





class       __A     :


				def __init__(		self   , UpperCAmelCase_ = ""   , UpperCAmelCase_ = False							):
								# Mapping from the first character of the prefix of the node
								lowerCamelCase	 ={}

								# A node will be a leaf if the tree contains its word
								lowerCamelCase	 =is_leaf

								lowerCamelCase	 =prefix


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =0
								for q, w in zip(self.prefix   , UpperCAmelCase_							):
												if q != w:
																break

												x += 1

								return self.prefix[:x], self.prefix[x:], word[x:]


				def _snake_case       (		self   , UpperCAmelCase_							):
								for word in words:
												self.insert(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								# Case 1: If the word is the prefix of the node
								# Solution: We set the current node as leaf
								if self.prefix == word:
												lowerCamelCase	 =True

								# Case 2: The node has no edges that have a prefix to the word
								# Solution: We create an edge from the current node to a new one
								# containing the word
								elif word[0] not in self.nodes:
												lowerCamelCase	 =RadixNode(prefix=UpperCAmelCase_   , is_leaf=UpperCAmelCase_							)

								else:
												lowerCamelCase	 =self.nodes[word[0]]
												lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =incoming_node.match(
												    UpperCAmelCase_							)

												# Case 3: The node prefix is equal to the matching
												# Solution: We insert remaining word on the next node
												if remaining_prefix == "":
																self.nodes[matching_string[0]].insert(UpperCAmelCase_							)

												# Case 4: The word is greater equal to the matching
												# Solution: Create a node in between both nodes, change
												# prefixes and add the new node for the remaining word
												else:
																lowerCamelCase	 =remaining_prefix

																lowerCamelCase	 =self.nodes[matching_string[0]]
																lowerCamelCase	 =RadixNode(UpperCAmelCase_   , UpperCAmelCase_							)
																lowerCamelCase	 =aux_node

																if remaining_word == "":
																				lowerCamelCase	 =True
																else:
																				self.nodes[matching_string[0]].insert(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =self.nodes.get(word[0]   , UpperCAmelCase_							)
								if not incoming_node:
												return False
								else:
												lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =incoming_node.match(
												    UpperCAmelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# This applies when the word and the prefix are equal
												elif remaining_word == "":
																return incoming_node.is_leaf
												# We have word remaining so we check the next node
												else:
																return incoming_node.find(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =self.nodes.get(word[0]   , UpperCAmelCase_							)
								if not incoming_node:
												return False
								else:
												lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =incoming_node.match(
												    UpperCAmelCase_							)
												# If there is remaining prefix, the word can't be on the tree
												if remaining_prefix != "":
																return False
												# We have word remaining so we check the next node
												elif remaining_word != "":
																return incoming_node.delete(UpperCAmelCase_							)
												else:
																# If it is not a leaf, we don't have to delete
																if not incoming_node.is_leaf:
																				return False
																else:
																				# We delete the nodes if no edges go from it
																				if len(incoming_node.nodes							) == 0:
																								del self.nodes[word[0]]
																								# We merge the current node with its only child
																								if len(self.nodes							) == 1 and not self.is_leaf:
																												lowerCamelCase	 =list(self.nodes.values()							)[0]
																												lowerCamelCase	 =merging_node.is_leaf
																												self.prefix += merging_node.prefix
																												lowerCamelCase	 =merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																				elif len(incoming_node.nodes							) > 1:
																								lowerCamelCase	 =False
																				# If there is 1 edge, we merge it with its child
																				else:
																								lowerCamelCase	 =list(incoming_node.nodes.values()							)[0]
																								lowerCamelCase	 =merging_node.is_leaf
																								incoming_node.prefix += merging_node.prefix
																								lowerCamelCase	 =merging_node.nodes

																				return True


				def _snake_case       (		self   , UpperCAmelCase_ = 0							):
								if self.prefix != "":
												print("""-""" * height   , self.prefix   , """  (leaf)""" if self.is_leaf else """"""							)

								for value in self.nodes.values():
												value.print_tree(height + 1							)





def      _lowercase					(						)						->  bool:
				lowerCamelCase	 ="""banana bananas bandana band apple all beast""".split()
				lowerCamelCase	 =RadixNode()
				root.insert_many(_UpperCAmelCase				)

				assert all(root.find(_UpperCAmelCase				) for word in words				)
				assert not root.find("""bandanas"""				)
				assert not root.find("""apps"""				)
				root.delete("""all"""				)
				assert not root.find("""all"""				)
				root.delete("""banana"""				)
				assert not root.find("""banana"""				)
				assert root.find("""bananas"""				)

				return True





def      _lowercase					(						)						->  None:
				assert test_trie()





def      _lowercase					(						)						->  None:
				lowerCamelCase	 =RadixNode()
				lowerCamelCase	 ="""banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(_UpperCAmelCase				)

				print("""Words:"""			,      _UpperCAmelCase				)
				print("""Tree:"""				)
				root.print_tree()


if __name__ == "__main__":
							main()












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





