

'''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    A__ :



    def __init__(   self						,			*,  # begin keyword-only arguments
        UpperCamelCase__="<s>"						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="</s>"						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__=None						,			)  -> Union[str, Any]:





           '''simple docstring'''
           A_   ,     A_   ,     A_   ,     A_		   = bos, unk, pad, eos
           A_		   = []
           A_		   = []
           A_		   = {}
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           A_		   = self.add_symbol(UpperCamelCase__    )
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(UpperCamelCase__    )
           A_		   = len(self.symbols    )



    def __eq__(   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return self.indices == other.indices



    def __getitem__(   self						,			UpperCamelCase__    )  -> List[Any]:





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



    def __len__(   self    )  -> List[str]:





           '''simple docstring'''
           return len(self.symbols    )



    def __contains__(   self						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           return sym in self.indices



    @classmethod
    def    snake_case_     (   cls						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = cls()
           d.add_from_file(UpperCamelCase__    )
           return d



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__=1						,			UpperCamelCase__=False    )  -> Tuple:





           '''simple docstring'''
           if word in self.indices and not overwrite:
                  A_		   = self.indices[word]
                  A_		   = self.count[idx] + n
                  return idx
           else:
                  A_		   = len(self.symbols    )
                  A_		   = idx
                  self.symbols.append(UpperCamelCase__    )
                  self.count.append(UpperCamelCase__    )
                  return idx



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> int:





           '''simple docstring'''
           return 0







    def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           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

           A_		   = f.readlines()
           A_		   = self._load_meta(UpperCamelCase__    )

           for line in lines[indices_start_line:]:
                  try:
                         A_   ,     A_		   = line.rstrip().rsplit(""" """						,			1    )
                         if field == "#fairseq:overwrite":
                                A_		   = True
                                A_   ,     A_		   = line.rsplit(""" """						,			1    )
                         else:
                                A_		   = False
                         A_		   = int(UpperCamelCase__    )
                         A_		   = 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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Union[str, 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}
       A_		   = dict((re.sub(r"""@@$""",    """""",    UpperCAmelCase__			), v) if k.endswith("""@@"""			) else (re.sub(r"""$""",    """</w>""",    UpperCAmelCase__			), v) for k, v in d.items()			)
       A_		   = """<s> <pad> </s> <unk>""".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'''{k}</w>''']
              A_		   = d[k]  # restore
       return da
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Any:
       # prep
       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

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

       A_		   = chkpt["""cfg"""]["""model"""]

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

       A_		   = os.path.join(UpperCAmelCase__,    VOCAB_FILES_NAMES["""merges_file"""]			)
       shutil.copyfile(UpperCAmelCase__,    UpperCAmelCase__			)

       # model config
       A_		   = os.path.join(UpperCAmelCase__,    """config.json"""			)

       A_		   = {
           """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
       A_		   = os.path.join(UpperCAmelCase__,    UpperCAmelCase__			)

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

       # model
       A_		   = chkpt["""model"""]

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

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

       A_		   = BioGptConfig.from_pretrained(UpperCAmelCase__			)
       A_		   = BioGptForCausalLM(UpperCAmelCase__			)

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

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

       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 multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
      import sqlitea

      import sqlalchemy


class    A__ ( _snake_case     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = False						,			**UpperCamelCase__						,			)  -> Optional[Any]:





           '''simple docstring'''
           super().__init__(features=UpperCamelCase__						,			cache_dir=UpperCamelCase__						,			keep_in_memory=UpperCamelCase__						,			**UpperCamelCase__    )
           A_		   = Sql(
               cache_dir=UpperCamelCase__						,			features=UpperCamelCase__						,			sql=UpperCamelCase__						,			con=UpperCamelCase__						,			**UpperCamelCase__						,			)







    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = None
           A_		   = None
           A_		   = None
           A_		   = None

           self.builder.download_and_prepare(
               download_config=UpperCamelCase__						,			download_mode=UpperCamelCase__						,			verification_mode=UpperCamelCase__						,			base_path=UpperCamelCase__						,			)

           # Build dataset for splits
           A_		   = self.builder.as_dataset(
               split="""train"""						,			verification_mode=UpperCamelCase__						,			in_memory=self.keep_in_memory    )
           return dataset


class    A__ :



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> List[Any]:





           '''simple docstring'''
           if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''    )

           A_		   = dataset
           A_		   = name
           A_		   = con
           A_		   = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           A_		   = num_proc
           A_		   = to_sql_kwargs



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           A_		   = self.to_sql_kwargs.pop("""sql"""						,			UpperCamelCase__    )
           A_		   = self.to_sql_kwargs.pop("""con"""						,			UpperCamelCase__    )
           A_		   = self.to_sql_kwargs.pop("""index"""						,			UpperCamelCase__    )

           A_		   = self._write(index=UpperCamelCase__						,			**self.to_sql_kwargs    )
           return written



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





           '''simple docstring'''
           A_   ,     A_   ,     A_		   = args
           A_		   = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
           A_		   = query_table(
               table=self.dataset.data						,			key=slice(UpperCamelCase__						,			offset + self.batch_size    )						,			indices=self.dataset._indices						,			)
           A_		   = batch.to_pandas()
           A_		   = df.to_sql(self.name						,			self.con						,			index=UpperCamelCase__						,			**UpperCamelCase__    )
           return num_rows or len(UpperCamelCase__    )







    def    snake_case_     (   self						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> int:





           '''simple docstring'''
           A_		   = 0

           if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0						,			len(self.dataset    )						,			self.batch_size    )						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                         written += self._batch_sql((offset, index, to_sql_kwargs)    )
           else:
                  A_   ,     A_		   = len(self.dataset    ), self.batch_size
                  with multiprocessing.Pool(self.num_proc    ) as pool:
                         for num_rows in logging.tqdm(
                             pool.imap(
                                 self._batch_sql						,			[(offset, index, to_sql_kwargs) for offset in range(0						,			UpperCamelCase__						,			UpperCamelCase__    )]						,			)						,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                                written += num_rows

           return written
