






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()

A__      :     Union[str, Any]       =					2
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       Optional[Any],    *,  # begin keyword-only arguments
				    lowerCamelCase		:       Optional[int]="<s>",    lowerCamelCase		:       Any="<pad>",    lowerCamelCase		:       Union[str, Any]="</s>",    lowerCamelCase		:       Optional[int]="<unk>",    lowerCamelCase		:       Dict=None,    ):

								'''simple docstring'''
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     bos, unk, pad, eos
								lowercase__  					=     []
								lowercase__  					=     []
								lowercase__  					=     {}
								lowercase__  					=     self.add_symbol(lowerCamelCase    )
								lowercase__  					=     self.add_symbol(lowerCamelCase    )
								lowercase__  					=     self.add_symbol(lowerCamelCase    )
								lowercase__  					=     self.add_symbol(lowerCamelCase    )
								if extra_special_symbols:
												for s in extra_special_symbols:
																self.add_symbol(lowerCamelCase    )
								lowercase__  					=     len(self.symbols    )




				def __eq__(	self		:       Tuple,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								return self.indices == other.indices




				def __getitem__(	self		:       str,    lowerCamelCase		:       Any    ):

								'''simple docstring'''
								if idx < len(self.symbols    ):
												return self.symbols[idx]
								return self.unk_word




				def __len__(	self		:       List[Any]    ):

								'''simple docstring'''
								return len(self.symbols    )




				def __contains__(	self		:       Union[str, Any],    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								return sym in self.indices




				@classmethod
				def 							lowercase__  (	cls		:       Union[str, Any],    lowerCamelCase		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     cls()
								d.add_from_file(lowerCamelCase    )
								return d




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[str],    lowerCamelCase		:       str=1,    lowerCamelCase		:       str=False    ):

								'''simple docstring'''
								if word in self.indices and not overwrite:
												lowercase__  					=     self.indices[word]
												lowercase__  					=     self.count[idx] + n
												return idx
								else:
												lowercase__  					=     len(self.symbols    )
												lowercase__  					=     idx
												self.symbols.append(lowerCamelCase    )
												self.count.append(lowerCamelCase    )
												return idx




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								return 0




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								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

								lowercase__  					=     f.readlines()
								lowercase__  					=     self._load_meta(lowerCamelCase    )

								for line in lines[indices_start_line:]:
												try:
																lowercase__					,		lowercase__  					=     line.rstrip().rsplit(''' ''',    1    )
																if field == "#fairseq:overwrite":
																				lowercase__  					=     True
																				lowercase__					,		lowercase__  					=     line.rsplit(''' ''',    1    )
																else:
																				lowercase__  					=     False
																lowercase__  					=     int(lowerCamelCase    )
																lowercase__  					=     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     a     (		lowerCamelCase_							):






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






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






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

				# handle various types of models

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

				lowercase__  					=     chkpt['''cfg''']['''model''']

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

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

				lowercase__  					=     os.path.join(lowerCamelCase_						,  VOCAB_FILES_NAMES['''merges_file''']							)
				shutil.copyfile(lowerCamelCase_						,  lowerCamelCase_							)

				# model config
				lowercase__  					=     os.path.join(lowerCamelCase_						,  '''config.json'''							)

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

				# tokenizer config
				lowercase__  					=     os.path.join(lowerCamelCase_						,  lowerCamelCase_							)

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

				# model
				lowercase__  					=     chkpt['''model''']

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

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

				lowercase__  					=     BioGptConfig.from_pretrained(lowerCamelCase_							)
				lowercase__  					=     BioGptForCausalLM(lowerCamelCase_							)

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

				# save
				lowercase__  					=     os.path.join(lowerCamelCase_						,  lowerCamelCase_							)
				print(F"""Generating {pytorch_weights_dump_path}"""							)
				torch.save(lowerCamelCase_						,  lowerCamelCase_							)

				print('''Conversion is done!'''							)


if __name__ == "__main__":
							A__      :     Union[str, Any]       =					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.'
							)
							A__      :     Tuple       =					parser.parse_args()
							convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)












import heapq as hq
import math
from collections.abc import Iterator
class  _UpperCAmelCase   :

				"""simple docstring"""




				def __init__(	self		:       str,    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     str(id_    )
								lowercase__  					=     None
								lowercase__  					=     None
								lowercase__  					=     []
								lowercase__  					=     {}  # {vertex:distance}




				def __lt__(	self		:       Dict,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								return self.key < other.key




				def __repr__(	self		:       Dict    ):

								'''simple docstring'''
								return self.id




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								self.neighbors.append(lowerCamelCase    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								lowercase__  					=     weight






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				# add the neighbors:
				graph[a - 1].add_neighbor(graph[b - 1]							)
				graph[b - 1].add_neighbor(graph[a - 1]							)
				# add the edges:
				graph[a - 1].add_edge(graph[b - 1]						,  lowerCamelCase_							)
				graph[b - 1].add_edge(graph[a - 1]						,  lowerCamelCase_							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0
				lowercase__  					=     graph[:]
				while q:
								lowercase__  					=     min(lowerCamelCase_							)
								q.remove(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in q) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
				for i in range(1						,  len(lowerCamelCase_							)							):
								a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
				return a






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for u in graph:
								lowercase__  					=     math.inf
								lowercase__  					=     None
				lowercase__  					=     0

				lowercase__  					=     list(lowerCamelCase_							)
				hq.heapify(lowerCamelCase_							)

				while h:
								lowercase__  					=     hq.heappop(lowerCamelCase_							)
								for v in u.neighbors:
												if (v in h) and (u.edges[v.id] < v.key):
																lowercase__  					=     u
																lowercase__  					=     u.edges[v.id]
																hq.heapify(lowerCamelCase_							)

				for i in range(1						,  len(lowerCamelCase_							)							):
								yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)






def     a     (		):






				'''simple docstring'''


if __name__ == "__main__":
							import doctest

							doctest.testmod()




