




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase__					:       List[Any]		=       logging.get_logger(__name__)

lowerCAmelCase__					:       Tuple		=       ['''UtteranceLevel''']
@torch.no_grad()
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> Optional[Any]:
				snake_case__ :     Union[str, Any] 							=				torch.load(lowercase__      ,			map_location='cpu'   )
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""   )

				snake_case__ :     int 							=				checkpoint['Downstream']

				snake_case__ :     Dict 							=				HubertConfig.from_pretrained(lowercase__   )
				snake_case__ :     List[str] 							=				HubertForSequenceClassification.from_pretrained(lowercase__      ,			config=lowercase__   )
				snake_case__ :     Tuple 							=				WavaVecaFeatureExtractor.from_pretrained(
				    lowercase__      ,			return_attention_mask=lowercase__      ,			do_normalize=lowercase__   )

				if hf_congfig.use_weighted_layer_sum:
								snake_case__ :     Union[str, Any] 							=				checkpoint['Featurizer']['weights']

				snake_case__ :     Union[str, Any] 							=				downstream_dict['projector.weight']
				snake_case__ :     List[str] 							=				downstream_dict['projector.bias']
				snake_case__ :     Union[str, Any] 							=				downstream_dict['model.post_net.linear.weight']
				snake_case__ :     Any 							=				downstream_dict['model.post_net.linear.bias']

				hf_feature_extractor.save_pretrained(lowercase__   )
				hf_model.save_pretrained(lowercase__   )


if __name__ == "__main__":
				lowerCAmelCase__					:       str		=       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.''')
				lowerCAmelCase__					:       Union[str, Any]		=       parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowerCAmelCase__					:       Dict		=       {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 			UpperCamelCase__						(    A__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

				while len(A__   ) > 2:
								# Choose a random edge.
								snake_case__ :     Union[str, Any] 							=				random.choice(list(graph_copy.keys()   )   )
								snake_case__ :     Any 							=				random.choice(graph_copy[u]   )

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

								# Remove nodes u and v.
								del graph_copy[u]
								del graph_copy[v]
								for neighbor in uv_neighbors:
												if u in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )

    # Find cutset.
				snake_case__ :     Tuple 							=				[contracted_nodes[node] for node in graph_copy]
				return {
				    (node, neighbor)
				    for node in groups[0]
				    for neighbor in graph[node]
				    if neighbor in groups[1]
				}


if __name__ == "__main__":
				print(partition_graph(TEST_GRAPH))


