





'''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()

lowercase__		:     Dict			=							2
class 							__lowerCAmelCase	:






       """simple docstring"""





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

            '''simple docstring'''




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





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

            '''simple docstring'''




            return self.indices == other.indices





       def __getitem__(				self  :						List[Any]			,					lowerCAmelCase__  :						int	)       ->	Tuple:

            '''simple docstring'''




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





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

            '''simple docstring'''




            return len(self.symbols	)





       def __contains__(				self  :						Optional[int]			,					lowerCAmelCase__  :						List[Any]	)       ->	List[Any]:

            '''simple docstring'''




            return sym in self.indices





       @classmethod
       def snake_case__		(				cls  :						Any			,					lowerCAmelCase__  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			cls()
            d.add_from_file(lowerCAmelCase__	)
            return d





       def snake_case__		(				self  :						Optional[int]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int=1			,					lowerCAmelCase__  :						Union[str, Any]=False	)       ->	str:

            '''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(lowerCAmelCase__	)
                 self.count.append(lowerCAmelCase__	)
                 return idx





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Optional[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            return 0







       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




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

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

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







def    a__							(							lowercase   :	Dict )						->						List[Any]:





     """simple docstring"""

     _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    a__							(							lowercase   :	Any,   lowercase   :	int )						->						Any:





     """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							       =			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''': 0.0_2,
         '''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__":
   lowercase__		:     Optional[Any]			=							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.'
   )
   lowercase__		:     List[str]			=							parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	int									=       OpenAIGPTTokenizer
       _snake_case      :	Tuple									=       OpenAIGPTTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Optional[int]									=       False





       def snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _UpperCamelCase							       =			[
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''w</w>''',
                '''r</w>''',
                '''t</w>''',
                '''lo''',
                '''low''',
                '''er</w>''',
                '''low</w>''',
                '''lowest</w>''',
                '''newer</w>''',
                '''wider</w>''',
                '''<unk>''',
            ]
            _UpperCamelCase							       =			dict(zip(lowerCAmelCase__			,					range(len(lowerCAmelCase__	)	)	)	)
            _UpperCamelCase							       =			['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''vocab_file''']	)
            _UpperCamelCase							       =			os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['''merges_file''']	)
            with open(self.vocab_file			,					'''w'''	) as fp:
                 fp.write(json.dumps(lowerCAmelCase__	)	)
            with open(self.merges_file			,					'''w'''	) as fp:
                 fp.write('''\n'''.join(lowerCAmelCase__	)	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            return "lower newer", "lower newer"





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

            '''simple docstring'''




            _UpperCamelCase							       =			OpenAIGPTTokenizer(self.vocab_file			,					self.merges_file	)

            _UpperCamelCase							       =			'''lower'''
            _UpperCamelCase							       =			['''low''', '''er</w>''']
            _UpperCamelCase							       =			tokenizer.tokenize(lowerCAmelCase__	)
            self.assertListEqual(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			tokens + ['''<unk>''']
            _UpperCamelCase							       =			[14, 15, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Tuple=15	)       ->	Union[str, Any]:

            '''simple docstring'''




            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
                      _UpperCamelCase							       =			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)

                      # Simple input
                      _UpperCamelCase							       =			'''This is a simple input'''
                      _UpperCamelCase							       =			['''This is a simple input 1''', '''This is a simple input 2''']
                      _UpperCamelCase							       =			('''This is a simple input''', '''This is a pair''')
                      _UpperCamelCase							       =			[
                          ('''This is a simple input 1''', '''This is a simple input 2'''),
                          ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                      ]

                      # Simple input tests
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Simple input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(lowerCAmelCase__			,					tokenizer_r.encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''	)

                      # Pair input
                      self.assertRaises(
                          lowerCAmelCase__			,					tokenizer_r.batch_encode_plus			,					lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					padding='''max_length'''			,					)







       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            pass

@require_ftfy
@require_spacy
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       pass



