






'''simple docstring'''







import functools
import operator

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


_A     :      Tuple   					=logging.get_logger(__name__)

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

class _lowercase     (    _lowercase		):
							a    				=	"""sew-d"""




							def __init__(							self:    str      ,							UpperCamelCase__:    List[str]=32      ,							UpperCamelCase__:    Union[str, Any]=768      ,							UpperCamelCase__:    List[Any]=12      ,							UpperCamelCase__:    Union[str, Any]=12      ,							UpperCamelCase__:    List[Any]=3_072      ,							UpperCamelCase__:    List[str]=2      ,							UpperCamelCase__:    Optional[int]=512      ,							UpperCamelCase__:    Optional[Any]=256      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Union[str, Any]=("p2c", "c2p")      ,							UpperCamelCase__:    Dict="layer_norm"      ,							UpperCamelCase__:    List[str]="gelu_python"      ,							UpperCamelCase__:    Any=0.1      ,							UpperCamelCase__:    Any=0.1      ,							UpperCamelCase__:    Any=0.1      ,							UpperCamelCase__:    List[Any]=0.0      ,							UpperCamelCase__:    Dict=0.1      ,							UpperCamelCase__:    Tuple=0.02      ,							UpperCamelCase__:    str=1e-7      ,							UpperCamelCase__:    str=1e-5      ,							UpperCamelCase__:    List[str]="group"      ,							UpperCamelCase__:    Optional[Any]="gelu"      ,							UpperCamelCase__:    Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,							UpperCamelCase__:    Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,							UpperCamelCase__:    Optional[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    List[str]=128      ,							UpperCamelCase__:    Union[str, Any]=16      ,							UpperCamelCase__:    Union[str, Any]=True      ,							UpperCamelCase__:    Optional[int]=0.05      ,							UpperCamelCase__:    str=10      ,							UpperCamelCase__:    Union[str, Any]=2      ,							UpperCamelCase__:    Tuple=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    List[Any]=0      ,							UpperCamelCase__:    Tuple="mean"      ,							UpperCamelCase__:    Any=False      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    Any=256      ,							UpperCamelCase__:    Any=0      ,							UpperCamelCase__:    str=1      ,							UpperCamelCase__:    List[Any]=2      ,							**UpperCamelCase__:    str      ,							):
														super().__init__(**UpperCamelCase__      ,							pad_token_id=UpperCamelCase__      ,							bos_token_id=UpperCamelCase__      ,							eos_token_id=UpperCamelCase__					)
														lowerCamelCase__ :     List[str]	  =	hidden_size
														lowerCamelCase__ :     int	  =	feat_extract_norm
														lowerCamelCase__ :     int	  =	feat_extract_activation
														lowerCamelCase__ :     str	  =	list(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	list(UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	list(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	conv_bias
														lowerCamelCase__ :     Optional[Any]	  =	num_conv_pos_embeddings
														lowerCamelCase__ :     Optional[Any]	  =	num_conv_pos_embedding_groups
														lowerCamelCase__ :     int	  =	len(self.conv_dim					)
														lowerCamelCase__ :     List[Any]	  =	num_hidden_layers
														lowerCamelCase__ :     str	  =	intermediate_size
														lowerCamelCase__ :     int	  =	squeeze_factor
														lowerCamelCase__ :     Optional[int]	  =	max_position_embeddings
														lowerCamelCase__ :     Optional[Any]	  =	position_buckets
														lowerCamelCase__ :     Optional[Any]	  =	share_att_key
														lowerCamelCase__ :     str	  =	relative_attention
														lowerCamelCase__ :     Dict	  =	norm_rel_ebd
														lowerCamelCase__ :     Dict	  =	list(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	hidden_act
														lowerCamelCase__ :     Optional[int]	  =	num_attention_heads
														lowerCamelCase__ :     Dict	  =	hidden_dropout
														lowerCamelCase__ :     int	  =	attention_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_dropout
														lowerCamelCase__ :     List[str]	  =	feat_proj_dropout
														lowerCamelCase__ :     Union[str, Any]	  =	final_dropout
														lowerCamelCase__ :     List[Any]	  =	layer_norm_eps
														lowerCamelCase__ :     int	  =	feature_layer_norm_eps
														lowerCamelCase__ :     int	  =	initializer_range
														lowerCamelCase__ :     Tuple	  =	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
														lowerCamelCase__ :     List[Any]	  =	apply_spec_augment
														lowerCamelCase__ :     str	  =	mask_time_prob
														lowerCamelCase__ :     List[str]	  =	mask_time_length
														lowerCamelCase__ :     Any	  =	mask_time_min_masks
														lowerCamelCase__ :     List[Any]	  =	mask_feature_prob
														lowerCamelCase__ :     str	  =	mask_feature_length
														lowerCamelCase__ :     int	  =	mask_feature_min_masks

														# ctc loss
														lowerCamelCase__ :     List[Any]	  =	ctc_loss_reduction
														lowerCamelCase__ :     Dict	  =	ctc_zero_infinity

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




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









'''simple docstring'''







from __future__ import annotations

from typing import Any

class _lowercase     (    _lowercase		):
							pass



class _lowercase     :




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Any	  =	data
														lowerCamelCase__ :     Node | None	  =	None




							def __iter__(							self:    List[Any]					):
														lowerCamelCase__ :     Optional[Any]	  =	self
														lowerCamelCase__ :     int	  =	[]
														while node:
																					if node in visited:
																												raise ContainsLoopError
																					visited.append(UpperCamelCase__					)
																					yield node.data
																					lowerCamelCase__ :     List[str]	  =	node.next_node




							@property
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														try:
																					list(self					)
																					return False
														except ContainsLoopError:
																					return True


if __name__ == "__main__":
			_A     :      Any   					=Node(1)
			_A     :      Optional[int]   					=Node(2)
			_A     :      Dict   					=Node(3)
			_A     :      Optional[Any]   					=Node(4)
			print(root_node.has_loop)  # False
			_A     :      Any   					=root_node.next_node
			print(root_node.has_loop)  # True

			_A     :      Dict   					=Node(5)
			_A     :      Union[str, Any]   					=Node(6)
			_A     :      str   					=Node(5)
			_A     :      int   					=Node(6)
			print(root_node.has_loop)  # False

			_A     :      Optional[Any]   					=Node(1)
			print(root_node.has_loop)  # False

