

"""simple docstring"""





import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       DistilBertForMaskedLM,
       DistilBertForMultipleChoice,
       DistilBertForQuestionAnswering,
       DistilBertForSequenceClassification,
       DistilBertForTokenClassification,
       DistilBertModel,
   )




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			List[str]=1_3,					UpperCAmelCase__							:			Union[str, Any]=7,					UpperCAmelCase__							:			Optional[int]=True,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=False,					UpperCAmelCase__							:			Union[str, Any]=True,					UpperCAmelCase__							:			Any=9_9,					UpperCAmelCase__							:			List[Any]=3_2,					UpperCAmelCase__							:			int=5,					UpperCAmelCase__							:			Dict=4,					UpperCAmelCase__							:			Dict=3_7,					UpperCAmelCase__							:			List[str]="gelu",					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Optional[Any]=0.1,					UpperCAmelCase__							:			Union[str, Any]=5_1_2,					UpperCAmelCase__							:			int=1_6,					UpperCAmelCase__							:			int=2,					UpperCAmelCase__							:			Optional[Any]=0.02,					UpperCAmelCase__							:			Optional[int]=3,					UpperCAmelCase__							:			Tuple=4,					UpperCAmelCase__							:			List[str]=None,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_token_type_ids
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						type_vocab_size
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range
    __lowercase								=						num_labels
    __lowercase								=						num_choices
    __lowercase								=						scope


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

    __lowercase								=						None
    if self.use_input_mask:
       __lowercase								=						random_attention_mask([self.batch_size, self.seq_length]	)

    __lowercase								=						None
    __lowercase								=						None
    __lowercase								=						None
    if self.use_labels:
       __lowercase								=						ids_tensor([self.batch_size],					self.type_sequence_label_size	)
       __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.num_labels	)
       __lowercase								=						ids_tensor([self.batch_size],					self.num_choices	)

    __lowercase								=						self.get_config()

    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 					_lowercase (			self							:			str	):
    return DistilBertConfig(
        vocab_size=self.vocab_size,					dim=self.hidden_size,					n_layers=self.num_hidden_layers,					n_heads=self.num_attention_heads,					hidden_dim=self.intermediate_size,					hidden_act=self.hidden_act,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			str	):
    __lowercase								=						DistilBertModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						DistilBertForMaskedLM(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						DistilBertForQuestionAnswering(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					start_positions=UpperCAmelCase__,					end_positions=UpperCAmelCase__	)
    self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)	)
    self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						self.num_labels
    __lowercase								=						DistilBertForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			int	):
    __lowercase								=						self.num_labels
    __lowercase								=						DistilBertForTokenClassification(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()

    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any	):
    __lowercase								=						self.num_choices
    __lowercase								=						DistilBertForMultipleChoice(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						input_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
    __lowercase								=						input_mask.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__,					)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)



 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.prepare_config_and_inputs()
    ((__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase))								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	(
     (
         DistilBertModel,
         DistilBertForMaskedLM,
         DistilBertForMultipleChoice,
         DistilBertForQuestionAnswering,
         DistilBertForSequenceClassification,
         DistilBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 __UpperCAmelCase	:					List[Any]   		=	(
     {
         "feature-extraction": DistilBertModel,
         "fill-mask": DistilBertForMaskedLM,
         "question-answering": DistilBertForQuestionAnswering,
         "text-classification": DistilBertForSequenceClassification,
         "token-classification": DistilBertForTokenClassification,
         "zero-shot": DistilBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase	:					Union[str, Any]   		=	True
 __UpperCAmelCase	:					Optional[Any]   		=	True
 __UpperCAmelCase	:					Union[str, Any]   		=	True
 __UpperCAmelCase	:					str   		=	True


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						DistilBertModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					dim=3_7	)


 def 					_lowercase (			self							:			Optional[int]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			List[str]	):
    for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						DistilBertModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 @slow
 @require_torch_gpu
 def 					_lowercase (			self							:			Any	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       # BertForMultipleChoice behaves incorrectly in JIT environments.
       if model_class == DistilBertForMultipleChoice:
          return

       __lowercase								=						True
       __lowercase								=						model_class(config=UpperCAmelCase__	)

       __lowercase								=						self._prepare_for_class(UpperCAmelCase__,					UpperCAmelCase__	)
       __lowercase								=						torch.jit.trace(
           UpperCAmelCase__,					(inputs_dict["input_ids"].to("cpu"	), inputs_dict["attention_mask"].to("cpu"	))	)

       with tempfile.TemporaryDirectory() as tmp:
          torch.jit.save(UpperCAmelCase__,					os.path.join(UpperCAmelCase__,					"traced_model.pt"	)	)
          __lowercase								=						torch.jit.load(os.path.join(UpperCAmelCase__,					"traced_model.pt"	),					map_location=UpperCAmelCase__	)
          loaded(inputs_dict["input_ids"].to(UpperCAmelCase__	),					inputs_dict["attention_mask"].to(UpperCAmelCase__	)	)







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						DistilBertModel.from_pretrained("distilbert-base-uncased"	)
    __lowercase								=						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
    __lowercase								=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
    with torch.no_grad():
       __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	)[0]
    __lowercase								=						torch.Size((1, 1_1, 7_6_8)	)
    self.assertEqual(output.shape,					UpperCAmelCase__	)
    __lowercase								=						torch.tensor(
        [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]	)

    self.assertTrue(torch.allclose(output[:, 1:4, 1:4],					UpperCAmelCase__,					atol=1E-4	)	)







"""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()

_a       =							2




class 					_lowerCAmelCase  :







 """simple docstring"""







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


 def __eq__(			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return self.indices == other.indices


 def __getitem__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str]	):
    if idx < len(self.symbols	):
       return self.symbols[idx]
    return self.unk_word


 def __len__(			self							:			str	):
    return len(self.symbols	)


 def __contains__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    return sym in self.indices


 @classmethod
 def 					_lowercase (			cls							:			List[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						cls()
    d.add_from_file(UpperCAmelCase__	)
    return d


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=1,					UpperCAmelCase__							:			str=False	):
    if word in self.indices and not overwrite:
       __lowercase								=						self.indices[word]
       __lowercase								=						self.count[idx] + n
       return idx
    else:
       __lowercase								=						len(self.symbols	)
       __lowercase								=						idx
       self.symbols.append(UpperCAmelCase__	)
       self.count.append(UpperCAmelCase__	)
       return idx


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str	):
    return 0



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[Any]	):
    if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       try:
          with open(UpperCAmelCase__,					"r",					encoding="utf-8"	) as fd:
             self.add_from_file(UpperCAmelCase__	)
       except FileNotFoundError as fnfe:
          raise fnfe
       except UnicodeError:
          raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCAmelCase__	)	)
       return

    __lowercase								=						f.readlines()
    __lowercase								=						self._load_meta(UpperCAmelCase__	)

    for line in lines[indices_start_line:]:
       try:
          __lowercase		,__lowercase								=						line.rstrip().rsplit(" ",					1	)
          if field == "#fairseq:overwrite":
             __lowercase								=						True
             __lowercase		,__lowercase								=						line.rsplit(" ",					1	)
          else:
             __lowercase								=						False
          __lowercase								=						int(UpperCAmelCase__	)
          __lowercase								=						line
          if word in self and not overwrite:
             raise RuntimeError(
                 "Duplicate word found when loading Dictionary: '{}'. "
                 "Duplicate words can overwrite earlier ones by adding the "
                 "#fairseq:overwrite flag at the end of the corresponding row "
                 "in the dictionary file. If using the Camembert model, please "
                 "download an updated copy of the model file.".format(UpperCAmelCase__	)	)
          self.add_symbol(UpperCAmelCase__,					n=UpperCAmelCase__,					overwrite=UpperCAmelCase__	)
       except ValueError:
          raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"	)







def   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						List[Any]:
   '''simple docstring'''


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

   # handle various types of models

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

   __lowercase								=						chkpt["cfg"]["model"]

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

   # merges_file (bpecodes)
   __lowercase								=						os.path.join(UpperCamelCase_,			"bpecodes")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {bpecodes_file} does not exist!""")

   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["merges_file"])
   shutil.copyfile(UpperCamelCase_,			UpperCamelCase_)

   # model config
   __lowercase								=						os.path.join(UpperCamelCase_,			"config.json")

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

   # tokenizer config
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)

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

   # model
   __lowercase								=						chkpt["model"]

   # remove unneeded keys
   __lowercase								=						[
       "decoder.version",
   ]
   for k in ignore_keys:
      model_state_dict.pop(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						list(model_state_dict.keys())
   for layer_name in layer_names:
      if layer_name.endswith("output_projection.weight"):
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)
      else:
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)

   __lowercase								=						BioGptConfig.from_pretrained(UpperCamelCase_)
   __lowercase								=						BioGptForCausalLM(UpperCamelCase_)

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

   # save
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)
   print(F"""Generating {pytorch_weights_dump_path}""")
   torch.save(UpperCamelCase_,			UpperCamelCase_)

   print("Conversion is done!")


if __name__ == "__main__":
   _a       =							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.'
   )
   _a       =							parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




