
'''simple docstring'''





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 						a   :




				def __init__(							self	:				Union[str, Any],    *,  # begin keyword-only arguments
				    SCREAMING_SNAKE_CASE_	:				List[Any]="<s>",    SCREAMING_SNAKE_CASE_	:				List[str]="<pad>",    SCREAMING_SNAKE_CASE_	:				Any="</s>",    SCREAMING_SNAKE_CASE_	:				List[str]="<unk>",    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    ):
										snake_case,					snake_case,					snake_case,					snake_case       :     Dict =    bos, unk, pad, eos
										snake_case       :     int =    []
										snake_case       :     Optional[int] =    []
										snake_case       :     Optional[Any] =    {}
										snake_case       :     Optional[int] =    self.add_symbol(SCREAMING_SNAKE_CASE_      )
										snake_case       :     int =    self.add_symbol(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    self.add_symbol(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    self.add_symbol(SCREAMING_SNAKE_CASE_      )
										if extra_special_symbols:
																for s in extra_special_symbols:
																						self.add_symbol(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Union[str, Any] =    len(self.symbols      )




				def __eq__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.indices == other.indices




				def __getitem__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										if idx < len(self.symbols      ):
																return self.symbols[idx]
										return self.unk_word




				def __len__(							self	:				Any      ):
										return len(self.symbols      )




				def __contains__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return sym in self.indices




				@classmethod
				def 	__snake_case							(							cls	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     int =    cls()
										d.add_from_file(SCREAMING_SNAKE_CASE_      )
										return d




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str=1,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False      ):
										if word in self.indices and not overwrite:
																snake_case       :     Any =    self.indices[word]
																snake_case       :     Tuple =    self.count[idx] + n
																return idx
										else:
																snake_case       :     Any =    len(self.symbols      )
																snake_case       :     Tuple =    idx
																self.symbols.append(SCREAMING_SNAKE_CASE_      )
																self.count.append(SCREAMING_SNAKE_CASE_      )
																return idx




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										return 0




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

										snake_case       :     int =    f.readlines()
										snake_case       :     int =    self._load_meta(SCREAMING_SNAKE_CASE_      )

										for line in lines[indices_start_line:]:
																try:
																						snake_case,					snake_case       :     Tuple =    line.rstrip().rsplit(''' ''',    1      )
																						if field == "#fairseq:overwrite":
																												snake_case       :     Dict =    True
																												snake_case,					snake_case       :     int =    line.rsplit(''' ''',    1      )
																						else:
																												snake_case       :     str =    False
																						snake_case       :     int =    int(SCREAMING_SNAKE_CASE_      )
																						snake_case       :     List[Any] =    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(SCREAMING_SNAKE_CASE_      )      )
																						self.add_symbol(SCREAMING_SNAKE_CASE_,    n=SCREAMING_SNAKE_CASE_,    overwrite=SCREAMING_SNAKE_CASE_      )
																except ValueError:
																						raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''      )




def      A (       A_							:					Tuple       ):
						# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
						# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
						snake_case       :     List[Any] =    dict((re.sub(R'''@@$'''    ,							''''''    ,							A_       ), v) if k.endswith('''@@'''       ) else (re.sub(R'''$'''    ,							'''</w>'''    ,							A_       ), v) for k, v in d.items()       )
						snake_case       :     Optional[Any] =    '''<s> <pad> </s> <unk>'''.split()
						# restore the special tokens
						for k in keep_keys:
												del da[F"""{k}</w>"""]
												snake_case       :     Tuple =    d[k]  # restore
						return da

def      A (       A_							:					Any    ,							A_							:					Any       ):
						# prep
						if not os.path.exists(A_       ):
												raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!"""       )
						os.makedirs(A_    ,							exist_ok=A_       )
						print(F"""Writing results to {pytorch_dump_folder_path}"""       )

						# handle various types of models

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

						snake_case       :     Any =    chkpt['''cfg''']['''model''']

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

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

						snake_case       :     str =    os.path.join(A_    ,							VOCAB_FILES_NAMES['''merges_file''']       )
						shutil.copyfile(A_    ,							A_       )

						# model config
						snake_case       :     Union[str, Any] =    os.path.join(A_    ,							'''config.json'''       )

						snake_case       :     str =    {
						    '''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(A_    ,							'''w'''    ,							encoding='''utf-8'''       ) as f:
												f.write(json.dumps(A_    ,							ensure_ascii=A_    ,							indent=A_       )       )

						# tokenizer config
						snake_case       :     Optional[int] =    os.path.join(A_    ,							A_       )

						snake_case       :     List[Any] =    {
						    '''bos_token''': '''<s>''',
						    '''eos_token''': '''</s>''',
						    '''model_max_length''': 1024,
						    '''pad_token''': '''<pad>''',
						    '''special_tokens_map_file''': None,
						    '''tokenizer_class''': '''BioGptTokenizer''',
						    '''unk_token''': '''<unk>''',
						}

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

						# model
						snake_case       :     Union[str, Any] =    chkpt['''model''']

						# remove unneeded keys
						snake_case       :     Optional[int] =    [
						    '''decoder.version''',
						]
						for k in ignore_keys:
												model_state_dict.pop(A_    ,							A_       )

						snake_case       :     Union[str, Any] =    list(model_state_dict.keys()       )
						for layer_name in layer_names:
												if layer_name.endswith('''output_projection.weight'''       ):
																		snake_case       :     Optional[int] =    model_state_dict.pop(A_       )
												else:
																		snake_case       :     Optional[int] =    model_state_dict.pop(A_       )

						snake_case       :     Dict =    BioGptConfig.from_pretrained(A_       )
						snake_case       :     Optional[Any] =    BioGptForCausalLM(A_       )

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

						# save
						snake_case       :     Any =    os.path.join(A_    ,							A_       )
						print(F"""Generating {pytorch_weights_dump_path}"""       )
						torch.save(A_    ,							A_       )

						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)



'''simple docstring'''





from __future__ import annotations

def      A (       A_							:					list[int]    ,							A_							:					int       ):
						snake_case       :     list[list[int]] =    []
						snake_case       :     list[int] =    []
						snake_case       :     int =    0
						snake_case       :     int =    sum(A_       )
						create_state_space_tree(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
						return result

def      A (       A_							:					list[int]    ,							A_							:					int    ,							A_							:					int    ,							A_							:					list[int]    ,							A_							:					list[list[int]]    ,							A_							:					int    ,							):
						if sum(A_       ) > max_sum or (remaining_nums_sum + sum(A_       )) < max_sum:
												return
						if sum(A_       ) == max_sum:
												result.append(A_       )
												return
						for index in range(A_    ,							len(A_       )       ):
												create_state_space_tree(
												    A_    ,							A_    ,							index + 1    ,							[*path, nums[index]]    ,							A_    ,							remaining_nums_sum - nums[index]    ,							)
UpperCAmelCase    					=   [3, 34, 4, 12, 5, 2]
UpperCAmelCase    					=   9
UpperCAmelCase    					=   generate_sum_of_subsets_soln(nums, max_sum)





print(*result)

