import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     BigBirdConfig
  lowercase		:     jnp.dtype            =				jnp.floataa
  lowercase		:     bool            =				True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Union[str, Any]:
         super().setup()
         A						:     List[Any]         =nn.Dense(5      ,	dtype=self.dtype     )







  def __call__(				self    :					Any      ,	*SCREAMING_SNAKE_CASE__    :					List[Any]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Dict:
         A						:     Dict         =super().__call__(*SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
         A						:     List[str]         =self.cls(outputs[2]     )
         return outputs[:2] + (cls_out,)







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				FlaxBigBirdForNaturalQuestionsModule







def A__  (			lowercase:     str,		lowercase:     Optional[int],		lowercase:     Tuple,		lowercase:     Dict,		lowercase:     Optional[Any],		lowercase:     Dict					)				->		int:
       def cross_entropy(lowercase:     Optional[int],		lowercase:     Dict,		lowercase:     List[str]=None					):
              A						:     Any         =logits.shape[-1]
              A						:     List[Any]         =(labels[..., None] == jnp.arange(lowercase					)[None]).astype('f4'					)
              A						:     Union[str, Any]         =jax.nn.log_softmax(lowercase,		axis=-1					)
              A						:     Union[str, Any]         =-jnp.sum(labels * logits,		axis=-1					)
              if reduction is not None:
                     A						:     List[Any]         =reduction(lowercase					)
              return loss

       A						:     Union[str, Any]         =partial(lowercase,		reduction=jnp.mean					)
       A						:     Any         =cross_entropy(lowercase,		lowercase					)
       A						:     List[str]         =cross_entropy(lowercase,		lowercase					)
       A						:     Union[str, Any]         =cross_entropy(lowercase,		lowercase					)
       return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     str            =				"google/bigbird-roberta-base"
  lowercase		:     int            =				3000
  lowercase		:     int            =				10500

  lowercase		:     int            =				128
  lowercase		:     int            =				3

  lowercase		:     int            =				1
  lowercase		:     int            =				5

  # tx_args
  lowercase		:     float            =				3e-5
  lowercase		:     float            =				0.0
  lowercase		:     int            =				20000
  lowercase		:     float            =				0.0_0_9_5

  lowercase		:     str            =				"bigbird-roberta-natural-questions"
  lowercase		:     str            =				"training-expt"
  lowercase		:     str            =				"data/nq-training.jsonl"
  lowercase		:     str            =				"data/nq-validation.jsonl"







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Dict:
         os.makedirs(self.base_dir      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =os.path.join(self.base_dir      ,	self.save_dir     )
         A						:     Optional[Any]         =self.batch_size_per_device * jax.device_count()







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     int
  lowercase		:     int            =				4096  # no dynamic padding on TPUs







  def __call__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Tuple:
         A						:     Dict         =self.collate_fn(SCREAMING_SNAKE_CASE__     )
         A						:     Union[str, Any]         =jax.tree_util.tree_map(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         return batch







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Dict:
         A  ,  A						:     Optional[int]         =self.fetch_inputs(features['input_ids']     )
         A						:     Union[str, Any]         ={
             'input_ids': jnp.array(SCREAMING_SNAKE_CASE__      ,	dtype=jnp.intaa     ),
             'attention_mask': jnp.array(SCREAMING_SNAKE_CASE__      ,	dtype=jnp.intaa     ),
             'start_labels': jnp.array(features['start_token']      ,	dtype=jnp.intaa     ),
             'end_labels': jnp.array(features['end_token']      ,	dtype=jnp.intaa     ),
             'pooled_labels': jnp.array(features['category']      ,	dtype=jnp.intaa     ),
         }
         return batch







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					list     )    ->				Union[str, Any]:
         A						:     Tuple         =[self._fetch_inputs(SCREAMING_SNAKE_CASE__     ) for ids in input_ids]
         return zip(*SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					list     )    ->				Any:
         A						:     Any         =[1 for _ in range(len(SCREAMING_SNAKE_CASE__     )     )]
         while len(SCREAMING_SNAKE_CASE__     ) < self.max_length:
                input_ids.append(self.pad_id     )
                attention_mask.append(0     )
         return input_ids, attention_mask







def A__  (			lowercase:     Optional[Any],		lowercase:     List[Any],		lowercase:     Optional[Any]=None					)				->		Union[str, Any]:
       if seed is not None:
              A						:     Dict         =dataset.shuffle(seed=lowercase					)
       for i in range(len(lowercase					) // batch_size					):
              A						:     Optional[int]         =dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(lowercase					)



@partial(jax.pmap,		axis_name='batch'					)
def A__  (			lowercase:     str,		lowercase:     str,		**lowercase:     Any					)				->		Optional[int]:
       def loss_fn(lowercase:     Dict					):
              A						:     Union[str, Any]         =model_inputs.pop('start_labels'					)
              A						:     Optional[Any]         =model_inputs.pop('end_labels'					)
              A						:     Any         =model_inputs.pop('pooled_labels'					)

              A						:     List[Any]         =state.apply_fn(**lowercase,		params=lowercase,		dropout_rng=lowercase,		train=lowercase					)
              A  ,  A  ,  A						:     List[str]         =outputs

              return state.loss_fn(
                  lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		)

       A  ,  A						:     Union[str, Any]         =jax.random.split(lowercase					)
       A						:     Dict         =jax.value_and_grad(lowercase					)
       A  ,  A						:     str         =grad_fn(state.params					)
       A						:     Any         =jax.lax.pmean({'loss': loss},		axis_name='batch'					)
       A						:     Dict         =jax.lax.pmean(lowercase,		'batch'					)

       A						:     List[str]         =state.apply_gradients(grads=lowercase					)
       return state, metrics, new_drp_rng



@partial(jax.pmap,		axis_name='batch'					)
def A__  (			lowercase:     str,		**lowercase:     Tuple					)				->		List[str]:
       A						:     List[str]         =model_inputs.pop('start_labels'					)
       A						:     Union[str, Any]         =model_inputs.pop('end_labels'					)
       A						:     Union[str, Any]         =model_inputs.pop('pooled_labels'					)

       A						:     List[Any]         =state.apply_fn(**lowercase,		params=state.params,		train=lowercase					)
       A  ,  A  ,  A						:     List[Any]         =outputs

       A						:     Tuple         =state.loss_fn(lowercase,		lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)
       A						:     int         =jax.lax.pmean({'loss': loss},		axis_name='batch'					)
       return metrics



class  SCREAMING_SNAKE_CASE_ (					train_state.TrainState			):
  '''simple docstring'''


  lowercase		:     Callable            =				struct.field(pytree_node=lowerCAmelCase_			)







@dataclass
class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  lowercase		:     Args
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     Callable
  lowercase		:     wandb
  lowercase		:     Callable            =				None







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int=None     )    ->				List[Any]:
         A						:     List[Any]         =model.params
         A						:     int         =TrainState.create(
             apply_fn=model.__call__      ,	params=SCREAMING_SNAKE_CASE__      ,	tx=SCREAMING_SNAKE_CASE__      ,	loss_fn=SCREAMING_SNAKE_CASE__      ,	)
         if ckpt_dir is not None:
                A  ,  A  ,  A  ,  A  ,  A						:     Dict         =restore_checkpoint(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                A						:     str         ={
                    'lr': args.lr,
                    'init_lr': args.init_lr,
                    'warmup_steps': args.warmup_steps,
                    'num_train_steps': num_train_steps,
                    'weight_decay': args.weight_decay,
                }
                A  ,  A						:     Optional[int]         =build_tx(**SCREAMING_SNAKE_CASE__     )
                A						:     List[Any]         =train_state.TrainState(
                    step=SCREAMING_SNAKE_CASE__      ,	apply_fn=model.__call__      ,	params=SCREAMING_SNAKE_CASE__      ,	tx=SCREAMING_SNAKE_CASE__      ,	opt_state=SCREAMING_SNAKE_CASE__      ,	)
                A						:     int         =args
                A						:     List[str]         =data_collator
                A						:     List[str]         =lr
                A						:     Any         =params
         A						:     Any         =jax_utils.replicate(SCREAMING_SNAKE_CASE__     )
         return state







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Optional[int]:
         A						:     Union[str, Any]         =self.args
         A						:     Optional[int]         =len(SCREAMING_SNAKE_CASE__     ) // args.batch_size

         A						:     Optional[int]         =jax.random.PRNGKey(0     )
         A						:     str         =jax.random.split(SCREAMING_SNAKE_CASE__      ,	jax.device_count()     )
         for epoch in range(args.max_epochs     ):
                A						:     Optional[Any]         =jnp.array(0      ,	dtype=jnp.floataa     )
                A						:     Union[str, Any]         =get_batched_dataset(SCREAMING_SNAKE_CASE__      ,	args.batch_size      ,	seed=SCREAMING_SNAKE_CASE__     )
                A						:     Any         =0
                for batch in tqdm(SCREAMING_SNAKE_CASE__      ,	total=SCREAMING_SNAKE_CASE__      ,	desc=f'Running EPOCH-{epoch}'     ):
                       A						:     Dict         =self.data_collator(SCREAMING_SNAKE_CASE__     )
                       A  ,  A  ,  A						:     Union[str, Any]         =self.train_step_fn(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                       running_loss += jax_utils.unreplicate(metrics['loss']     )
                       i += 1
                       if i % args.logging_steps == 0:
                              A						:     Union[str, Any]         =jax_utils.unreplicate(state.step     )
                              A						:     int         =running_loss.item() / i
                              A						:     List[Any]         =self.scheduler_fn(state_step - 1     )

                              A						:     str         =self.evaluate(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                              A						:     Tuple         ={
                                  'step': state_step.item(),
                                  'eval_loss': eval_loss.item(),
                                  'tr_loss': tr_loss,
                                  'lr': lr.item(),
                              }
                              tqdm.write(str(SCREAMING_SNAKE_CASE__     )     )
                              self.logger.log(SCREAMING_SNAKE_CASE__      ,	commit=SCREAMING_SNAKE_CASE__     )

                       if i % args.save_steps == 0:
                              self.save_checkpoint(args.save_dir + f'-e{epoch}-s{i}'      ,	state=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				Optional[Any]:
         A						:     List[Any]         =get_batched_dataset(SCREAMING_SNAKE_CASE__      ,	self.args.batch_size     )
         A						:     str         =len(SCREAMING_SNAKE_CASE__     ) // self.args.batch_size
         A						:     List[str]         =jnp.array(0      ,	dtype=jnp.floataa     )
         A						:     Optional[int]         =0
         for batch in tqdm(SCREAMING_SNAKE_CASE__      ,	total=SCREAMING_SNAKE_CASE__      ,	desc='Evaluating ... '     ):
                A						:     Dict         =self.data_collator(SCREAMING_SNAKE_CASE__     )
                A						:     Optional[Any]         =self.val_step_fn(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )
                running_loss += jax_utils.unreplicate(metrics['loss']     )
                i += 1
         return running_loss / i







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				str:
         A						:     List[Any]         =jax_utils.unreplicate(SCREAMING_SNAKE_CASE__     )
         print(f'SAVING CHECKPOINT IN {save_dir}'      ,	end=' ... '     )
         self.model_save_fn(SCREAMING_SNAKE_CASE__      ,	params=state.params     )
         with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	'opt_state.msgpack'     )      ,	'wb'     ) as f:
                f.write(to_bytes(state.opt_state     )     )
         joblib.dump(self.args      ,	os.path.join(SCREAMING_SNAKE_CASE__      ,	'args.joblib'     )     )
         joblib.dump(self.data_collator      ,	os.path.join(SCREAMING_SNAKE_CASE__      ,	'data_collator.joblib'     )     )
         with open(os.path.join(SCREAMING_SNAKE_CASE__      ,	'training_state.json'     )      ,	'w'     ) as f:
                json.dump({'step': state.step.item()}      ,	SCREAMING_SNAKE_CASE__     )
         print('DONE'     )







def A__  (			lowercase:     Tuple,		lowercase:     Dict					)				->		Optional[Any]:
       print(F'RESTORING CHECKPOINT FROM {save_dir}',		end=' ... '					)
       with open(os.path.join(lowercase,		'flax_model.msgpack'					),		'rb'					) as f:
              A						:     Tuple         =from_bytes(state.params,		f.read()					)

       with open(os.path.join(lowercase,		'opt_state.msgpack'					),		'rb'					) as f:
              A						:     List[str]         =from_bytes(state.opt_state,		f.read()					)

       A						:     Any         =joblib.load(os.path.join(lowercase,		'args.joblib'					)					)
       A						:     Any         =joblib.load(os.path.join(lowercase,		'data_collator.joblib'					)					)

       with open(os.path.join(lowercase,		'training_state.json'					),		'r'					) as f:
              A						:     List[str]         =json.load(lowercase					)
       A						:     int         =training_state['step']

       print('DONE'					)
       return params, opt_state, step, args, data_collator



def A__  (			lowercase:     Dict,		lowercase:     List[Any],		lowercase:     List[str],		lowercase:     List[str]					)				->		int:
       A						:     str         =num_train_steps - warmup_steps
       A						:     Any         =optax.linear_schedule(init_value=lowercase,		end_value=lowercase,		transition_steps=lowercase					)
       A						:     str         =optax.linear_schedule(init_value=lowercase,		end_value=1e-7,		transition_steps=lowercase					)
       A						:     int         =optax.join_schedules(schedules=[warmup_fn, decay_fn],		boundaries=[warmup_steps]					)
       return lr



def A__  (			lowercase:     Union[str, Any],		lowercase:     Union[str, Any],		lowercase:     Tuple,		lowercase:     Union[str, Any],		lowercase:     List[str]					)				->		Union[str, Any]:
       def weight_decay_mask(lowercase:     List[Any]					):
              A						:     Union[str, Any]         =traverse_util.flatten_dict(lowercase					)
              A						:     List[str]         ={k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
              return traverse_util.unflatten_dict(lowercase					)

       A						:     Dict         =scheduler_fn(lowercase,		lowercase,		lowercase,		lowercase					)

       A						:     List[str]         =optax.adamw(learning_rate=lowercase,		weight_decay=lowercase,		mask=lowercase					)
       return tx, lr







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							:      int      							=2



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


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







  def __eq__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				str:
         return self.indices == other.indices







  def __getitem__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				List[Any]:
         if idx < len(self.symbols     ):
                return self.symbols[idx]
         return self.unk_word







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







  def __contains__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
         return sym in self.indices







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
         A						:     Union[str, Any]         =cls()
         d.add_from_file(SCREAMING_SNAKE_CASE__     )
         return d







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Any=1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=False     )    ->				Any:
         if word in self.indices and not overwrite:
                A						:     int         =self.indices[word]
                A						:     Union[str, Any]         =self.count[idx] + n
                return idx
         else:
                A						:     Tuple         =len(self.symbols     )
                A						:     str         =idx
                self.symbols.append(SCREAMING_SNAKE_CASE__     )
                self.count.append(SCREAMING_SNAKE_CASE__     )
                return idx







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[Any]:
         return 0







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				Optional[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

         A						:     str         =f.readlines()
         A						:     int         =self._load_meta(SCREAMING_SNAKE_CASE__     )

         for line in lines[indices_start_line:]:
                try:
                       A  ,  A						:     Optional[int]         =line.rstrip().rsplit(' '      ,	1     )
                       if field == "#fairseq:overwrite":
                              A						:     int         =True
                              A  ,  A						:     Optional[Any]         =line.rsplit(' '      ,	1     )
                       else:
                              A						:     Any         =False
                       A						:     Tuple         =int(SCREAMING_SNAKE_CASE__     )
                       A						:     Optional[int]         =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__  (			lowercase:     Union[str, Any]					)				->		str:
       # (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}
       A						:     int         =dict((re.sub(r'@@$',		'',		lowercase					), v) if k.endswith('@@'					) else (re.sub(r'$',		'</w>',		lowercase					), v) for k, v in d.items()					)
       A						:     int         ='<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              A						:     List[Any]         =d[k]  # restore
       return da



def A__  (			lowercase:     Optional[int],		lowercase:     Optional[Any]					)				->		str:
       # prep
       if not os.path.exists(lowercase					):
              raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'					)
       os.makedirs(lowercase,		exist_ok=lowercase					)
       print(F'Writing results to {pytorch_dump_folder_path}'					)

       # handle various types of models

       A						:     List[str]         =os.path.join(lowercase,		'checkpoint.pt'					)
       if not os.path.isfile(lowercase					):
              raise ValueError(F'path to the file {checkpoint_file} does not exist!'					)
       A						:     Optional[Any]         =torch.load(lowercase,		map_location='cpu'					)

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

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

       # merges_file (bpecodes)
       A						:     List[str]         =os.path.join(lowercase,		'bpecodes'					)
       if not os.path.isfile(lowercase					):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'					)

       A						:     List[str]         =os.path.join(lowercase,		VOCAB_FILES_NAMES['merges_file']					)
       shutil.copyfile(lowercase,		lowercase					)

       # model config
       A						:     Tuple         =os.path.join(lowercase,		'config.json'					)

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

       # tokenizer config
       A						:     int         =os.path.join(lowercase,		lowercase					)

       A						:     List[str]         ={
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1_024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

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

       # model
       A						:     List[Any]         =chkpt['model']

       # remove unneeded keys
       A						:     List[Any]         =[
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowercase,		lowercase					)

       A						:     str         =list(model_state_dict.keys()					)
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'					):
                     A						:     Union[str, Any]         =model_state_dict.pop(lowercase					)
              else:
                     A						:     List[str]         =model_state_dict.pop(lowercase					)

       A						:     Any         =BioGptConfig.from_pretrained(lowercase					)
       A						:     str         =BioGptForCausalLM(lowercase					)

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

       # save
       A						:     Tuple         =os.path.join(lowercase,		lowercase					)
       print(F'Generating {pytorch_weights_dump_path}'					)
       torch.save(lowercase,		lowercase					)

       print('Conversion is done!'					)


if __name__ == "__main__":
    _lowercase							:      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.'''
    )
    _lowercase							:      List[Any]      							=parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






