


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						:     Optional[Any]			,   *,  # begin keyword-only arguments
          lowercase_						:     Optional[Any]="<s>"			,   lowercase_						:     str="<pad>"			,   lowercase_						:     int="</s>"			,   lowercase_						:     List[Any]="<unk>"			,   lowercase_						:     Any=None			,   )					->						Any:
             """simple docstring"""




             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		str		=bos, unk, pad, eos
             _lowerCamelCase       :		Dict		=[]
             _lowerCamelCase       :		Tuple		=[]
             _lowerCamelCase       :		Union[str, Any]		={}
             _lowerCamelCase       :		Union[str, Any]		=self.add_symbol(lowercase_ )
             _lowerCamelCase       :		Tuple		=self.add_symbol(lowercase_ )
             _lowerCamelCase       :		List[str]		=self.add_symbol(lowercase_ )
             _lowerCamelCase       :		Tuple		=self.add_symbol(lowercase_ )
             if extra_special_symbols:
                    for s in extra_special_symbols:
                           self.add_symbol(lowercase_ )
             _lowerCamelCase       :		int		=len(self.symbols )






      def __eq__(					self						:     Dict			,   lowercase_						:     Optional[int] )					->						Any:
             """simple docstring"""




             return self.indices == other.indices






      def __getitem__(					self						:     Dict			,   lowercase_						:     List[Any] )					->						Optional[int]:
             """simple docstring"""




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






      def __len__(					self						:     Optional[Any] )					->						str:
             """simple docstring"""




             return len(self.symbols )






      def __contains__(					self						:     List[Any]			,   lowercase_						:     Optional[Any] )					->						Union[str, Any]:
             """simple docstring"""




             return sym in self.indices






      @classmethod
      def        lowerCamelCase       (					cls						:     Union[str, Any]			,   lowercase_						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Any		=cls()
             d.add_from_file(lowercase_ )
             return d






      def        lowerCamelCase       (					self						:     Any			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     List[Any]=1			,   lowercase_						:     Tuple=False )					->						int:
             """simple docstring"""




             if word in self.indices and not overwrite:
                    _lowerCamelCase       :		Optional[int]		=self.indices[word]
                    _lowerCamelCase       :		Any		=self.count[idx] + n
                    return idx
             else:
                    _lowerCamelCase       :		Dict		=len(self.symbols )
                    _lowerCamelCase       :		str		=idx
                    self.symbols.append(lowercase_ )
                    self.count.append(lowercase_ )
                    return idx






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     Union[str, Any] )					->						Optional[Any]:
             """simple docstring"""




             return 0






      def        lowerCamelCase       (					self						:     Optional[int]			,   lowercase_						:     List[str] )					->						List[Any]:
             """simple docstring"""




             if isinstance(lowercase_			,   lowercase_ ):
                    try:
                           with open(lowercase_			,   'r'			,   encoding='utf-8' ) as fd:
                                  self.add_from_file(lowercase_ )
                    except FileNotFoundError as fnfe:
                           raise fnfe
                    except UnicodeError:
                           raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowercase_ ) )
                    return

             _lowerCamelCase       :		Union[str, Any]		=f.readlines()
             _lowerCamelCase       :		Union[str, Any]		=self._load_meta(lowercase_ )

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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





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

       # handle various types of models

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

       _lowerCamelCase       :		Tuple		=chkpt['cfg']['model']

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

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

       _lowerCamelCase       :		List[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			VOCAB_FILES_NAMES['merges_file']     )
       shutil.copyfile(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       # model config
       _lowerCamelCase       :		str		=os.path.join(SCREAMING_SNAKE_CASE__		,			'config.json'     )

       _lowerCamelCase       :		Union[str, Any]		={
           '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(SCREAMING_SNAKE_CASE__		,			'w'		,			encoding='utf-8'     ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE__		,			ensure_ascii=SCREAMING_SNAKE_CASE__		,			indent=SCREAMING_SNAKE_CASE__     )     )

       # tokenizer config
       _lowerCamelCase       :		str		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Union[str, Any]		={
           '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(SCREAMING_SNAKE_CASE__		,			'w'		,			encoding='utf-8'     ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE__		,			ensure_ascii=SCREAMING_SNAKE_CASE__		,			indent=SCREAMING_SNAKE_CASE__     )     )

       # model
       _lowerCamelCase       :		Any		=chkpt['model']

       # remove unneeded keys
       _lowerCamelCase       :		Optional[Any]		=[
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Optional[int]		=list(model_state_dict.keys()     )
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'     ):
                     _lowerCamelCase       :		Tuple		=model_state_dict.pop(SCREAMING_SNAKE_CASE__     )
              else:
                     _lowerCamelCase       :		Any		=model_state_dict.pop(SCREAMING_SNAKE_CASE__     )

       _lowerCamelCase       :		Union[str, Any]		=BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=BioGptForCausalLM(SCREAMING_SNAKE_CASE__     )

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

       # save
       _lowerCamelCase       :		Any		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(F'''Generating {pytorch_weights_dump_path}'''     )
       torch.save(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       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)






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCamelCase											=       datasets.utils.logging.get_logger(__name__)

lowerCamelCase											=       ['names', 'prefix']
lowerCamelCase											=       ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCamelCase											=       ['encoding_errors', 'on_bad_lines']
lowerCamelCase											=       ['date_format']




@dataclass
class A  (   datasets.BuilderConfig     ):
      UpperCamelCase__							:  str      =","
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[Union[int, List[int], str]]      ="infer"
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[Union[int, str, List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[Union[List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[Literal["c", "python", "pyarrow"]]      =None
      UpperCamelCase__							:  Dict[Union[int, str], Callable[[Any], Any]]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[Union[int, List[int]]]      =None
      UpperCamelCase__							:  Optional[int]      =None
      UpperCamelCase__							:  Optional[Union[str, List[str]]]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ="."
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ='"'
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  int      =10000
      UpperCamelCase__							:  Optional[datasets.Features]      =None
      UpperCamelCase__							:  Optional[str]      ="strict"
      UpperCamelCase__							:  Literal["error", "warn", "skip"]      ="error"
      UpperCamelCase__							:  Optional[str]      =None






      def        lowerCamelCase       (					self						:     List[str] )					->						str:
             """simple docstring"""




             if self.delimiter is not None:
                    _lowerCamelCase       :		Union[str, Any]		=self.delimiter
             if self.column_names is not None:
                    _lowerCamelCase       :		Dict		=self.column_names






      @property
      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		={
                 'sep': self.sep,
                 'header': self.header,
                 'names': self.names,
                 'index_col': self.index_col,
                 'usecols': self.usecols,
                 'prefix': self.prefix,
                 'mangle_dupe_cols': self.mangle_dupe_cols,
                 'engine': self.engine,
                 'converters': self.converters,
                 'true_values': self.true_values,
                 'false_values': self.false_values,
                 'skipinitialspace': self.skipinitialspace,
                 'skiprows': self.skiprows,
                 'nrows': self.nrows,
                 'na_values': self.na_values,
                 'keep_default_na': self.keep_default_na,
                 'na_filter': self.na_filter,
                 'verbose': self.verbose,
                 'skip_blank_lines': self.skip_blank_lines,
                 'thousands': self.thousands,
                 'decimal': self.decimal,
                 'lineterminator': self.lineterminator,
                 'quotechar': self.quotechar,
                 'quoting': self.quoting,
                 'escapechar': self.escapechar,
                 'comment': self.comment,
                 'encoding': self.encoding,
                 'dialect': self.dialect,
                 'error_bad_lines': self.error_bad_lines,
                 'warn_bad_lines': self.warn_bad_lines,
                 'skipfooter': self.skipfooter,
                 'doublequote': self.doublequote,
                 'memory_map': self.memory_map,
                 'float_precision': self.float_precision,
                 'chunksize': self.chunksize,
                 'encoding_errors': self.encoding_errors,
                 'on_bad_lines': self.on_bad_lines,
                 'date_format': self.date_format,
             }

             # some kwargs must not be passed if they don't have a default value
             # some others are deprecated and we can also not pass them if they are the default value
             for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
                    if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,   lowercase_ ):
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 2):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

             return pd_read_csv_kwargs




class A  (   datasets.ArrowBasedBuilder     ):
      UpperCamelCase__							:  List[str]      =CsvConfig






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             return datasets.DatasetInfo(features=self.config.features )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             if not self.config.data_files:
                    raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
             _lowerCamelCase       :		int		=dl_manager.download_and_extract(self.config.data_files )
             if isinstance(lowercase_			,   (str, list, tuple) ):
                    _lowerCamelCase       :		List[Any]		=data_files
                    if isinstance(lowercase_			,   lowercase_ ):
                           _lowerCamelCase       :		Union[str, Any]		=[files]
                    _lowerCamelCase       :		Union[str, Any]		=[dl_manager.iter_files(lowercase_ ) for file in files]
                    return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,   gen_kwargs={'files': files} )]
             _lowerCamelCase       :		Optional[int]		=[]
             for split_name, files in data_files.items():
                    if isinstance(lowercase_			,   lowercase_ ):
                           _lowerCamelCase       :		Dict		=[files]
                    _lowerCamelCase       :		Optional[int]		=[dl_manager.iter_files(lowercase_ ) for file in files]
                    splits.append(datasets.SplitGenerator(name=lowercase_			,   gen_kwargs={'files': files} ) )
             return splits






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     pa.Table )					->						pa.Table:
             """simple docstring"""




             if self.config.features is not None:
                    _lowerCamelCase       :		List[str]		=self.config.features.arrow_schema
                    if all(not require_storage_cast(lowercase_ ) for feature in self.config.features.values() ):
                           # cheaper cast
                           _lowerCamelCase       :		Optional[int]		=pa.Table.from_arrays([pa_table[field.name] for field in schema]			,   schema=lowercase_ )
                    else:
                           # more expensive cast; allows str <-> int/float or str to Audio for example
                           _lowerCamelCase       :		List[Any]		=table_cast(lowercase_			,   lowercase_ )
             return pa_table






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.config.features.arrow_schema if self.config.features else None
             # dtype allows reading an int column as str
             _lowerCamelCase       :		Union[str, Any]		=(
                 {
                     name: dtype.to_pandas_dtype() if not require_storage_cast(lowercase_ ) else object
                     for name, dtype, feature in zip(schema.names			,   schema.types			,   self.config.features.values() )
                 }
                 if schema is not None
                 else None
             )
             for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_ ) ):
                    _lowerCamelCase       :		Union[str, Any]		=pd.read_csv(lowercase_			,   iterator=lowercase_			,   dtype=lowercase_			,   **self.config.pd_read_csv_kwargs )
                    try:
                           for batch_idx, df in enumerate(lowercase_ ):
                                  _lowerCamelCase       :		Tuple		=pa.Table.from_pandas(lowercase_ )
                                  # Uncomment for debugging (will print the Arrow table size and elements)
                                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                  yield (file_idx, batch_idx), self._cast_table(lowercase_ )
                    except ValueError as e:
                           logger.error(F'''Failed to read file \'{file}\' with error {type(lowercase_ )}: {e}''' )
                           raise


