



"""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()

lowercase__		:				Any          =	2







class       UpperCamelCase__   :




      """simple docstring"""




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



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



      def __getitem__(      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[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				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        return sym in self.indices



      @classmethod
      def      SCREAMING_SNAKE_CASE__ (      cls				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Any       ):
        lowerCAmelCase_   :				Optional[int]								= cls()
        d.add_from_file(SCREAMING_SNAKE_CASE_       )
        return d



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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        return 0





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any       ):
        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

        lowerCAmelCase_   :				Dict								= f.readlines()
        lowerCAmelCase_   :				List[str]								= self._load_meta(SCREAMING_SNAKE_CASE_       )

        for line in lines[indices_start_line:]:
          try:
            lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= line.rstrip().rsplit(' '    ,			1       )
            if field == "#fairseq:overwrite":
              lowerCAmelCase_   :				Union[str, Any]								= True
              lowerCAmelCase_						,lowerCAmelCase_   :				List[Any]								= line.rsplit(' '    ,			1       )
            else:
              lowerCAmelCase_   :				str								= False
            lowerCAmelCase_   :				Tuple								= int(SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				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 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= dict((re.sub(R'@@$'    ,   ''    ,   lowerCAmelCase__						), v) if k.endswith('@@'						) else (re.sub(R'$'    ,   '</w>'    ,   lowerCAmelCase__						), v) for k, v in d.items()						)
  lowerCAmelCase_   :				Any								= '<s> <pad> </s> <unk>'.split()
  # restore the special tokens
  for k in keep_keys:
    del da[f"{k}</w>"]
    lowerCAmelCase_   :				Union[str, Any]								= d[k]  # restore
  return da

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				List[str]						)						->   Dict:

  """simple docstring"""




  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_   :				Tuple								= 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_   :				int								= torch.load(lowerCAmelCase__    ,   map_location='cpu'						)

  lowerCAmelCase_   :				List[Any]								= chkpt['cfg']['model']

  # dicts
  lowerCAmelCase_   :				Optional[Any]								= 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_   :				List[Any]								= Dictionary.load(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= rewrite_dict_keys(src_dict.indices						)
  lowerCAmelCase_   :				Optional[int]								= len(lowerCAmelCase__						)
  lowerCAmelCase_   :				Dict								= 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_   :				str								= os.path.join(lowerCAmelCase__    ,   'bpecodes'						)
  if not os.path.isfile(lowerCAmelCase__						):
    raise ValueError(f"path to the file {bpecodes_file} does not exist!"						)

  lowerCAmelCase_   :				Dict								= os.path.join(lowerCAmelCase__    ,   VOCAB_FILES_NAMES['merges_file']						)
  shutil.copyfile(lowerCAmelCase__    ,   lowerCAmelCase__						)

  # model config
  lowerCAmelCase_   :				Dict								= os.path.join(lowerCAmelCase__    ,   'config.json'						)

  lowerCAmelCase_   :				int								= {
      '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_   :				Any								= os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__						)

  lowerCAmelCase_   :				Optional[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(lowerCAmelCase__    ,   'w'    ,   encoding='utf-8'						) as f:
    f.write(json.dumps(lowerCAmelCase__    ,   ensure_ascii=lowerCAmelCase__    ,   indent=lowerCAmelCase__						)						)

  # model
  lowerCAmelCase_   :				Any								= chkpt['model']

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

  lowerCAmelCase_   :				str								= list(model_state_dict.keys()						)
  for layer_name in layer_names:
    if layer_name.endswith('output_projection.weight'						):
      lowerCAmelCase_   :				Optional[int]								= model_state_dict.pop(lowerCAmelCase__						)
    else:
      lowerCAmelCase_   :				List[str]								= model_state_dict.pop(lowerCAmelCase__						)

  lowerCAmelCase_   :				Dict								= BioGptConfig.from_pretrained(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= BioGptForCausalLM(lowerCAmelCase__						)

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

  # save
  lowerCAmelCase_   :				str								= os.path.join(lowerCAmelCase__    ,   lowerCAmelCase__						)
  print(f"Generating {pytorch_weights_dump_path}"						)
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)

  print('Conversion is done!'						)


if __name__ == "__main__":
     lowercase__		:				Dict          =	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."""
     )
     lowercase__		:				Optional[Any]          =	parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """linear"""
      _SCREAMING_SNAKE_CASE  				=     """cosine"""
      _SCREAMING_SNAKE_CASE  				=     """cosine_with_restarts"""
      _SCREAMING_SNAKE_CASE  				=     """polynomial"""
      _SCREAMING_SNAKE_CASE  				=     """constant"""
      _SCREAMING_SNAKE_CASE  				=     """constant_with_warmup"""
      _SCREAMING_SNAKE_CASE  				=     """piecewise_constant"""




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int = -1						)						->   Tuple:

  """simple docstring"""




  return LambdaLR(lowerCAmelCase__    ,   lambda lowerCAmelCase__						: 1    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = -1						)						->   str:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1.0    ,   lowerCAmelCase__						)						)
    return 1.0

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				str								= {}
  lowerCAmelCase_   :				Optional[Any]								= step_rules.split(','						)
  for rule_str in rule_list[:-1]:
    lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= rule_str.split(':'						)
    lowerCAmelCase_   :				List[str]								= int(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[int]								= float(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= value
  lowerCAmelCase_   :				str								= float(rule_list[-1]						)

  def create_rules_function(lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				int						):
    def rule_func(lowerCAmelCase__      :				int						) -> float:
      lowerCAmelCase_   :				str								= sorted(rules_dict.keys()						)
      for i, sorted_step in enumerate(lowerCAmelCase__						):
        if steps < sorted_step:
          return rules_dict[sorted_steps[i]]
      return last_lr_multiple

    return rule_func

  lowerCAmelCase_   :				Dict								= create_rules_function(lowerCAmelCase__    ,   lowerCAmelCase__						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]=-1						)						->   Any:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    return max(
        0.0    ,   float(num_training_steps - current_step						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float = 0.5    ,   lowerCAmelCase__      :				int = -1						)						->   Union[str, Any]:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				Any						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				List[Any]								= float(current_step - num_warmup_steps						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)
    return max(0.0    ,   0.5 * (1.0 + math.cos(math.pi * float(lowerCAmelCase__						) * 2.0 * progress						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				int = -1						)						->   int:

  """simple docstring"""




  def lr_lambda(lowerCAmelCase__      :				str						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    lowerCAmelCase_   :				Dict								= float(current_step - num_warmup_steps						) / float(max(1    ,   num_training_steps - num_warmup_steps						)						)
    if progress >= 1.0:
      return 0.0
    return max(0.0    ,   0.5 * (1.0 + math.cos(math.pi * ((float(lowerCAmelCase__						) * progress) % 1.0)						))						)

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=1e-7    ,   lowerCAmelCase__      :				Union[str, Any]=1.0    ,   lowerCAmelCase__      :				int=-1						)						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= optimizer.defaults['lr']
  if not (lr_init > lr_end):
    raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"						)

  def lr_lambda(lowerCAmelCase__      :				int						):
    if current_step < num_warmup_steps:
      return float(lowerCAmelCase__						) / float(max(1    ,   lowerCAmelCase__						)						)
    elif current_step > num_training_steps:
      return lr_end / lr_init  # as LambdaLR multiplies by lr_init
    else:
      lowerCAmelCase_   :				Any								= lr_init - lr_end
      lowerCAmelCase_   :				int								= num_training_steps - num_warmup_steps
      lowerCAmelCase_   :				Dict								= 1 - (current_step - num_warmup_steps) / decay_steps
      lowerCAmelCase_   :				Dict								= lr_range * pct_remaining**power + lr_end
      return decay / lr_init  # as LambdaLR multiplies by lr_init

  return LambdaLR(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)


lowercase__		:				Any          =	{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, SchedulerType]    ,   lowerCAmelCase__      :				Optimizer    ,   lowerCAmelCase__      :				Optional[str] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				Optional[int] = None    ,   lowerCAmelCase__      :				int = 1    ,   lowerCAmelCase__      :				float = 1.0    ,   lowerCAmelCase__      :				int = -1    ,   )						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= SchedulerType(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[Any]								= TYPE_TO_SCHEDULER_FUNCTION[name]
  if name == SchedulerType.CONSTANT:
    return schedule_func(lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  if name == SchedulerType.PIECEWISE_CONSTANT:
    return schedule_func(lowerCAmelCase__    ,   step_rules=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  # All other schedulers require `num_warmup_steps`
  if num_warmup_steps is None:
    raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."						)

  if name == SchedulerType.CONSTANT_WITH_WARMUP:
    return schedule_func(lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)

  # All other schedulers require `num_training_steps`
  if num_training_steps is None:
    raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."						)

  if name == SchedulerType.COSINE_WITH_RESTARTS:
    return schedule_func(
        lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   num_cycles=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__    ,   )

  if name == SchedulerType.POLYNOMIAL:
    return schedule_func(
        lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   power=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__    ,   )

  return schedule_func(
      lowerCAmelCase__    ,   num_warmup_steps=lowerCAmelCase__    ,   num_training_steps=lowerCAmelCase__    ,   last_epoch=lowerCAmelCase__						)





