


"""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						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							bos, unk, pad, eos
         lowerCAmelCase					:					List[str]								=							[]
         lowerCAmelCase					:					Optional[int]								=							[]
         lowerCAmelCase					:					Optional[Any]								=							{}
         lowerCAmelCase					:					List[Any]								=							self.add_symbol(snake_case__						)
         lowerCAmelCase					:					str								=							self.add_symbol(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.add_symbol(snake_case__						)
         lowerCAmelCase					:					Dict								=							self.add_symbol(snake_case__						)
         if extra_special_symbols:
           for s in extra_special_symbols:
             self.add_symbol(snake_case__						)
         lowerCAmelCase					:					Union[str, 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					:					List[str]								=							cls()
         d.add_from_file(snake_case__						)
         return d

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






         """simple docstring"""





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

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         return 0







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase					:					List[str]								=							f.readlines()
         lowerCAmelCase					:					Any								=							self._load_meta(snake_case__						)

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

def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







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


  '''simple docstring'''







  if not os.path.exists(SCREAMING_SNAKE_CASE    ):
    raise ValueError(f"""path {biogpt_checkpoint_path} does not exist!"""    )
  os.makedirs(SCREAMING_SNAKE_CASE    ,				exist_ok=SCREAMING_SNAKE_CASE    )
  print(f"""Writing results to {pytorch_dump_folder_path}"""    )

  # handle various types of models

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

  lowerCAmelCase					:					Tuple								=							chkpt["cfg"]["model"]

  # dicts
  lowerCAmelCase					:					Optional[int]								=							os.path.join(SCREAMING_SNAKE_CASE    ,				"dict.txt"    )
  if not os.path.isfile(SCREAMING_SNAKE_CASE    ):
    raise ValueError(f"""path to the file {dict_file} does not exist!"""    )
  lowerCAmelCase					:					Optional[Any]								=							Dictionary.load(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Tuple								=							rewrite_dict_keys(src_dict.indices    )
  lowerCAmelCase					:					List[str]								=							len(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Dict								=							os.path.join(SCREAMING_SNAKE_CASE    ,				VOCAB_FILES_NAMES["vocab_file"]    )
  print(f"""Generating {src_vocab_file} of {src_vocab_size} records"""    )
  with open(SCREAMING_SNAKE_CASE    ,				"w"    ,				encoding="utf-8"    ) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE    ,				ensure_ascii=SCREAMING_SNAKE_CASE    ,				indent=SCREAMING_SNAKE_CASE    )    )

  # merges_file (bpecodes)
  lowerCAmelCase					:					List[str]								=							os.path.join(SCREAMING_SNAKE_CASE    ,				"bpecodes"    )
  if not os.path.isfile(SCREAMING_SNAKE_CASE    ):
    raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""    )

  lowerCAmelCase					:					str								=							os.path.join(SCREAMING_SNAKE_CASE    ,				VOCAB_FILES_NAMES["merges_file"]    )
  shutil.copyfile(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

  # model config
  lowerCAmelCase					:					Any								=							os.path.join(SCREAMING_SNAKE_CASE    ,				"config.json"    )

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

  # tokenizer config
  lowerCAmelCase					:					Tuple								=							os.path.join(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

  lowerCAmelCase					:					Any								=							{
      "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(SCREAMING_SNAKE_CASE    ,				"w"    ,				encoding="utf-8"    ) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE    ,				ensure_ascii=SCREAMING_SNAKE_CASE    ,				indent=SCREAMING_SNAKE_CASE    )    )

  # model
  lowerCAmelCase					:					List[Any]								=							chkpt["model"]

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

  lowerCAmelCase					:					int								=							list(model_state_dict.keys()    )
  for layer_name in layer_names:
    if layer_name.endswith("output_projection.weight"    ):
      lowerCAmelCase					:					Tuple								=							model_state_dict.pop(SCREAMING_SNAKE_CASE    )
    else:
      lowerCAmelCase					:					Dict								=							model_state_dict.pop(SCREAMING_SNAKE_CASE    )

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

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

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

  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"""







def  a__      (    SCREAMING_SNAKE_CASE   :   list[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					str								=							len(SCREAMING_SNAKE_CASE    )
  for i in range(SCREAMING_SNAKE_CASE    ):
    for j in range(i + 1    ,				SCREAMING_SNAKE_CASE    ):
      if numbers[j] < numbers[i]:
        lowerCAmelCase						,  lowerCAmelCase					:					Any								=							numbers[j], numbers[i]
  return numbers


if __name__ == "__main__":
     lowerCAmelCase__				    =       input('''Enter numbers separated by a comma:\n''').strip()
     lowerCAmelCase__				    =       [int(item) for item in user_input.split(''',''')]
     print(exchange_sort(unsorted))





