






"""simple docstring"""



import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCAmelCase	:	str				=      logging.getLogger(__name__)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]=2			,					snake_case       : int=3			,					snake_case       : Union[str, Any]=16			,					snake_case       : int = 10			,					snake_case       : int = 2				)->		int:






     '''simple docstring'''



     def get_dataset(snake_case       : List[str]				):
          UpperCAmelCase__						:    Dict							=							torch.randn(batch_size * n_batches			,					1				)
          return TensorDataset(snake_case			,					a * x + b + 0.1 * torch.randn(batch_size * n_batches			,					1				)				)

     UpperCAmelCase__						:    Any							=							get_dataset(snake_case				)
     UpperCAmelCase__						:    Union[str, Any]							=							get_dataset(snake_case				)
     UpperCAmelCase__						:    int							=							DataLoader(snake_case			,					shuffle=snake_case			,					batch_size=snake_case			,					num_workers=4				)
     UpperCAmelCase__						:    List[Any]							=							DataLoader(snake_case			,					shuffle=snake_case			,					batch_size=snake_case			,					num_workers=4				)
     return (train_dataloader, valid_dataloader)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int			,					snake_case       : List[Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Tuple=None				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    int							=							[]
     for epoch in range(snake_case				):
          # Train quickly
          model.train()
          for batch in dataloader:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							batch
               UpperCAmelCase__						:    List[Any]							=							model(snake_case				)
               UpperCAmelCase__						:    str							=							torch.nn.functional.mse_loss(snake_case			,					snake_case				)
               accelerator.backward(snake_case				)
               optimizer.step()
               optimizer.zero_grad()
          rands.append(random.random()				)  # Introduce some randomness
          if scheduler is not None:
               scheduler.step()
     return rands




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Dict							):


        '''simple docstring'''
        super().__init__()
        UpperCAmelCase__						:    str							=							nn.Parameter(torch.randn(1							)							)
        UpperCAmelCase__						:    int							=							nn.Parameter(torch.randn(1							)							)




   def       __a							(		self      :	List[str]							,			snake_case__      :	Any							):


        '''simple docstring'''
        return x * self.a + self.b




class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2							)
             UpperCAmelCase__						:    Optional[Any]							=							DummyModel()
             UpperCAmelCase__						:    List[str]							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							dummy_dataloaders()
             UpperCAmelCase__						:    Optional[Any]							=							ProjectConfiguration(total_limit=1							,			project_dir=snake_case__							,			automatic_checkpoint_naming=snake_case__							)
             # Train baseline
             UpperCAmelCase__						:    Any							=							Accelerator(project_config=snake_case__							)
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir							)							)							,			1							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2							)
             UpperCAmelCase__						:    Optional[Any]							=							DummyModel()
             UpperCAmelCase__						:    int							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    List[str]							=							dummy_dataloaders()
             # Train baseline
             UpperCAmelCase__						:    Any							=							Accelerator()
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save initial
             UpperCAmelCase__						:    List[str]							=							os.path.join(snake_case__							,			"initial"							)
             accelerator.save_state(snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    int							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    Union[str, Any]							=							optimizer.state_dict()
             UpperCAmelCase__						:    Dict							=							train(3							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    str							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    List[Any]							=							optimizer.state_dict()

             # Train partially
             set_seed(4_2							)
             UpperCAmelCase__						:    Union[str, Any]							=							DummyModel()
             UpperCAmelCase__						:    Union[str, Any]							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							dummy_dataloaders()
             UpperCAmelCase__						:    Union[str, Any]							=							Accelerator()
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             accelerator.load_state(snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    str							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    Tuple							=							optimizer.state_dict()
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)

             UpperCAmelCase__						:    Tuple							=							train(2							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save everything
             UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case__							,			"checkpoint"							)
             accelerator.save_state(snake_case__							)

             # Load everything back in and make sure all states work
             accelerator.load_state(snake_case__							)
             test_rands += train(1							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    Optional[int]							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    str							=							optimizer.state_dict()
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2							)
             UpperCAmelCase__						:    List[Any]							=							DummyModel()
             UpperCAmelCase__						:    int							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							dummy_dataloaders()
             UpperCAmelCase__						:    Dict							=							ProjectConfiguration(automatic_checkpoint_naming=snake_case__							)

             # Train baseline
             UpperCAmelCase__						:    Dict							=							Accelerator(project_dir=snake_case__							,			project_config=snake_case__							)
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save initial
             accelerator.save_state()
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    Optional[Any]							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    List[Any]							=							optimizer.state_dict()
             UpperCAmelCase__						:    int							=							train(3							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    Dict							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    Dict							=							optimizer.state_dict()

             # Train partially
             set_seed(4_2							)
             UpperCAmelCase__						:    Union[str, Any]							=							DummyModel()
             UpperCAmelCase__						:    Union[str, Any]							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							dummy_dataloaders()
             UpperCAmelCase__						:    int							=							ProjectConfiguration(iteration=1							,			automatic_checkpoint_naming=snake_case__							)
             UpperCAmelCase__						:    str							=							Accelerator(project_dir=snake_case__							,			project_config=snake_case__							)
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             accelerator.load_state(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_0"							)							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    Any							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    Dict							=							optimizer.state_dict()
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)

             UpperCAmelCase__						:    int							=							train(2							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_1"							)							)
             test_rands += train(1							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             ((UpperCAmelCase__)							,				(UpperCAmelCase__))						:    str							=							model.a.item(), model.b.item()
             UpperCAmelCase__						:    List[str]							=							optimizer.state_dict()
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
             self.assertEqual(snake_case__							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							torch.tensor([1, 2, 3]							)
        UpperCAmelCase__						:    List[Any]							=							torch.tensor([2, 3, 4]							)
        UpperCAmelCase__						:    Tuple							=							DummyModel()
        UpperCAmelCase__						:    List[str]							=							torch.optim.Adam(net.parameters()							)
        UpperCAmelCase__						:    List[str]							=							Accelerator()
        with self.assertRaises(snake_case__							) as ve:
             accelerator.register_for_checkpointing(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    List[str]							=							str(ve.exception							)
        self.assertTrue("Item at index 0" in message							)
        self.assertTrue("Item at index 1" in message							)
        self.assertFalse("Item at index 2" in message							)
        self.assertFalse("Item at index 3" in message							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2							)
             UpperCAmelCase__						:    Dict							=							DummyModel()
             UpperCAmelCase__						:    Tuple							=							torch.optim.Adam(params=model.parameters()							,			lr=1e-3							)
             UpperCAmelCase__						:    Optional[int]							=							torch.optim.lr_scheduler.StepLR(snake_case__							,			step_size=1							,			gamma=0.99							)
             UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							dummy_dataloaders()
             UpperCAmelCase__						:    List[str]							=							ProjectConfiguration(automatic_checkpoint_naming=snake_case__							)
             # Train baseline
             UpperCAmelCase__						:    int							=							Accelerator(project_dir=snake_case__							,			project_config=snake_case__							)
             UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							accelerator.prepare(
                 snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             # Save initial
             accelerator.save_state()
             UpperCAmelCase__						:    Union[str, Any]							=							scheduler.state_dict()
             train(3							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             self.assertNotEqual(snake_case__							,			scheduler.state_dict()							)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_0"							)							)
             self.assertEqual(snake_case__							,			scheduler.state_dict()							)
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2							)
             UpperCAmelCase__						:    List[str]							=							DummyModel()
             UpperCAmelCase__						:    Any							=							ProjectConfiguration(automatic_checkpoint_naming=snake_case__							,			total_limit=2							)
             # Train baseline
             UpperCAmelCase__						:    List[str]							=							Accelerator(project_dir=snake_case__							,			project_config=snake_case__							)
             UpperCAmelCase__						:    str							=							accelerator.prepare(snake_case__							)
             # Save 3 states:
             for _ in range(1_1							):
                  accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_0"							)							)							)
             self.assertTrue(os.path.exists(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_9"							)							)							)
             self.assertTrue(os.path.exists(os.path.join(snake_case__							,			"checkpoints"							,			"checkpoint_10"							)							)							)




   @require_cuda
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__							)]
        execute_subprocess_async(snake_case__							,			env=os.environ.copy()							)


if __name__ == "__main__":
 _lowerCAmelCase	:	Optional[Any]				=      """/tmp/accelerate/state_checkpointing"""
 _lowerCAmelCase	:	List[Any]				=      DummyModel()
 _lowerCAmelCase	:	Dict				=      torch.optim.Adam(params=model.parameters(), lr=1e-3)
 _lowerCAmelCase	:	Optional[int]				=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
 _lowerCAmelCase			,  _lowerCAmelCase	:	int				=      dummy_dataloaders()
 _lowerCAmelCase	:	Optional[int]				=      ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 _lowerCAmelCase	:	int				=      Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
 if accelerator.process_index == 0:
  if os.path.exists(savedir):
   shutil.rmtree(savedir)
  os.makedirs(savedir)
 _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase	:	Tuple				=      accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 _lowerCAmelCase			,  _lowerCAmelCase	:	Optional[int]				=      accelerator.prepare(model, optimizer)
 train(3, model, train_dataloader, optimizer, accelerator, scheduler)
 # Check that the intial optimizer is loaded on the GPU
 for group in optimizer.param_groups:
  _lowerCAmelCase	:	List[str]				=      group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 _lowerCAmelCase	:	Dict				=      model.cpu()
 accelerator.wait_for_everyone()
 accelerator.save_state()
 accelerator.wait_for_everyone()

 # Check CPU state
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
 for group in optimizer.param_groups:
  _lowerCAmelCase	:	Optional[Any]				=      group["""params"""][0].device
  break
 assert (
     param_device.type == torch.device("""cpu""").type
 ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

 # Check device state
 model.to(accelerator.device)
 accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
 for group in optimizer.param_groups:
  _lowerCAmelCase	:	Optional[int]				=      group["""params"""][0].device
  break
 assert (
     param_device.type == accelerator.device.type
 ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

 # Check error
 with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
  accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
 accelerator.wait_for_everyone()
 if accelerator.process_index == 0:
  shutil.rmtree(savedir)
 accelerator.wait_for_everyone()










"""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()

_lowerCAmelCase	:	Optional[int]				=      2




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			*,  # begin keyword-only arguments
       snake_case__      :	List[str]="<s>"							,			snake_case__      :	str="<pad>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Any="<unk>"							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


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


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        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

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

        for line in lines[indices_start_line:]:
             try:
                  UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rstrip().rsplit(" "							,			1							)
                  if field == "#fairseq:overwrite":
                       UpperCAmelCase__						:    Union[str, Any]							=							True
                       UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							line.rsplit(" "							,			1							)
                  else:
                       UpperCAmelCase__						:    Tuple							=							False
                  UpperCAmelCase__						:    Any							=							int(snake_case__							)
                  UpperCAmelCase__						:    List[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(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 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



     if not os.path.exists(snake_case				):
          raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
     os.makedirs(snake_case			,					exist_ok=snake_case				)
     print(f'Writing results to {pytorch_dump_folder_path}'				)

     # handle various types of models

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

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

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

     # merges_file (bpecodes)
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"bpecodes"				)
     if not os.path.isfile(snake_case				):
          raise ValueError(f'path to the file {bpecodes_file} does not exist!'				)

     UpperCAmelCase__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    Any							=							{
         "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(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    str							=							{
         "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(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # model
     UpperCAmelCase__						:    Union[str, Any]							=							chkpt["model"]

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

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

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							os.path.join(snake_case			,					snake_case				)
     print(f'Generating {pytorch_weights_dump_path}'				)
     torch.save(snake_case			,					snake_case				)

     print("Conversion is done!"				)


if __name__ == "__main__":
 _lowerCAmelCase	:	List[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	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


