





"""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__						= 2



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


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

  def __eq__(		self :							Union[str, Any]       , __a :							int						)		->					Any:
        return self.indices == other.indices

  def __getitem__(		self :							Union[str, Any]       , __a :							Tuple						)		->					Any:
        if idx < len(self.symbols						):
              return self.symbols[idx]
        return self.unk_word

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

  def __contains__(		self :							Union[str, Any]       , __a :							str						)		->					Dict:
        return sym in self.indices

  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		cls :							List[Any]       , __a :							Optional[int]						)		->					Optional[int]:
        _UpperCamelCase   :						Dict    =							cls()
        d.add_from_file(__a						)
        return d

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							List[str]       , __a :							Any=1       , __a :							List[str]=False						)		->					List[Any]:
        if word in self.indices and not overwrite:
              _UpperCamelCase   :						List[Any]    =							self.indices[word]
              _UpperCamelCase   :						Union[str, Any]    =							self.count[idx] + n
              return idx
        else:
              _UpperCamelCase   :						Dict    =							len(self.symbols						)
              _UpperCamelCase   :						List[str]    =							idx
              self.symbols.append(__a						)
              self.count.append(__a						)
              return idx

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Tuple						)		->					int:
        return 0


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Dict						)		->					List[str]:
        if isinstance(__a       , __a						):
              try:
                    with open(__a       , "r"       , encoding="utf-8"						) as fd:
                          self.add_from_file(__a						)
              except FileNotFoundError as fnfe:
                    raise fnfe
              except UnicodeError:
                    raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(__a						)						)
              return

        _UpperCamelCase   :						Dict    =							f.readlines()
        _UpperCamelCase   :						List[str]    =							self._load_meta(__a						)

        for line in lines[indices_start_line:]:
              try:
                    _UpperCamelCase,	_UpperCamelCase   :						Tuple    =							line.rstrip().rsplit(" "       , 1						)
                    if field == "#fairseq:overwrite":
                          _UpperCamelCase   :						Tuple    =							True
                          _UpperCamelCase,	_UpperCamelCase   :						int    =							line.rsplit(" "       , 1						)
                    else:
                          _UpperCamelCase   :						Optional[Any]    =							False
                    _UpperCamelCase   :						str    =							int(__a						)
                    _UpperCamelCase   :						List[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(__a						)						)
                    self.add_symbol(__a       , n=__a       , overwrite=__a						)
              except ValueError:
                    raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)





def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						int    =							dict((re.sub(r"@@$"		,""		,lowercase_      ), v) if k.endswith("@@"      ) else (re.sub(r"$"		,"</w>"		,lowercase_      ), 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        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[int]:




      """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

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

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

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

      _UpperCamelCase   :						List[Any]    =							os.path.join(lowercase_		,VOCAB_FILES_NAMES["merges_file"]      )
      shutil.copyfile(lowercase_		,lowercase_      )

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

      _UpperCamelCase   :						str    =							{
          "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
      _UpperCamelCase   :						Tuple    =							os.path.join(lowercase_		,lowercase_      )

      _UpperCamelCase   :						Tuple    =							{
          "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
      _UpperCamelCase   :						int    =							chkpt["model"]

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

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

      _UpperCamelCase   :						List[Any]    =							BioGptConfig.from_pretrained(lowercase_      )
      _UpperCamelCase   :						List[str]    =							BioGptForCausalLM(lowercase_      )

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

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

      print("Conversion is done!"      )


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







"""simple docstring"""


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      for _ in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
      return lrs




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[]
      for step in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        _UpperCamelCase   :						List[str]    =							os.path.join(lowercase_		,"schedule.bin"      )
                        torch.save(scheduler.state_dict()		,lowercase_      )

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							Any       , __a :							List[str]						)		->					List[str]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)


  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 1e-3)       , clip_threshold=1.0       , decay_rate=-0.8       , betaa=__a       , weight_decay=0.0       , relative_step=__a       , scale_parameter=__a       , warmup_init=__a       , )
        for _ in range(1000						):
              _UpperCamelCase   :						Optional[Any]    =							criterion(__a       , __a						)
              loss.backward()
              optimizer.step()
              w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
              w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()       , [0.4, 0.2, -0.5]       , tol=1e-2						)





@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						Optional[Any]    =							{
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"num_warmup_steps": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, "num_cycles": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
                [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
            ),
            get_inverse_sqrt_schedule: (
                {"num_warmup_steps": 2},
                [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
            ),
        }

        for scheduler_func, data in scheds.items():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              if scheduler_func.__name__ != "get_constant_schedule":
                    LambdaScheduleWrapper.wrap_scheduler(__a						)  # wrap to test picklability of the schedule
              _UpperCamelCase   :						int    =							unwrap_and_save_reload_schedule(__a       , self.num_steps						)
              self.assertListEqual(__a       , __a       , msg=F'''failed for {scheduler_func} in save and reload'''						)





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
