





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase_									=       2
class      UpperCamelCase_						:




					def __init__(						self			,  *,  # begin keyword-only arguments
					    lowerCAmelCase_="<s>"			,  lowerCAmelCase_="<pad>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="<unk>"			,  lowerCAmelCase_=None			,  )						->							Tuple:
										_snake_case							,       _snake_case							,       _snake_case							,       _snake_case													=			bos, unk, pad, eos
										_snake_case													=			[]
										_snake_case													=			[]
										_snake_case													=			{}
										_snake_case													=			self.add_symbol(__lowerCAmelCase					)
										_snake_case													=			self.add_symbol(__lowerCAmelCase					)
										_snake_case													=			self.add_symbol(__lowerCAmelCase					)
										_snake_case													=			self.add_symbol(__lowerCAmelCase					)
										if extra_special_symbols:
															for s in extra_special_symbols:
																				self.add_symbol(__lowerCAmelCase					)
										_snake_case													=			len(self.symbols					)




					def __eq__(						self			,  lowerCAmelCase_					)						->							Tuple:
										return self.indices == other.indices




					def __getitem__(						self			,  lowerCAmelCase_					)						->							List[Any]:
										if idx < len(self.symbols					):
															return self.symbols[idx]
										return self.unk_word




					def __len__(						self					)						->							Tuple:
										return len(self.symbols					)




					def __contains__(						self			,  lowerCAmelCase_					)						->							int:
										return sym in self.indices




					@classmethod
					def 		lowerCAmelCase     (						cls			,  lowerCAmelCase_					)						->							int:
										_snake_case													=			cls()
										d.add_from_file(__lowerCAmelCase					)
										return d




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1			,  lowerCAmelCase_=False					)						->							List[Any]:
										if word in self.indices and not overwrite:
															_snake_case													=			self.indices[word]
															_snake_case													=			self.count[idx] + n
															return idx
										else:
															_snake_case													=			len(self.symbols					)
															_snake_case													=			idx
															self.symbols.append(__lowerCAmelCase					)
															self.count.append(__lowerCAmelCase					)
															return idx




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										return 0






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[str]:
										if isinstance(__lowerCAmelCase			,  __lowerCAmelCase					):
															try:
																				with open(__lowerCAmelCase			,  'r'			,  encoding='utf-8'					) as fd:
																									self.add_from_file(__lowerCAmelCase					)
															except FileNotFoundError as fnfe:
																				raise fnfe
															except UnicodeError:
																				raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(__lowerCAmelCase					)					)
															return

										_snake_case													=			f.readlines()
										_snake_case													=			self._load_meta(__lowerCAmelCase					)

										for line in lines[indices_start_line:]:
															try:
																				_snake_case							,       _snake_case													=			line.rstrip().rsplit(' '			,  1					)
																				if field == "#fairseq:overwrite":
																									_snake_case													=			True
																									_snake_case							,       _snake_case													=			line.rsplit(' '			,  1					)
																				else:
																									_snake_case													=			False
																				_snake_case													=			int(__lowerCAmelCase					)
																				_snake_case													=			line
																				if word in self and not overwrite:
																									raise RuntimeError(
																									    'Duplicate word found when loading Dictionary: \'{}\'. '
																									    'Duplicate words can overwrite earlier ones by adding the '
																									    '#fairseq:overwrite flag at the end of the corresponding row '
																									    'in the dictionary file. If using the Camembert model, please '
																									    'download an updated copy of the model file.'.format(__lowerCAmelCase					)					)
																				self.add_symbol(__lowerCAmelCase			,  n=__lowerCAmelCase			,  overwrite=__lowerCAmelCase					)
															except ValueError:
																				raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''					)





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Any:



					'''simple docstring'''




					_snake_case													=			dict((re.sub(R'@@$'    ,      ''    ,      UpperCamelCase__  ), v) if k.endswith('@@'  ) else (re.sub(R'$'    ,      '</w>'    ,      UpperCamelCase__  ), v) for k, v in d.items()  )
					_snake_case													=			'<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F'''{k}</w>''']
										_snake_case													=			d[k]  # restore
					return da





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    List[Any]  )     ->	Optional[Any]:



					'''simple docstring'''




					if not os.path.exists(UpperCamelCase__  ):
										raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''  )
					os.makedirs(UpperCamelCase__    ,      exist_ok=UpperCamelCase__  )
					print(F'''Writing results to {pytorch_dump_folder_path}'''  )

					# handle various types of models

					_snake_case													=			os.path.join(UpperCamelCase__    ,      'checkpoint.pt'  )
					if not os.path.isfile(UpperCamelCase__  ):
										raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''  )
					_snake_case													=			torch.load(UpperCamelCase__    ,      map_location='cpu'  )

					_snake_case													=			chkpt['cfg']['model']

					# dicts
					_snake_case													=			os.path.join(UpperCamelCase__    ,      'dict.txt'  )
					if not os.path.isfile(UpperCamelCase__  ):
										raise ValueError(F'''path to the file {dict_file} does not exist!'''  )
					_snake_case													=			Dictionary.load(UpperCamelCase__  )
					_snake_case													=			rewrite_dict_keys(src_dict.indices  )
					_snake_case													=			len(UpperCamelCase__  )
					_snake_case													=			os.path.join(UpperCamelCase__    ,      VOCAB_FILES_NAMES['vocab_file']  )
					print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''  )
					with open(UpperCamelCase__    ,      'w'    ,      encoding='utf-8'  ) as f:
										f.write(json.dumps(UpperCamelCase__    ,      ensure_ascii=UpperCamelCase__    ,      indent=UpperCamelCase__  )  )

					# merges_file (bpecodes)
					_snake_case													=			os.path.join(UpperCamelCase__    ,      'bpecodes'  )
					if not os.path.isfile(UpperCamelCase__  ):
										raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''  )

					_snake_case													=			os.path.join(UpperCamelCase__    ,      VOCAB_FILES_NAMES['merges_file']  )
					shutil.copyfile(UpperCamelCase__    ,      UpperCamelCase__  )

					# model config
					_snake_case													=			os.path.join(UpperCamelCase__    ,      'config.json'  )

					_snake_case													=			{
					    'activation_dropout': args['activation_dropout'],
					    'architectures': ['BioGptForCausalLM'],
					    'attention_probs_dropout_prob': args['attention_dropout'],
					    'bos_token_id': 0,
					    'eos_token_id': 2,
					    'hidden_act': args['activation_fn'],
					    'hidden_dropout_prob': args['dropout'],
					    'hidden_size': args['decoder_embed_dim'],
					    'initializer_range': 0.02,
					    'intermediate_size': args['decoder_ffn_embed_dim'],
					    'layer_norm_eps': 1e-12,
					    'layerdrop': args['decoder_layerdrop'],
					    'max_position_embeddings': args['max_target_positions'],
					    'model_type': 'biogpt',
					    'num_attention_heads': args['decoder_attention_heads'],
					    'num_hidden_layers': args['decoder_layers'],
					    'pad_token_id': 1,
					    'scale_embedding': not args['no_scale_embedding'],
					    'tie_word_embeddings': args['share_decoder_input_output_embed'],
					    'vocab_size': src_vocab_size,
					}

					# good hparam defaults to start with

					print(F'''Generating {biogpt_model_config_file}'''  )
					with open(UpperCamelCase__    ,      'w'    ,      encoding='utf-8'  ) as f:
										f.write(json.dumps(UpperCamelCase__    ,      ensure_ascii=UpperCamelCase__    ,      indent=UpperCamelCase__  )  )

					# tokenizer config
					_snake_case													=			os.path.join(UpperCamelCase__    ,      UpperCamelCase__  )

					_snake_case													=			{
					    'bos_token': '<s>',
					    'eos_token': '</s>',
					    'model_max_length': 1_024,
					    'pad_token': '<pad>',
					    'special_tokens_map_file': None,
					    'tokenizer_class': 'BioGptTokenizer',
					    'unk_token': '<unk>',
					}

					print(F'''Generating {biogpt_tokenizer_config_file}'''  )
					with open(UpperCamelCase__    ,      'w'    ,      encoding='utf-8'  ) as f:
										f.write(json.dumps(UpperCamelCase__    ,      ensure_ascii=UpperCamelCase__    ,      indent=UpperCamelCase__  )  )

					# model
					_snake_case													=			chkpt['model']

					# remove unneeded keys
					_snake_case													=			[
					    'decoder.version',
					]
					for k in ignore_keys:
										model_state_dict.pop(UpperCamelCase__    ,      UpperCamelCase__  )

					_snake_case													=			list(model_state_dict.keys()  )
					for layer_name in layer_names:
										if layer_name.endswith('output_projection.weight'  ):
															_snake_case													=			model_state_dict.pop(UpperCamelCase__  )
										else:
															_snake_case													=			model_state_dict.pop(UpperCamelCase__  )

					_snake_case													=			BioGptConfig.from_pretrained(UpperCamelCase__  )
					_snake_case													=			BioGptForCausalLM(UpperCamelCase__  )

					# check that it loads ok
					model_new.load_state_dict(UpperCamelCase__  )

					# save
					_snake_case													=			os.path.join(UpperCamelCase__    ,      UpperCamelCase__  )
					print(F'''Generating {pytorch_weights_dump_path}'''  )
					torch.save(UpperCamelCase__    ,      UpperCamelCase__  )

					print('Conversion is done!'  )


if __name__ == "__main__":
					UpperCAmelCase_									=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
					        """ bpecodes, etc."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCAmelCase_									=       parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



