
import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  :       Optional[int]					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Optional[int]					      =     [
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

lowerCamelCase_  :       int					      =     {
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

lowerCamelCase_  :       List[str]					      =     list("""ARNDCQEGHILKMFPSTWYV""")

lowerCamelCase_  :       int					      =     restypes + ["""X"""]
lowerCamelCase_  :       str					      =     restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]







def 				A__    (  )     ->		Any:
				with TemporaryDirectory() as tempdir:
								UpperCamelCase_:							Union[str, Any]		=  """\n""".join(lowerCamelCase  )
								UpperCamelCase_:							Optional[Any]		=  Path(lowerCamelCase  ) / """vocab.txt"""
								vocab_file.write_text(lowerCamelCase  )
								UpperCamelCase_:							Optional[Any]		=  EsmTokenizer(vocab_file=str(lowerCamelCase  )  )
				UpperCamelCase_:							str		=  0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				UpperCamelCase_:							int		=  our_module.load_state_dict(original_module.state_dict()  )
				if status.missing_keys:
								raise ValueError(F'''Missing keys: {status.missing_keys}'''  )
				if status.unexpected_keys:
								raise ValueError(F'''Unexpected keys: {status.unexpected_keys}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[str]:
				if model.startswith("""esmfold"""  ):
								UpperCamelCase_:							Dict		=  MODEL_MAPPING[model]()
				else:
								UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

				if model.startswith("""esmfold"""  ):
								UpperCamelCase_:							Optional[Any]		=  esm.esm.embed_dim
								UpperCamelCase_:							Optional[int]		=  esm.esm.num_layers
								UpperCamelCase_:							Tuple		=  esm.esm.attention_heads
								UpperCamelCase_:							int		=  4 * embed_dim
								UpperCamelCase_:							Optional[Any]		=  esm.esm.token_dropout
								UpperCamelCase_:							List[str]		=  False  # This code path does not exist in ESM-2
								UpperCamelCase_:							Any		=  """rotary"""
								UpperCamelCase_:							List[str]		=  True
								UpperCamelCase_:							int		=  EsmFoldConfig()
								for key, val in esm.cfg.items():
												if hasattr(lowerCamelCase   ,			lowerCamelCase  ) and key != "trunk":
																setattr(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )
								for key, val in esm.cfg.trunk.items():
												if hasattr(esmfold_config.trunk   ,			lowerCamelCase  ) and key != "structure_module":
																setattr(esmfold_config.trunk   ,			lowerCamelCase   ,			lowerCamelCase  )
								for key, val in esm.cfg.trunk.structure_module.items():
												if hasattr(esmfold_config.trunk.structure_module   ,			lowerCamelCase  ):
																setattr(esmfold_config.trunk.structure_module   ,			lowerCamelCase   ,			lowerCamelCase  )
				elif hasattr(lowerCamelCase   ,			"""args"""  ):
								# Indicates an ESM-1b or ESM-1v model
								UpperCamelCase_:							Optional[Any]		=  esm.args.embed_dim
								UpperCamelCase_:							List[str]		=  esm.args.layers
								UpperCamelCase_:							Union[str, Any]		=  esm.args.attention_heads
								UpperCamelCase_:							List[str]		=  esm.args.ffn_embed_dim
								UpperCamelCase_:							Any		=  esm.args.token_dropout
								UpperCamelCase_:							List[Any]		=  True if esm.emb_layer_norm_before else False
								UpperCamelCase_:							Union[str, Any]		=  """absolute"""
								UpperCamelCase_:							int		=  False
								UpperCamelCase_:							str		=  None
				else:
								# Indicates an ESM-2 model
								UpperCamelCase_:							Union[str, Any]		=  esm.embed_dim
								UpperCamelCase_:							Dict		=  esm.num_layers
								UpperCamelCase_:							Dict		=  esm.attention_heads
								UpperCamelCase_:							int		=  4 * embed_dim  # This is hardcoded in ESM-2
								UpperCamelCase_:							int		=  esm.token_dropout
								UpperCamelCase_:							Tuple		=  False  # This code path does not exist in ESM-2
								UpperCamelCase_:							Any		=  """rotary"""
								UpperCamelCase_:							List[Any]		=  False
								UpperCamelCase_:							int		=  None

				if is_folding_model:
								UpperCamelCase_:							Tuple		=  esm.esm.alphabet
				UpperCamelCase_:							int		=  tuple(alphabet.all_toks  )
				UpperCamelCase_:							int		=  alphabet.mask_idx
				UpperCamelCase_:							List[Any]		=  alphabet.padding_idx

				if is_folding_model:
								UpperCamelCase_:							Optional[Any]		=  esm.esm
				else:
								UpperCamelCase_:							str		=  esm

				UpperCamelCase_:							int		=  EsmConfig(
				    vocab_size=original_esm_model.embed_tokens.num_embeddings   ,			mask_token_id=lowerCamelCase   ,			hidden_size=lowerCamelCase   ,			num_hidden_layers=lowerCamelCase   ,			num_attention_heads=lowerCamelCase   ,			intermediate_size=lowerCamelCase   ,			max_position_embeddings=10_26   ,			layer_norm_eps=1E-5   ,			attention_probs_dropout_prob=0.0   ,			hidden_dropout_prob=0.0   ,			pad_token_id=lowerCamelCase   ,			emb_layer_norm_before=lowerCamelCase   ,			token_dropout=lowerCamelCase   ,			position_embedding_type=lowerCamelCase   ,			is_folding_model=lowerCamelCase   ,			esmfold_config=lowerCamelCase   ,			vocab_list=lowerCamelCase   ,			)
				if classification_head:
								UpperCamelCase_:							List[str]		=  esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our ESM config:"""   ,			lowerCamelCase  )

				if model.startswith("""esmfold"""  ):
								UpperCamelCase_:							List[str]		=  EsmForProteinFolding
				elif classification_head:
								UpperCamelCase_:							Optional[Any]		=  EsmForSequenceClassification
				else:
								UpperCamelCase_:							int		=  EsmForMaskedLM
				UpperCamelCase_:							int		=  model_class(lowerCamelCase  )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				UpperCamelCase_:							str		=  original_esm_model.embed_tokens.weight
				if position_embedding_type == "absolute":
								UpperCamelCase_:							Dict		=  original_esm_model.embed_positions.weight

				if config.emb_layer_norm_before:
								UpperCamelCase_:							List[Any]		=  original_esm_model.emb_layer_norm_before.weight
								UpperCamelCase_:							Union[str, Any]		=  original_esm_model.emb_layer_norm_before.bias

				UpperCamelCase_:							Dict		=  original_esm_model.emb_layer_norm_after.weight
				UpperCamelCase_:							List[str]		=  original_esm_model.emb_layer_norm_after.bias

				for i in range(config.num_hidden_layers  ):
								# Encoder: start of layer
								UpperCamelCase_:							EsmLayer		=  model.esm.encoder.layer[i]
								# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
								UpperCamelCase_:							List[str]		=  original_esm_model.layers[i]

								# self attention
								UpperCamelCase_:							EsmSelfAttention		=  layer.attention.self
								assert (
								    esm_layer.self_attn.k_proj.weight.data.shape
								    == esm_layer.self_attn.q_proj.weight.data.shape
								    == esm_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)  )
								)

								UpperCamelCase_:							Dict		=  esm_layer.self_attn.q_proj.weight
								UpperCamelCase_:							Dict		=  esm_layer.self_attn.q_proj.bias
								UpperCamelCase_:							Dict		=  esm_layer.self_attn.k_proj.weight
								UpperCamelCase_:							Union[str, Any]		=  esm_layer.self_attn.k_proj.bias
								UpperCamelCase_:							Optional[int]		=  esm_layer.self_attn.v_proj.weight
								UpperCamelCase_:							Tuple		=  esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn   ,			"""rot_emb"""   ,			lowerCamelCase  ) is not None:
												# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
												# During the training of ESM-2 the model was converted to float16 precision, which also converts
												# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
												# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
												# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
												# we make sure the new model copies the data from the old inv_freq.
												UpperCamelCase_:							str		=  esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								UpperCamelCase_:							Optional[int]		=  esm_layer.self_attn_layer_norm.weight
								UpperCamelCase_:							str		=  esm_layer.self_attn_layer_norm.bias
								UpperCamelCase_:							List[Any]		=  esm_layer.final_layer_norm.weight
								UpperCamelCase_:							Union[str, Any]		=  esm_layer.final_layer_norm.bias

								# self-attention output
								UpperCamelCase_:							EsmSelfOutput		=  layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								UpperCamelCase_:							Tuple		=  esm_layer.self_attn.out_proj.weight
								UpperCamelCase_:							Optional[int]		=  esm_layer.self_attn.out_proj.bias

								# intermediate
								UpperCamelCase_:							EsmIntermediate		=  layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								UpperCamelCase_:							int		=  esm_layer.fca.weight
								UpperCamelCase_:							Tuple		=  esm_layer.fca.bias

								# output
								UpperCamelCase_:							EsmOutput		=  layer.output
								assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
								UpperCamelCase_:							List[Any]		=  esm_layer.fca.weight
								UpperCamelCase_:							Tuple		=  esm_layer.fca.bias
								# end of layer

				if is_folding_model:
								UpperCamelCase_:							List[str]		=  esm.esm_s_combine.data
								UpperCamelCase_:							Dict		=  esm.afa_to_esm.data
								transfer_and_check_weights(esm.embedding   ,			model.embedding  )
								transfer_and_check_weights(esm.esm_s_mlp   ,			model.esm_s_mlp  )
								transfer_and_check_weights(esm.trunk   ,			model.trunk  )
								transfer_and_check_weights(esm.distogram_head   ,			model.distogram_head  )
								transfer_and_check_weights(esm.ptm_head   ,			model.ptm_head  )
								transfer_and_check_weights(esm.lm_head   ,			model.lm_head  )
								transfer_and_check_weights(esm.lddt_head   ,			model.lddt_head  )

				elif classification_head:
								UpperCamelCase_:							Dict		=  esm.esm.classification_heads["""mnli"""].dense.weight
								UpperCamelCase_:							int		=  esm.classification_heads["""mnli"""].dense.bias
								UpperCamelCase_:							List[str]		=  esm.classification_heads["""mnli"""].out_proj.weight
								UpperCamelCase_:							List[str]		=  esm.classification_heads["""mnli"""].out_proj.bias
				else:
								# LM Head
								UpperCamelCase_:							int		=  esm.lm_head.dense.weight
								UpperCamelCase_:							str		=  esm.lm_head.dense.bias
								UpperCamelCase_:							str		=  esm.lm_head.layer_norm.weight
								UpperCamelCase_:							str		=  esm.lm_head.layer_norm.bias
								UpperCamelCase_:							Optional[int]		=  esm.lm_head.weight
								UpperCamelCase_:							Optional[int]		=  esm.lm_head.bias

				# Contact prediction head
				transfer_and_check_weights(esm.contact_head   ,			model.esm.contact_head  )

				# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
				if is_folding_model:
								# Folding models aren't trained on masked inputs and don't like mask tokens.
								UpperCamelCase_:							List[Any]		=  SAMPLE_DATA[:2]
				else:
								UpperCamelCase_:							str		=  SAMPLE_DATA

				if is_folding_model:
								UpperCamelCase_:							List[str]		=  get_esmfold_tokenizer()
								UpperCamelCase_:							int		=  hf_tokenizer(
								    [row[1] for row in sample_data]   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase   ,			add_special_tokens=lowerCamelCase  )
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							int		=  esmfold_encode_sequences([row[1] for row in sample_data]  )
								UpperCamelCase_:							Dict		=  torch.all(hf_tokens["""input_ids"""] == esmfold_aas  ) and torch.all(
								    hf_tokens["""attention_mask"""] == esmfold_mask  )
				else:
								# Let's check that we get the same results.
								UpperCamelCase_:							Dict		=  alphabet.get_batch_converter()
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Tuple		=  batch_converter(lowerCamelCase  )
								# Prepare tokenizer and make sure it matches
								with TemporaryDirectory() as tempdir:
												UpperCamelCase_:							List[str]		=  """\n""".join(alphabet.all_toks  )
												UpperCamelCase_:							Union[str, Any]		=  Path(lowerCamelCase  ) / """vocab.txt"""
												vocab_file.write_text(lowerCamelCase  )
												UpperCamelCase_:							str		=  EsmTokenizer(vocab_file=str(lowerCamelCase  )  )

								UpperCamelCase_:							int		=  hf_tokenizer([row[1] for row in sample_data]   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase  )
								UpperCamelCase_:							int		=  torch.all(hf_tokens["""input_ids"""] == batch_tokens  )

				print("""Do both models tokenizers output the same tokens?"""   ,			"""🔥""" if success else """💩"""  )
				if not success:
								raise Exception("""Tokenization does not match!"""  )

				with torch.no_grad():
								if is_folding_model:
												# Let's test the model in parts
												# ESMFold always converts the ESM stem to float16, which requires float16 ops
												# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
												# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
												# original and the converted model on the GPU at the same time.
												UpperCamelCase_:							Union[str, Any]		=  esm.cuda().infer([row[1] for row in sample_data]  )
												UpperCamelCase_:							Dict		=  model.cuda()(
												    input_ids=hf_tokens["""input_ids"""].cuda()   ,			attention_mask=hf_tokens["""attention_mask"""].cuda()  )
								else:
												UpperCamelCase_:							str		=  model(**lowerCamelCase   ,			output_hidden_states=lowerCamelCase  )
												UpperCamelCase_:							Union[str, Any]		=  our_output["""logits"""]
												if classification_head:
																UpperCamelCase_:							Optional[Any]		=  esm.model.classification_heads["""mnli"""](esm.extract_features(lowerCamelCase  )  )
												else:
																UpperCamelCase_:							Tuple		=  esm(hf_tokens["""input_ids"""]   ,			repr_layers=list(range(9_99  )  )  )
																UpperCamelCase_:							Tuple		=  their_output["""logits"""]

								if is_folding_model:
												UpperCamelCase_:							str		=  torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]  )  ).item()
												UpperCamelCase_:							Any		=  torch.allclose(our_output["""positions"""]   ,			their_output["""positions"""]   ,			atol=1E-5  )
								else:
												UpperCamelCase_:							Union[str, Any]		=  torch.max(torch.abs(our_output - their_output  )  ).item()
												UpperCamelCase_:							List[str]		=  torch.allclose(lowerCamelCase   ,			lowerCamelCase   ,			atol=1E-5  )

								print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-5
								print("""Do both models output the same tensors?"""   ,			"""🔥""" if success else """💩"""  )

								if not success:
												raise Exception("""Something went wRoNg"""  )

								if not is_folding_model:
												# Let's check contact prediction too
												UpperCamelCase_:							Dict		=  model.predict_contacts(hf_tokens["""input_ids"""]   ,			hf_tokens["""attention_mask"""]  )
												UpperCamelCase_:							List[str]		=  esm.predict_contacts(hf_tokens["""input_ids"""]  )
												UpperCamelCase_:							Any		=  torch.max(torch.abs(our_output - their_output  )  ).item()
												UpperCamelCase_:							Any		=  torch.allclose(lowerCamelCase   ,			lowerCamelCase   ,			atol=1E-5  )

												print("""Contact prediction testing:"""  )
												print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-5
												print("""Do both models output the same tensors?"""   ,			"""🔥""" if success else """💩"""  )

												if not success:
																raise Exception("""Something went wRoNg"""  )

								pathlib.Path(lowerCamelCase  ).mkdir(parents=lowerCamelCase   ,			exist_ok=lowerCamelCase  )
								print(F'''Saving model to {pytorch_dump_folder_path}'''  )
								model.save_pretrained(lowerCamelCase  )

								del esm  # Free up some memory before continuing

				print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''  )
				hf_tokenizer.save_pretrained(lowerCamelCase  )

				if push_to_repo:
								model.push_to_hub(repo_id=lowerCamelCase   ,			use_auth_token=lowerCamelCase  )
								hf_tokenizer.push_to_hub(repo_id=lowerCamelCase   ,			use_auth_token=lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :       int					      =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--pytorch_dump_folder_path""", type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
							)
							parser.add_argument("""--model""", default=None, type=str, required=True, help="""Name of model to convert.""")
							parser.add_argument("""--push_to_repo""", type=str, help="""Repo to upload to (including username!).""")
							parser.add_argument("""--auth_token""", type=str, help="""HuggingFace auth token.""")
							lowerCamelCase_  :       Dict					      =     parser.parse_args()
							convert_esm_checkpoint_to_pytorch(
							    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
							)











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
__A				    =						{
    '''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        __a     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												UpperCAmelCase_=   u + v
												UpperCAmelCase_=   list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												UpperCAmelCase_=   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(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # Find cutset.
						UpperCAmelCase_=   [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))




