






'''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()

SCREAMING_SNAKE_CASE__      =   2






class 				a_						:

  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			,		)  ->      Dict:



        """simple docstring"""






        UpperCamelCase				  =					bos, unk, pad, eos
        UpperCamelCase				  =					[]
        UpperCamelCase				  =					[]
        UpperCamelCase				  =					{}
        UpperCamelCase				  =					self.add_symbol(A__      )
        UpperCamelCase				  =					self.add_symbol(A__      )
        UpperCamelCase				  =					self.add_symbol(A__      )
        UpperCamelCase				  =					self.add_symbol(A__      )
        if extra_special_symbols:
              for s in extra_special_symbols:
                    self.add_symbol(A__      )
        UpperCamelCase				  =					len(self.symbols      )

  def __eq__(      self			,		_SCREAMING_SNAKE_CASE      )  ->      Union[str, Any]:



        """simple docstring"""






        return self.indices == other.indices

  def __getitem__(      self			,		_SCREAMING_SNAKE_CASE      )  ->      List[str]:



        """simple docstring"""






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

  def __len__(      self      )  ->      Union[str, Any]:



        """simple docstring"""






        return len(self.symbols      )

  def __contains__(      self			,		_SCREAMING_SNAKE_CASE      )  ->      Union[str, Any]:



        """simple docstring"""






        return sym in self.indices

  @classmethod
  def   A__						(      cls			,		_SCREAMING_SNAKE_CASE      )  ->      str:



        """simple docstring"""






        UpperCamelCase				  =					cls()
        d.add_from_file(A__      )
        return d

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=1			,		_SCREAMING_SNAKE_CASE=False      )  ->      int:



        """simple docstring"""






        if word in self.indices and not overwrite:
              UpperCamelCase				  =					self.indices[word]
              UpperCamelCase				  =					self.count[idx] + n
              return idx
        else:
              UpperCamelCase				  =					len(self.symbols      )
              UpperCamelCase				  =					idx
              self.symbols.append(A__      )
              self.count.append(A__      )
              return idx

  def   A__						(      self			,		_SCREAMING_SNAKE_CASE      )  ->      List[str]:



        """simple docstring"""






        return 0


  def   A__						(      self			,		_SCREAMING_SNAKE_CASE      )  ->      str:



        """simple docstring"""






        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				  =					f.readlines()
        UpperCamelCase				  =					self._load_meta(A__      )

        for line in lines[indices_start_line:]:
              try:
                    UpperCamelCase				  =					line.rstrip().rsplit(""" """			,		1      )
                    if field == "#fairseq:overwrite":
                          UpperCamelCase				  =					True
                          UpperCamelCase				  =					line.rsplit(""" """			,		1      )
                    else:
                          UpperCamelCase				  =					False
                    UpperCamelCase				  =					int(A__      )
                    UpperCamelCase				  =					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__ (      __UpperCamelCase    )-> List[Any]:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      UpperCamelCase				  =					dict((re.sub(R"""@@$"""		,						""""""		,						lowercase_    ), v) if k.endswith("""@@"""    ) else (re.sub(R"""$"""		,						"""</w>"""		,						lowercase_    ), v) for k, v in d.items()    )
      UpperCamelCase				  =					"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F"{k}</w>"]
            UpperCamelCase				  =					d[k]  # restore
      return da







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> Tuple:
      # prep
      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				  =					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				  =					torch.load(lowercase_		,						map_location="""cpu"""    )

      UpperCamelCase				  =					chkpt["""cfg"""]["""model"""]

      # dicts
      UpperCamelCase				  =					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				  =					Dictionary.load(lowercase_    )
      UpperCamelCase				  =					rewrite_dict_keys(src_dict.indices    )
      UpperCamelCase				  =					len(lowercase_    )
      UpperCamelCase				  =					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				  =					os.path.join(lowercase_		,						"""bpecodes"""    )
      if not os.path.isfile(lowercase_    ):
            raise ValueError(F"path to the file {bpecodes_file} does not exist!"    )

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

      # model config
      UpperCamelCase				  =					os.path.join(lowercase_		,						"""config.json"""    )

      UpperCamelCase				  =					{
          """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				  =					os.path.join(lowercase_		,						lowercase_    )

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

      # model
      UpperCamelCase				  =					chkpt["""model"""]

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

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

      UpperCamelCase				  =					BioGptConfig.from_pretrained(lowercase_    )
      UpperCamelCase				  =					BioGptForCausalLM(lowercase_    )

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

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

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


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



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



