


"""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  SCREAMING_SNAKE_CASE__							:
       """simple docstring"""


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






         """simple docstring"""





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

       def __eq__(		self						,	snake_case__		):






         """simple docstring"""





         return self.indices == other.indices

       def __getitem__(		self						,	snake_case__		):






         """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						,	snake_case__		):






         """simple docstring"""





         return sym in self.indices

       @classmethod
       def 			lowercase__				(		cls						,	snake_case__		):






         """simple docstring"""





         lowerCAmelCase	:	Dict								=				cls()
         d.add_from_file(__a		)
         return d

       def 			lowercase__				(		self						,	snake_case__						,	snake_case__=1						,	snake_case__=False		):






         """simple docstring"""





         if word in self.indices and not overwrite:
           lowerCAmelCase	:	List[Any]								=				self.indices[word]
           lowerCAmelCase	:	Union[str, Any]								=				self.count[idx] + n
           return idx
         else:
           lowerCAmelCase	:	Dict								=				len(self.symbols		)
           lowerCAmelCase	:	List[str]								=				idx
           self.symbols.append(__a		)
           self.count.append(__a		)
           return idx

       def 			lowercase__				(		self						,	snake_case__		):






         """simple docstring"""





         return 0







       def 			lowercase__				(		self						,	snake_case__		):






         """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	:	Dict								=				f.readlines()
         lowerCAmelCase	:	List[str]								=				self._load_meta(__a		)

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

def    a__     (							SCREAMING_SNAKE_CASE       :       int							):


  '''simple docstring'''







  lowerCAmelCase	:	int								=				dict((re.sub(r"@@$"							, ""							, lowercase_							), v) if k.endswith("@@"							) else (re.sub(r"$"							, "</w>"							, lowercase_							), v) for k, v in d.items()							)
  lowerCAmelCase	:	Optional[int]								=				"<s> <pad> </s> <unk>".split()
  # restore the special tokens
  for k in keep_keys:
    del da[f"""{k}</w>"""]
    lowerCAmelCase	:	List[str]								=				d[k]  # restore
  return da
def    a__     (							SCREAMING_SNAKE_CASE       :       Optional[Any]							, SCREAMING_SNAKE_CASE       :       Union[str, 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

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

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

  # dicts
  lowerCAmelCase	:	int								=				os.path.join(lowercase_							, "dict.txt"							)
  if not os.path.isfile(lowercase_							):
    raise ValueError(f"""path to the file {dict_file} does not exist!"""							)
  lowerCAmelCase	:	Tuple								=				Dictionary.load(lowercase_							)
  lowerCAmelCase	:	str								=				rewrite_dict_keys(src_dict.indices							)
  lowerCAmelCase	:	Union[str, Any]								=				len(lowercase_							)
  lowerCAmelCase	:	Tuple								=				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)
  lowerCAmelCase	:	str								=				os.path.join(lowercase_							, "bpecodes"							)
  if not os.path.isfile(lowercase_							):
    raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""							)

  lowerCAmelCase	:	List[Any]								=				os.path.join(lowercase_							, VOCAB_FILES_NAMES["merges_file"]							)
  shutil.copyfile(lowercase_							, lowercase_							)

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

  lowerCAmelCase	:	str								=				{
      "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-1_2,
      "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
  lowerCAmelCase	:	Tuple								=				os.path.join(lowercase_							, lowercase_							)

  lowerCAmelCase	:	Tuple								=				{
      "bos_token": "<s>",
      "eos_token": "</s>",
      "model_max_length": 1_0_2_4,
      "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
  lowerCAmelCase	:	int								=				chkpt["model"]

  # remove unneeded keys
  lowerCAmelCase	:	List[str]								=				[
      "decoder.version",
  ]
  for k in ignore_keys:
    model_state_dict.pop(lowercase_							, lowercase_							)

  lowerCAmelCase	:	Dict								=				list(model_state_dict.keys()							)
  for layer_name in layer_names:
    if layer_name.endswith("output_projection.weight"							):
      lowerCAmelCase	:	List[Any]								=				model_state_dict.pop(lowercase_							)
    else:
      lowerCAmelCase	:	Dict								=				model_state_dict.pop(lowercase_							)

  lowerCAmelCase	:	List[Any]								=				BioGptConfig.from_pretrained(lowercase_							)
  lowerCAmelCase	:	List[str]								=				BioGptForCausalLM(lowercase_							)

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

  # save
  lowerCAmelCase	:	Dict								=				os.path.join(lowercase_							, lowercase_							)
  print(f"""Generating {pytorch_weights_dump_path}"""							)
  torch.save(lowercase_							, lowercase_							)

  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"""


import re




def        lowercase__     (       lowercase_      )       ->		bool:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							re.compile(
          r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"      )

      return bool(re.search(lowercase_		,lowercase_      )      )


if __name__ == "__main__":
  lowerCamelCase__						= "0094702343221"

  print(is_sri_lankan_phone_number(phone))
