
'''simple docstring'''





class 						a   :




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str = "",    SCREAMING_SNAKE_CASE_	:				bool = False      ):
										# Mapping from the first character of the prefix of the node
										snake_case       :     dict[str, RadixNode] =    {}

										# A node will be a leaf if the tree contains its word
										snake_case       :     List[str] =    is_leaf

										snake_case       :     List[Any] =    prefix




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     Union[str, Any] =    0
										for q, w in zip(self.prefix,    SCREAMING_SNAKE_CASE_      ):
																if q != w:
																						break

																x += 1

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




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				list[str]      ):
										for word in words:
																self.insert(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str      ):
										# Case 1: If the word is the prefix of the node
										# Solution: We set the current node as leaf
										if self.prefix == word:
																snake_case       :     Union[str, Any] =    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:
																snake_case       :     Optional[Any] =    RadixNode(prefix=SCREAMING_SNAKE_CASE_,    is_leaf=SCREAMING_SNAKE_CASE_      )

										else:
																snake_case       :     Optional[int] =    self.nodes[word[0]]
																snake_case,					snake_case,					snake_case       :     List[str] =    incoming_node.match(
																    SCREAMING_SNAKE_CASE_      )

																# 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(SCREAMING_SNAKE_CASE_      )

																# 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:
																						snake_case       :     List[Any] =    remaining_prefix

																						snake_case       :     Dict =    self.nodes[matching_string[0]]
																						snake_case       :     Optional[int] =    RadixNode(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																						snake_case       :     List[Any] =    aux_node

																						if remaining_word == "":
																												snake_case       :     Union[str, Any] =    True
																						else:
																												self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     Optional[Any] =    self.nodes.get(word[0],    SCREAMING_SNAKE_CASE_      )
										if not incoming_node:
																return False
										else:
																snake_case,					snake_case,					snake_case       :     Tuple =    incoming_node.match(
																    SCREAMING_SNAKE_CASE_      )
																# 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(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case       :     str =    self.nodes.get(word[0],    SCREAMING_SNAKE_CASE_      )
										if not incoming_node:
																return False
										else:
																snake_case,					snake_case,					snake_case       :     Optional[Any] =    incoming_node.match(
																    SCREAMING_SNAKE_CASE_      )
																# 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(SCREAMING_SNAKE_CASE_      )
																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:
																																								snake_case       :     Union[str, Any] =    list(self.nodes.values()      )[0]
																																								snake_case       :     Optional[int] =    merging_node.is_leaf
																																								self.prefix += merging_node.prefix
																																								snake_case       :     List[str] =    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																												elif len(incoming_node.nodes      ) > 1:
																																		snake_case       :     Tuple =    False
																												# If there is 1 edge, we merge it with its child
																												else:
																																		snake_case       :     Optional[Any] =    list(incoming_node.nodes.values()      )[0]
																																		snake_case       :     List[Any] =    merging_node.is_leaf
																																		incoming_node.prefix += merging_node.prefix
																																		snake_case       :     int =    merging_node.nodes

																												return True




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int = 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      A (       ):
						snake_case       :     int =    '''banana bananas bandana band apple all beast'''.split()
						snake_case       :     List[str] =    RadixNode()
						root.insert_many(A_       )

						assert all(root.find(A_       ) 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      A (       ):
						assert test_trie()

def      A (       ):
						snake_case       :     Any =    RadixNode()
						snake_case       :     Tuple =    '''banana bananas bandanas bandana band apple all beast'''.split()
						root.insert_many(A_       )

						print('''Words:'''    ,							A_       )
						print('''Tree:'''       )
						root.print_tree()


if __name__ == "__main__":
						main()



'''simple docstring'''





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase    					=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def      A (       A_							:					Dict    ,							A_							:					Any    ,							A_							:					List[str]    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						for attribute in key.split('''.'''       ):
												snake_case       :     Tuple =    getattr(A_    ,							A_       )

						if weight_type is not None:
												snake_case       :     Optional[Any] =    getattr(A_    ,							A_       ).shape
						else:
												snake_case       :     Any =    hf_pointer.shape

						assert hf_shape == value.shape, (
						    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""
						)

						if weight_type == "weight":
												snake_case       :     Optional[Any] =    value
						elif weight_type == "weight_g":
												snake_case       :     Any =    value
						elif weight_type == "weight_v":
												snake_case       :     int =    value
						elif weight_type == "bias":
												snake_case       :     Any =    value
						else:
												snake_case       :     Optional[int] =    value

						logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""       )

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     Dict =    []
						snake_case       :     Optional[Any] =    fairseq_model.state_dict()

						snake_case       :     int =    hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case       :     List[Any] =    False
												if "conv_layers" in name:
																		load_conv_layer(
																		    A_    ,							A_    ,							A_    ,							A_    ,							hf_model.config.feat_extract_norm == '''group'''    ,							)
																		snake_case       :     int =    True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
																														snake_case       :     Optional[int] =    True
																														if "*" in mapped_key:
																																				snake_case       :     Dict =    name.split(A_       )[0].split('''.'''       )[-2]
																																				snake_case       :     List[str] =    mapped_key.replace('''*'''    ,							A_       )
																														if "weight_g" in name:
																																				snake_case       :     Optional[Any] =    '''weight_g'''
																														elif "weight_v" in name:
																																				snake_case       :     Optional[Any] =    '''weight_v'''
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				snake_case       :     Any =    '''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				snake_case       :     Optional[int] =    '''weight'''
																														else:
																																				snake_case       :     Union[str, Any] =    None

																														set_recursively(A_    ,							A_    ,							A_    ,							A_    ,							A_       )
																								continue
												if not is_used:
																		unused_weights.append(A_       )

						logger.warning(F"""Unused weights: {unused_weights}"""       )

def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					int    ,							A_							:					Dict    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[str] =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     List[Any] =    name.split('''.'''       )
						snake_case       :     Dict =    int(items[0]       )
						snake_case       :     Any =    int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		snake_case       :     Tuple =    value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		snake_case       :     Dict =    value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		snake_case       :     str =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		snake_case       :     Tuple =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(A_       )

@torch.no_grad()
def      A (       A_							:					Tuple    ,							A_							:					Any    ,							A_							:					Optional[Any]=None       ):
						# load the pre-trained checkpoints
						snake_case       :     Any =    torch.load(A_       )
						snake_case       :     List[str] =    WavLMConfigOrig(checkpoint['''cfg''']       )
						snake_case       :     Union[str, Any] =    WavLMOrig(A_       )
						model.load_state_dict(checkpoint['''model''']       )
						model.eval()

						if config_path is not None:
												snake_case       :     Union[str, Any] =    WavLMConfig.from_pretrained(A_       )
						else:
												snake_case       :     Any =    WavLMConfig()

						snake_case       :     Optional[int] =    WavLMModel(A_       )

						recursively_load_weights(A_    ,							A_       )

						hf_wavlm.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						UpperCAmelCase    					=   parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

