
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[str]        = 2



class    _snake_case :


 def __init__(       self       ,	*,  # begin keyword-only arguments
     SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: Tuple     		=   bos, unk, pad, eos
   lowercase__	: Union[str, Any]     		=   []
   lowercase__	: str     		=   []
   lowercase__	: List[str]     		=   {}
   lowercase__	: List[str]     		=   self.add_symbol(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   self.add_symbol(SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   self.add_symbol(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   self.add_symbol(SCREAMING_SNAKE_CASE_)
   if extra_special_symbols:
     for s in extra_special_symbols:
       self.add_symbol(SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   len(self.symbols)


 def __eq__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.indices == other.indices


 def __getitem__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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


 def __len__(       self):


   '''simple docstring'''




   return len(self.symbols)


 def __contains__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return sym in self.indices


 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   cls()
   d.add_from_file(SCREAMING_SNAKE_CASE_)
   return d


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   if word in self.indices and not overwrite:
     lowercase__	: Optional[Any]     		=   self.indices[word]
     lowercase__	: int     		=   self.count[idx] + n
     return idx
   else:
     lowercase__	: Optional[int]     		=   len(self.symbols)
     lowercase__	: Dict     		=   idx
     self.symbols.append(SCREAMING_SNAKE_CASE_)
     self.count.append(SCREAMING_SNAKE_CASE_)
     return idx


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return 0



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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

   lowercase__	: Any     		=   f.readlines()
   lowercase__	: str     		=   self._load_meta(SCREAMING_SNAKE_CASE_)

   for line in lines[indices_start_line:]:
     try:
       lowercase__		,     lowercase__	: Tuple     		=   line.rstrip().rsplit(""" """       ,	1)
       if field == "#fairseq:overwrite":
         lowercase__	: int     		=   True
         lowercase__		,     lowercase__	: Dict     		=   line.rsplit(""" """       ,	1)
       else:
         lowercase__	: int     		=   False
       lowercase__	: Dict     		=   int(SCREAMING_SNAKE_CASE_)
       lowercase__	: str     		=   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						(   lowercase_ )		->						Any:


  '''simple docstring'''






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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  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

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

  lowercase__	: Optional[Any]     		=   chkpt["""cfg"""]["""model"""]

  # dicts
  lowercase__	: str     		=   os.path.join(lowercase_			,			"""dict.txt""" )
  if not os.path.isfile(lowercase_ ):
    raise ValueError(F'path to the file {dict_file} does not exist!' )
  lowercase__	: List[Any]     		=   Dictionary.load(lowercase_ )
  lowercase__	: List[Any]     		=   rewrite_dict_keys(src_dict.indices )
  lowercase__	: int     		=   len(lowercase_ )
  lowercase__	: Dict     		=   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)
  lowercase__	: Any     		=   os.path.join(lowercase_			,			"""bpecodes""" )
  if not os.path.isfile(lowercase_ ):
    raise ValueError(F'path to the file {bpecodes_file} does not exist!' )

  lowercase__	: Dict     		=   os.path.join(lowercase_			,			VOCAB_FILES_NAMES["""merges_file"""] )
  shutil.copyfile(lowercase_			,			lowercase_ )

  # model config
  lowercase__	: Optional[int]     		=   os.path.join(lowercase_			,			"""config.json""" )

  lowercase__	: Dict     		=   {
      """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(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

  # tokenizer config
  lowercase__	: Dict     		=   os.path.join(lowercase_			,			lowercase_ )

  lowercase__	: Optional[Any]     		=   {
      """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(lowercase_			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(lowercase_			,			ensure_ascii=lowercase_			,			indent=lowercase_ ) )

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

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

  lowercase__	: List[Any]     		=   list(model_state_dict.keys() )
  for layer_name in layer_names:
    if layer_name.endswith("""output_projection.weight""" ):
      lowercase__	: Optional[Any]     		=   model_state_dict.pop(lowercase_ )
    else:
      lowercase__	: Dict     		=   model_state_dict.pop(lowercase_ )

  lowercase__	: Optional[Any]     		=   BioGptConfig.from_pretrained(lowercase_ )
  lowercase__	: Optional[Any]     		=   BioGptForCausalLM(lowercase_ )

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

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

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


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








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""hello"""       ,	"""world""")
     self.assertIn("""Batch size was passed into `f`"""       ,	cm.exception.args[0])
     self.assertIn("""`f(arg1='hello', arg2='world')"""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""Oops, we had an error!"""       ,	cm.exception.args[0])



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






