



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()

a_						:     Tuple	     =		2





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			*,  # begin keyword-only arguments
         __a="<s>"				,			__a="<pad>"				,			__a="</s>"				,			__a="<unk>"				,			__a=None				,			):






           '''simple docstring'''



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







     def __eq__(self				,			__a    ):






           '''simple docstring'''



           return self.indices == other.indices







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



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







     def __len__(self    ):






           '''simple docstring'''



           return len(self.symbols    )







     def __contains__(self				,			__a    ):






           '''simple docstring'''



           return sym in self.indices







     @classmethod
     def 						_a      (cls				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		cls()
           d.add_from_file(__a    )
           return d







     def 						_a      (self				,			__a				,			__a=1				,			__a=False    ):






           '''simple docstring'''



           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(__a    )
                 self.count.append(__a    )
                 return idx







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return 0







     def 						_a      (self				,			__a    ):






           '''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

           lowerCamelCase								=		f.readlines()
           lowerCamelCase								=		self._load_meta(__a    )

           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(__a    )
                       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(__a    )    )
                       self.add_symbol(__a				,			n=__a				,			overwrite=__a    )
                 except ValueError:
                       raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"    )



def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		dict((re.sub(r"@@$"   ,   ""   ,   UpperCAmelCase__ ), v) if k.endswith("@@" ) else (re.sub(r"$"   ,   "</w>"   ,   UpperCAmelCase__ ), 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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      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

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

      lowerCamelCase								=		chkpt["cfg"]["model"]

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

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

      # model config
      lowerCamelCase								=		os.path.join(UpperCAmelCase__   ,   "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": 0.0_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(UpperCAmelCase__   ,   "w"   ,   encoding="utf-8" ) as f:
            f.write(json.dumps(UpperCAmelCase__   ,   ensure_ascii=UpperCAmelCase__   ,   indent=UpperCAmelCase__ ) )

      # tokenizer config
      lowerCamelCase								=		os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )

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

      # model
      lowerCamelCase								=		chkpt["model"]

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

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

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

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

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

      print("Conversion is done!" )


if __name__ == "__main__":
     a_						:     List[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.'
     )
     a_						:     Optional[Any]	     =		parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     Tuple	     =		{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     List[Any]	     =		[
         'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XmodForCausalLM',
         'XmodForMaskedLM',
         'XmodForMultipleChoice',
         'XmodForQuestionAnswering',
         'XmodForSequenceClassification',
         'XmodForTokenClassification',
         'XmodModel',
         'XmodPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xmod import (
              XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
              XmodForCausalLM,
              XmodForMaskedLM,
              XmodForMultipleChoice,
              XmodForQuestionAnswering,
              XmodForSequenceClassification,
              XmodForTokenClassification,
              XmodModel,
              XmodPreTrainedModel,
          )

else:
     import sys

     a_						:     str	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)