




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()

UpperCamelCase								=					2





class 			__lowerCamelCase	:




    """simple docstring"""

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


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


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


    def __len__(	self					:					Optional[Any]       )				->	Optional[Any]:
         return len(self.symbols       )


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


    @classmethod
    def        a				(	cls					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       )				->	Tuple:
         lowerCAmelCase__          =			cls()
         d.add_from_file(SCREAMING_SNAKE_CASE__       )
         return d


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


    def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Dict:
         return 0


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

         lowerCAmelCase__          =			f.readlines()
         lowerCAmelCase__          =			self._load_meta(SCREAMING_SNAKE_CASE__       )

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






def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

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

     # handle various types of models

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

     lowerCAmelCase__          =			chkpt["cfg"]["model"]

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

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

     lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    VOCAB_FILES_NAMES["merges_file"]      )
     shutil.copyfile(lowerCAmelCase_       ,    lowerCAmelCase_      )

     # model config
     lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    "config.json"      )

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

     # tokenizer config
     lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    lowerCAmelCase_      )

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

     # model
     lowerCAmelCase__          =			chkpt["model"]

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

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

     lowerCAmelCase__          =			BioGptConfig.from_pretrained(lowerCAmelCase_      )
     lowerCAmelCase__          =			BioGptForCausalLM(lowerCAmelCase_      )

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

     # save
     lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    lowerCAmelCase_      )
     print(F'Generating {pytorch_weights_dump_path}'      )
     torch.save(lowerCAmelCase_       ,    lowerCAmelCase_      )

     print("Conversion is done!"      )


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






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase								=					'__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase								=					'Dummy User'
UpperCamelCase								=					'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'

UpperCamelCase								=					'https://hub-ci.huggingface.co'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
UpperCamelCase								=					Path('~/.huggingface/hub_ci_token').expanduser()



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     monkeypatch.setattr("datasets.config.HF_ENDPOINT"       ,    lowerCAmelCase_      )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     HfFolder.save_token(lowerCAmelCase_      )
     yield
     HfFolder.delete_token()



@pytest.fixture(scope="session"      )
def 							_A       (						):
     """simple docstring"""

     return HfApi(endpoint=lowerCAmelCase_      )



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi      ):
     """simple docstring"""

     lowerCAmelCase__          =			HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase_      )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     def _cleanup_repo(lowerCAmelCase_					: Optional[Any]      ):
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )

     return _cleanup_repo



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     @contextmanager
     def _temporary_repo(lowerCAmelCase_					: str      ):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase_      )

     return _temporary_repo



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data/text_data.txt"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_img_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_img_data_
