




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()

lowerCamelCase    :		List[Any]			   =       2







class   A:



	'''simple docstring'''


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

							"""simple docstring"""






							lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   bos, unk, pad, eos
							lowerCamelCase_							=   []
							lowerCamelCase_							=   []
							lowerCamelCase_							=   {}
							lowerCamelCase_							=   self.add_symbol(UpperCamelCase__  )
							lowerCamelCase_							=   self.add_symbol(UpperCamelCase__  )
							lowerCamelCase_							=   self.add_symbol(UpperCamelCase__  )
							lowerCamelCase_							=   self.add_symbol(UpperCamelCase__  )
							if extra_special_symbols:
													for s in extra_special_symbols:
																			self.add_symbol(UpperCamelCase__  )
							lowerCamelCase_							=   len(self.symbols  )

	def __eq__(    self :  Optional[Any]						,							A_ :  Tuple  )       ->     str:

							"""simple docstring"""






							return self.indices == other.indices

	def __getitem__(    self :  str						,							A_ :  Optional[int]  )       ->     List[Any]:

							"""simple docstring"""






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

	def __len__(    self :  int  )       ->     List[str]:

							"""simple docstring"""






							return len(self.symbols  )

	def __contains__(    self :  Union[str, Any]						,							A_ :  Dict  )       ->     List[Any]:

							"""simple docstring"""






							return sym in self.indices

	@classmethod
	def     a__			(    cls :  Dict						,							A_ :  List[str]  )       ->     Optional[int]:

							"""simple docstring"""






							lowerCamelCase_							=   cls()
							d.add_from_file(UpperCamelCase__  )
							return d

	def     a__			(    self :  Optional[int]						,							A_ :  Any						,							A_ :  List[Any]=1						,							A_ :  List[str]=False  )       ->     Tuple:

							"""simple docstring"""






							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(UpperCamelCase__  )
													self.count.append(UpperCamelCase__  )
													return idx

	def     a__			(    self :  Optional[int]						,							A_ :  List[Any]  )       ->     int:

							"""simple docstring"""






							return 0





	def     a__			(    self :  List[Any]						,							A_ :  str  )       ->     str:

							"""simple docstring"""






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

							lowerCamelCase_							=   f.readlines()
							lowerCamelCase_							=   self._load_meta(UpperCamelCase__  )

							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(UpperCamelCase__  )
																			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(UpperCamelCase__  )  )
																			self.add_symbol(UpperCamelCase__						,							n=UpperCamelCase__						,							overwrite=UpperCamelCase__  )
													except ValueError:
																			raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''  )

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]					):


						'''simple docstring'''



						lowerCamelCase_							=   dict((re.sub(r'@@$'     ,							''     ,							UpperCAmelCase__					), v) if k.endswith('@@'					) else (re.sub(r'$'     ,							'</w>'     ,							UpperCAmelCase__					), 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       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	Tuple					):


						'''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

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

						lowerCamelCase_							=   chkpt['cfg']['model']

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

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

						# model config
						lowerCamelCase_							=   os.path.join(UpperCAmelCase__     ,							'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-1_2,
						    '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
						lowerCamelCase_							=   os.path.join(UpperCAmelCase__     ,							UpperCAmelCase__					)

						lowerCamelCase_							=   {
						    'bos_token': '<s>',
						    'eos_token': '</s>',
						    'model_max_length': 10_24,
						    '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
						lowerCamelCase_							=   chkpt['model']

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

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

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

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

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

						print('Conversion is done!'					)


if __name__ == "__main__":
	lowerCamelCase    :		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."
	)
	lowerCamelCase    :		Optional[int]			   =       parser.parse_args()
	convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)


lowerCamelCase    :		str			   =       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
            raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()					)}."""					)

      if tokenizer_name is None:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							tokenizer_name + 'Fast'					)}

      logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""					)

      for tokenizer_name in tokenizer_names:
            lowerCamelCase_							=   TOKENIZER_CLASSES[tokenizer_name]

            lowerCamelCase_							=   True
            if checkpoint_name is None:
                  lowerCamelCase_							=   list(tokenizer_class.max_model_input_sizes.keys()					)
            else:
                  lowerCamelCase_							=   [checkpoint_name]

            logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""					)

            for checkpoint in checkpoint_names:
                  logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""					)

                  # Load tokenizer
                  lowerCamelCase_							=   tokenizer_class.from_pretrained(lowercase     ,							force_download=lowercase					)

                  # Save fast tokenizer
                  logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""					)

                  # For organization names we create sub-directories
                  if "/" in checkpoint:
                        lowerCamelCase_					,    lowerCamelCase_							=   checkpoint.split('/'					)
                        lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                  elif add_prefix:
                        lowerCamelCase_							=   checkpoint
                        lowerCamelCase_							=   dump_path
                  else:
                        lowerCamelCase_							=   None
                        lowerCamelCase_							=   dump_path

                  logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()					)[0]:
                        lowerCamelCase_							=   list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
                        lowerCamelCase_							=   file_path.split(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

                        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  lowerCamelCase_							=   tokenizer.save_pretrained(
                      lowercase     ,							legacy_format=lowercase     ,							filename_prefix=lowercase					)
                  logger.info(f"""=> File names {file_names}"""					)

                  for file_name in file_names:
                        if not file_name.endswith('tokenizer.json'					):
                              os.remove(lowercase					)
                              logger.info(f"""=> removing {file_name}"""					)


if __name__ == "__main__":
 lowerCamelCase    :		Tuple			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
 )
 parser.add_argument(
     "--tokenizer_name",
     default=None,
     type=str,
     help=(
         F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
         "download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--checkpoint_name",
     default=None,
     type=str,
     help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
 )
 parser.add_argument(
     "--force_download",
     action="store_true",
     help="Re-download checkpoints.",
 )
 lowerCamelCase    :		Union[str, Any]			   =       parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)



