



'''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        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,*,  # begin keyword-only arguments
      __UpperCAmelCase="<s>"  ,__UpperCAmelCase="<pad>"  ,__UpperCAmelCase="</s>"  ,__UpperCAmelCase="<unk>"  ,__UpperCAmelCase=None  ,)    ->		int:
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					bos, unk, pad, eos
      lowerCAmelCase__ :  Dict							      =					[]
      lowerCAmelCase__ :  Tuple							      =					[]
      lowerCAmelCase__ :  Dict							      =					{}
      lowerCAmelCase__ :  int							      =					self.add_symbol(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[str]							      =					self.add_symbol(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					self.add_symbol(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.add_symbol(__UpperCAmelCase      )
      if extra_special_symbols:
          for s in extra_special_symbols:
              self.add_symbol(__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					len(self.symbols      )




  def __eq__(    self  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      return self.indices == other.indices




  def __getitem__(    self  ,__UpperCAmelCase      )    ->		int:
      if idx < len(self.symbols      ):
          return self.symbols[idx]
      return self.unk_word




  def __len__(    self      )    ->		List[str]:
      return len(self.symbols      )




  def __contains__(    self  ,__UpperCAmelCase      )    ->		Optional[int]:
      return sym in self.indices




  @classmethod
  def 							UpperCAmelCase_	(    cls  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[Any]							      =					cls()
      d.add_from_file(__UpperCAmelCase      )
      return d




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=1  ,__UpperCAmelCase=False      )    ->		List[Any]:
      if word in self.indices and not overwrite:
          lowerCAmelCase__ :  Tuple							      =					self.indices[word]
          lowerCAmelCase__ :  Optional[Any]							      =					self.count[idx] + n
          return idx
      else:
          lowerCAmelCase__ :  str							      =					len(self.symbols      )
          lowerCAmelCase__ :  Tuple							      =					idx
          self.symbols.append(__UpperCAmelCase      )
          self.count.append(__UpperCAmelCase      )
          return idx




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      return 0






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[str]:
      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

      lowerCAmelCase__ :  int							      =					f.readlines()
      lowerCAmelCase__ :  int							      =					self._load_meta(__UpperCAmelCase      )

      for line in lines[indices_start_line:]:
          try:
              lowerCAmelCase__      ,   lowerCAmelCase__ :  Tuple							      =					line.rstrip().rsplit(""" """  ,1      )
              if field == "#fairseq:overwrite":
                  lowerCAmelCase__ :  List[str]							      =					True
                  lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					line.rsplit(""" """  ,1      )
              else:
                  lowerCAmelCase__ :  Tuple							      =					False
              lowerCAmelCase__ :  Any							      =					int(__UpperCAmelCase      )
              lowerCAmelCase__ :  List[Any]							      =					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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					dict((re.sub(R"""@@$"""							,    """"""							,    UpperCamelCase			), v) if k.endswith("""@@"""			) else (re.sub(R"""$"""							,    """</w>"""							,    UpperCamelCase			), 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    _SCREAMING_SNAKE_CASE  (       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__ :  int							      =					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__ :  Union[str, Any]							      =					torch.load(UpperCamelCase							,    map_location="""cpu"""			)

    lowerCAmelCase__ :  Optional[int]							      =					chkpt["""cfg"""]["""model"""]

    # dicts
    lowerCAmelCase__ :  str							      =					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__ :  Any							      =					Dictionary.load(UpperCamelCase			)
    lowerCAmelCase__ :  Any							      =					rewrite_dict_keys(src_dict.indices			)
    lowerCAmelCase__ :  str							      =					len(UpperCamelCase			)
    lowerCAmelCase__ :  int							      =					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__ :  Optional[Any]							      =					os.path.join(UpperCamelCase							,    """bpecodes"""			)
    if not os.path.isfile(UpperCamelCase			):
        raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""			)

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

    # model config
    lowerCAmelCase__ :  Optional[Any]							      =					os.path.join(UpperCamelCase							,    """config.json"""			)

    lowerCAmelCase__ :  Optional[int]							      =					{
        """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
    lowerCAmelCase__ :  str							      =					os.path.join(UpperCamelCase							,    UpperCamelCase			)

    lowerCAmelCase__ :  Tuple							      =					{
        """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__ :  Union[str, Any]							      =					chkpt["""model"""]

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

    lowerCAmelCase__ :  List[str]							      =					list(model_state_dict.keys()			)
    for layer_name in layer_names:
        if layer_name.endswith("""output_projection.weight"""			):
            lowerCAmelCase__ :  Any							      =					model_state_dict.pop(UpperCamelCase			)
        else:
            lowerCAmelCase__ :  Any							      =					model_state_dict.pop(UpperCamelCase			)

    lowerCAmelCase__ :  Union[str, Any]							      =					BioGptConfig.from_pretrained(UpperCamelCase			)
    lowerCAmelCase__ :  Dict							      =					BioGptForCausalLM(UpperCamelCase			)

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

    # save
    lowerCAmelCase__ :  Any							      =					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'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase      						=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
 import torch

 print('''Torch version:''', torch.__version__)
 print('''Cuda available:''', torch.cuda.is_available())
 print('''Cuda version:''', torch.version.cuda)
 print('''CuDNN version:''', torch.backends.cudnn.version())
 print('''Number of GPUs available:''', torch.cuda.device_count())
 print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
 print('''Torch version:''', None)

try:
 import deepspeed

 print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
 print('''DeepSpeed version:''', None)

try:
 import tensorflow as tf

 print('''TensorFlow version:''', tf.__version__)
 print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
 print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
 print('''TensorFlow version:''', None)


