
import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: List[Any]=None			,							UpperCamelCase__			: Union[str, Any]=True			,							UpperCamelCase__			: str=None			,							**UpperCamelCase__			: List[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       config_class
    __magic_name__				     =       has_text_modality
    __magic_name__				     =       kwargs
    __magic_name__				     =       common_properties
  def 							_lowercase							( self			: List[Any]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict    )
    __magic_name__				     =       (
        ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
        if self.common_properties is None
        else self.common_properties
    )

    # Add common fields for text models
    if self.has_text_modality:
      common_properties.extend(["""vocab_size"""]    )

    # Test that config has the common properties as getters
    for prop in common_properties:
      self.parent.assertTrue(hasattr(UpperCamelCase__			,							UpperCamelCase__    )			,							msg=F'''`{prop}` does not exist'''    )

    # Test that config has the common properties as setter
    for idx, name in enumerate(UpperCamelCase__    ):
      try:
        setattr(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
        self.parent.assertEqual(
            getattr(UpperCamelCase__			,							UpperCamelCase__    )			,							UpperCamelCase__			,							msg=F'''`{name} value {idx} expected, but was {getattr(UpperCamelCase__			,							UpperCamelCase__    )}'''    )
      except NotImplementedError:
        # Some models might not be able to implement setters for common_properties
        # In that case, a NotImplementedError is raised
        pass

        # Test if config class can be called with Config(prop_name=..)
    for idx, name in enumerate(UpperCamelCase__    ):
      try:
        __magic_name__				     =       self.config_class(**{name: idx}    )
        self.parent.assertEqual(
            getattr(UpperCamelCase__			,							UpperCamelCase__    )			,							UpperCamelCase__			,							msg=F'''`{name} value {idx} expected, but was {getattr(UpperCamelCase__			,							UpperCamelCase__    )}'''    )
      except NotImplementedError:
        # Some models might not be able to implement setters for common_properties
        # In that case, a NotImplementedError is raised
        pass
  def 							_lowercase							( self			: List[str]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict    )
    __magic_name__				     =       json.loads(config.to_json_string()    )
    for key, value in self.inputs_dict.items():
      self.parent.assertEqual(obj[key]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Tuple    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict    )

    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__				     =       os.path.join(UpperCamelCase__			,							"""config.json"""    )
      config_first.to_json_file(UpperCamelCase__    )
      __magic_name__				     =       self.config_class.from_json_file(UpperCamelCase__    )

    self.parent.assertEqual(config_second.to_dict()			,							config_first.to_dict()    )
  def 							_lowercase							( self			: int    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict    )

    with tempfile.TemporaryDirectory() as tmpdirname:
      config_first.save_pretrained(UpperCamelCase__    )
      __magic_name__				     =       self.config_class.from_pretrained(UpperCamelCase__    )

    self.parent.assertEqual(config_second.to_dict()			,							config_first.to_dict()    )
  def 							_lowercase							( self			: str    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict    )

    __magic_name__				     =       """test"""
    with tempfile.TemporaryDirectory() as tmpdirname:
      __magic_name__				     =       os.path.join(UpperCamelCase__			,							UpperCamelCase__    )
      config_first.save_pretrained(UpperCamelCase__    )
      __magic_name__				     =       self.config_class.from_pretrained(UpperCamelCase__			,							subfolder=UpperCamelCase__    )

    self.parent.assertEqual(config_second.to_dict()			,							config_first.to_dict()    )
  def 							_lowercase							( self			: List[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.config_class(**self.inputs_dict			,							num_labels=5    )
    self.parent.assertEqual(len(config.idalabel    )			,							5    )
    self.parent.assertEqual(len(config.labelaid    )			,							5    )

    __magic_name__				     =       3
    self.parent.assertEqual(len(config.idalabel    )			,							3    )
    self.parent.assertEqual(len(config.labelaid    )			,							3    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[Any]:







    """simple docstring"""






    if self.config_class.is_composition:
      return
    __magic_name__				     =       self.config_class()
    self.parent.assertIsNotNone(UpperCamelCase__    )
  def 							_lowercase							( self			: Optional[int]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       copy.deepcopy(UpperCamelCase__    )
    __magic_name__				     =       self.config_class(**UpperCamelCase__    )
    __magic_name__				     =       []
    for key, value in config_common_kwargs.items():
      if key == "torch_dtype":
        if not is_torch_available():
          continue
        else:
          import torch

          if config.torch_dtype != torch.floataa:
            wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)    )
      elif getattr(UpperCamelCase__			,							UpperCamelCase__    ) != value:
        wrong_values.append((key, getattr(UpperCamelCase__			,							UpperCamelCase__    ), value)    )

    if len(UpperCamelCase__    ) > 0:
      __magic_name__				     =       """\n""".join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]    )
      raise ValueError(F'''The following keys were not properly set in the config:\n{errors}'''    )





  def 							_lowercase							( self			: Optional[int]    )			->	List[Any]:







    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.create_and_test_config_to_json_string()
    self.create_and_test_config_to_json_file()
    self.create_and_test_config_from_and_save_pretrained()
    self.create_and_test_config_from_and_save_pretrained_subfolder()
    self.create_and_test_config_with_num_labels()
    self.check_config_can_be_init_without_params()
    self.check_config_arguments_init()




"""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()

_a       =							2




class 					_lowerCAmelCase  :







 """simple docstring"""







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


 def __eq__(			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return self.indices == other.indices


 def __getitem__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str]	):
    if idx < len(self.symbols	):
       return self.symbols[idx]
    return self.unk_word


 def __len__(			self							:			str	):
    return len(self.symbols	)


 def __contains__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    return sym in self.indices


 @classmethod
 def 					_lowercase (			cls							:			List[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						cls()
    d.add_from_file(UpperCAmelCase__	)
    return d


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=1,					UpperCAmelCase__							:			str=False	):
    if word in self.indices and not overwrite:
       __lowercase								=						self.indices[word]
       __lowercase								=						self.count[idx] + n
       return idx
    else:
       __lowercase								=						len(self.symbols	)
       __lowercase								=						idx
       self.symbols.append(UpperCAmelCase__	)
       self.count.append(UpperCAmelCase__	)
       return idx


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str	):
    return 0



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[Any]	):
    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

    __lowercase								=						f.readlines()
    __lowercase								=						self._load_meta(UpperCAmelCase__	)

    for line in lines[indices_start_line:]:
       try:
          __lowercase		,__lowercase								=						line.rstrip().rsplit(" ",					1	)
          if field == "#fairseq:overwrite":
             __lowercase								=						True
             __lowercase		,__lowercase								=						line.rsplit(" ",					1	)
          else:
             __lowercase								=						False
          __lowercase								=						int(UpperCAmelCase__	)
          __lowercase								=						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   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						List[Any]:
   '''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

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

   __lowercase								=						chkpt["cfg"]["model"]

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

   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["merges_file"])
   shutil.copyfile(UpperCamelCase_,			UpperCamelCase_)

   # model config
   __lowercase								=						os.path.join(UpperCamelCase_,			"config.json")

   __lowercase								=						{
       "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
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						{
       "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
   __lowercase								=						chkpt["model"]

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

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

   __lowercase								=						BioGptConfig.from_pretrained(UpperCamelCase_)
   __lowercase								=						BioGptForCausalLM(UpperCamelCase_)

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

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

   print("Conversion is done!")


if __name__ == "__main__":
   _a       =							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       =							parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




