


import sys
from collections import defaultdict







class 	snake_case_       :
					def __init__(				self       :Union[str, Any]				) ->   str:
												a__					=					[]
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :str				) ->   Any:
												return self.node_position[vertex]
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :Any      ,__snake_case       :str				) ->   Tuple:
												a__					=					pos
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :str      ,__snake_case       :List[Any]      ,__snake_case       :int      ,__snake_case       :Union[str, Any]				) ->   Optional[int]:
												if start > size // 2 - 1:
																			return
												else:
																			if 2 * start + 2 >= size:
																										a__					=					2 * start + 1
																			else:
																										if heap[2 * start + 1] < heap[2 * start + 2]:
																																	a__					=					2 * start + 1
																										else:
																																	a__					=					2 * start + 2
																			if heap[smallest_child] < heap[start]:
																										a__				, a__					=					heap[smallest_child], positions[smallest_child]
																										a__				, a__					=					(
																										    heap[start],
																										    positions[start],
																										)
																										a__				, a__					=					temp, tempa

																										a__					=					self.get_position(positions[smallest_child]				)
																										self.set_position(
																										    positions[smallest_child]      ,self.get_position(positions[start]				)				)
																										self.set_position(positions[start]      ,__snake_case				)

																										self.top_to_bottom(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Dict      ,__snake_case       :List[Any]      ,__snake_case       :List[Any]      ,__snake_case       :Union[str, Any]				) ->   Optional[int]:
												a__					=					position[index]

												while index != 0:
																			a__					=					int((index - 2) / 2				) if index % 2 == 0 else int((index - 1) / 2				)

																			if val < heap[parent]:
																										a__					=					heap[parent]
																										a__					=					position[parent]
																										self.set_position(position[parent]      ,__snake_case				)
																			else:
																										a__					=					val
																										a__					=					temp
																										self.set_position(__snake_case      ,__snake_case				)
																										break
																			a__					=					parent
												else:
																			a__					=					val
																			a__					=					temp
																			self.set_position(__snake_case      ,0				)
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :Dict      ,__snake_case       :List[str]				) ->   Optional[int]:
												a__					=					len(__snake_case				) // 2 - 1
												for i in range(__snake_case      ,-1      ,-1				):
																			self.top_to_bottom(__snake_case      ,__snake_case      ,len(__snake_case				)      ,__snake_case				)



					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :List[Any]				) ->   Optional[Any]:
												a__					=					positions[0]
												a__					=					sys.maxsize
												self.top_to_bottom(__snake_case      ,0      ,len(__snake_case				)      ,__snake_case				)
												return temp






def 							__lowercase       (			__lowerCAmelCase							:    Dict       ):
							a__					=					Heap()

							a__					=					[0] * len(__lowerCAmelCase       )
							a__					=					[-1] * len(__lowerCAmelCase       )  # Neighboring Tree Vertex of selected vertex
							# Minimum Distance of explored vertex with neighboring vertex of partial tree
							# formed in graph
							a__					=					[]  # Heap of Distance of vertices from their neighboring vertex
							a__					=					[]

							for vertex in range(len(__lowerCAmelCase       )       ):
														distance_tv.append(sys.maxsize       )
														positions.append(__lowerCAmelCase       )
														heap.node_position.append(__lowerCAmelCase       )

							a__					=					[]
							a__					=					1
							a__					=					sys.maxsize
							for neighbor, distance in adjacency_list[0]:
														a__					=					0
														a__					=					distance
							heap.heapify(__lowerCAmelCase ,   __lowerCAmelCase       )

							for _ in range(1 ,   len(__lowerCAmelCase       )       ):
														a__					=					heap.delete_minimum(__lowerCAmelCase ,   __lowerCAmelCase       )
														if visited[vertex] == 0:
																					tree_edges.append((nbr_tv[vertex], vertex)       )
																					a__					=					1
																					for neighbor, distance in adjacency_list[vertex]:
																												if (
																												    visited[neighbor] == 0
																												    and distance < distance_tv[heap.get_position(__lowerCAmelCase       )]
																												):
																																			a__					=					distance
																																			heap.bottom_to_top(
																																			    __lowerCAmelCase ,   heap.get_position(__lowerCAmelCase       ) ,   __lowerCAmelCase ,   __lowerCAmelCase       )
																																			a__					=					vertex
							return tree_edges


if __name__ == "__main__":  # pragma: no cover
						# < --------- Prims Algorithm --------- >
						snake_case							:	Union[str, Any]					      =				int(input('''Enter number of edges: ''').strip())
						snake_case							:	Dict					      =				defaultdict(list)
						for _ in range(edges_number):
												snake_case							:	str					      =				[int(x) for x in input().strip().split()]
												adjacency_list[edge[0]].append([edge[1], edge[2]])
												adjacency_list[edge[1]].append([edge[0], edge[2]])
						print(prisms_algorithm(adjacency_list))








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()
snake_case							:	str					      =				logging.get_logger(__name__)

snake_case							:	Optional[Any]					      =				{
    '''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''',
}
snake_case							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

							if weight_type is not None:
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       ).shape
							else:
														a__					=					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":
														a__					=					value
							elif weight_type == "weight_g":
														a__					=					value
							elif weight_type == "weight_v":
														a__					=					value
							elif weight_type == "bias":
														a__					=					value
							else:
														a__					=					value

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






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					[]
							a__					=					fairseq_model.state_dict()

							a__					=					hf_model.feature_extractor

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

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

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






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					full_name.split('conv_layers.'       )[-1]
							a__					=					name.split('.'       )
							a__					=					int(items[0]       )
							a__					=					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.'
																					)
																					a__					=					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.'
																					)
																					a__					=					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."
																					)
																					a__					=					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.'
																					)
																					a__					=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
							else:
														unused_weights.append(__lowerCAmelCase       )






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														a__					=					WavLMConfig.from_pretrained(__lowerCAmelCase       )
							else:
														a__					=					WavLMConfig()

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Union[str, Any]					      =				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''')
						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




