       :     Dict								=			[]
							A__       :     Optional[Any]								=			count_inversions_bf(lowerCAmelCase							)
							A__				,							A__       :     int								=			count_inversions_recursive(lowerCAmelCase							)

							assert num_inversions_bf == num_inversions_recursive == 0
							print("""number of inversions = """			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()






"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "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    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





