
"""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()

SCREAMING_SNAKE_CASE_					     =	2
class       lowerCAmelCase_		:



							'''simple docstring'''





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




							def __eq__( self    ,				snake_case_				)       ->  int:
													return self.indices == other.indices




							def __getitem__( self    ,				snake_case_				)       ->  str:
													if idx < len(self.symbols				):
																			return self.symbols[idx]
													return self.unk_word




							def __len__( self				)       ->  List[Any]:
													return len(self.symbols				)




							def __contains__( self    ,				snake_case_				)       ->  Union[str, Any]:
													return sym in self.indices




							@classmethod
							def    A__						( cls    ,				snake_case_				)       ->  List[str]:
													__lowerCAmelCase     =  cls()
													d.add_from_file(snake_case_				)
													return d




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




							def    A__						( self    ,				snake_case_				)       ->  Dict:
													return 0




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

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

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

def  lowercase     (_lowerCAmelCase						):
						# (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}
						__lowerCAmelCase     =  dict((re.sub(r"""@@$""" ,    """""" ,    _lowerCAmelCase						), v) if k.endswith("""@@"""						) else (re.sub(r"""$""" ,    """</w>""" ,    _lowerCAmelCase						), v) for k, v in d.items()						)
						__lowerCAmelCase     =  """<s> <pad> </s> <unk>""".split()
						# restore the special tokens
						for k in keep_keys:
												del da[f"""{k}</w>"""]
												__lowerCAmelCase     =  d[k]  # restore
						return da

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						# 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

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

						__lowerCAmelCase     =  chkpt["""cfg"""]["""model"""]

						# dicts
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """dict.txt"""						)
						if not os.path.isfile(_lowerCAmelCase						):
												raise ValueError(f"""path to the file {dict_file} does not exist!"""						)
						__lowerCAmelCase     =  Dictionary.load(_lowerCAmelCase						)
						__lowerCAmelCase     =  rewrite_dict_keys(src_dict.indices						)
						__lowerCAmelCase     =  len(_lowerCAmelCase						)
						__lowerCAmelCase     =  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)
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """bpecodes"""						)
						if not os.path.isfile(_lowerCAmelCase						):
												raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""						)

						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    VOCAB_FILES_NAMES["""merges_file"""]						)
						shutil.copyfile(_lowerCAmelCase ,    _lowerCAmelCase						)

						# model config
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """config.json"""						)

						__lowerCAmelCase     =  {
						    """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
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    _lowerCAmelCase						)

						__lowerCAmelCase     =  {
						    """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
						__lowerCAmelCase     =  chkpt["""model"""]

						# remove unneeded keys
						__lowerCAmelCase     =  [
						    """decoder.version""",
						]
						for k in ignore_keys:
												model_state_dict.pop(_lowerCAmelCase ,    _lowerCAmelCase						)

						__lowerCAmelCase     =  list(model_state_dict.keys()						)
						for layer_name in layer_names:
												if layer_name.endswith("""output_projection.weight"""						):
																		__lowerCAmelCase     =  model_state_dict.pop(_lowerCAmelCase						)
												else:
																		__lowerCAmelCase     =  model_state_dict.pop(_lowerCAmelCase						)

						__lowerCAmelCase     =  BioGptConfig.from_pretrained(_lowerCAmelCase						)
						__lowerCAmelCase     =  BioGptForCausalLM(_lowerCAmelCase						)

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

						# save
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    _lowerCAmelCase						)
						print(f"""Generating {pytorch_weights_dump_path}"""						)
						torch.save(_lowerCAmelCase ,    _lowerCAmelCase						)

						print("""Conversion is done!"""						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character

def  lowercase     (_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += [key]
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator

def  lowercase     (*_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += keys
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __new__( cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													__lowerCAmelCase     =  super().__new__(cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)
													if not hasattr(snake_case_    ,				"""key_handler"""				):
																			setattr(snake_case_    ,				"""key_handler"""    ,				{}				)
													setattr(snake_case_    ,				"""handle_input"""    ,				KeyHandler.handle_input				)

													for value in attrs.values():
																			__lowerCAmelCase     =  getattr(snake_case_    ,				"""handle_key"""    ,				[]				)
																			for key in handled_keys:
																									__lowerCAmelCase     =  value
													return new_cls




							@staticmethod
							def    A__						( cls				)       ->  Tuple:
													__lowerCAmelCase     =  get_character()
													if char != KEYMAP["undefined"]:
																			__lowerCAmelCase     =  ord(snake_case_				)
													__lowerCAmelCase     =  cls.key_handler.get(snake_case_				)
													if handler:
																			__lowerCAmelCase     =  char
																			return handler(cls				)
													else:
																			return None

def  lowercase     (cls						):
						return KeyHandler(cls.__name__ ,    cls.__bases__ ,    cls.__dict__.copy()						)
