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     :  List[Any]					     =       2


class   lowerCamelCase					:
  """simple docstring"""







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







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







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







  def __len__(							self :       Optional[Any]   )  ->	Optional[int]:
        return len(self.symbols   )







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







  @classmethod
  def    __A     (							cls :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  cls()
        d.add_from_file(__magic_name__   )
        return d







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       int=1  ,     __magic_name__ :       Dict=False   )  ->	Any:
        if word in self.indices and not overwrite:
              SCREAMING_SNAKE_CASE_          =  self.indices[word]
              SCREAMING_SNAKE_CASE_          =  self.count[idx] + n
              return idx
        else:
              SCREAMING_SNAKE_CASE_          =  len(self.symbols   )
              SCREAMING_SNAKE_CASE_          =  idx
              self.symbols.append(__magic_name__   )
              self.count.append(__magic_name__   )
              return idx







  def    __A     (							self :       int  ,     __magic_name__ :       Any   )  ->	Optional[int]:
        return 0







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]   )  ->	int:
        if isinstance(__magic_name__  ,     __magic_name__   ):
              try:
                    with open(__magic_name__  ,     "r"  ,     encoding="utf-8"   ) as fd:
                          self.add_from_file(__magic_name__   )
              except FileNotFoundError as fnfe:
                    raise fnfe
              except UnicodeError:
                    raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(__magic_name__   )   )
              return

        SCREAMING_SNAKE_CASE_          =  f.readlines()
        SCREAMING_SNAKE_CASE_          =  self._load_meta(__magic_name__   )

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



def      a__ (		__UpperCamelCase				):
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE_          =  dict((re.sub(r"@@$"     ,      ""     ,      __UpperCamelCase				), v) if k.endswith("@@"				) else (re.sub(r"$"     ,      "</w>"     ,      __UpperCamelCase				), v) for k, v in d.items()				)
      SCREAMING_SNAKE_CASE_          =  "<s> <pad> </s> <unk>".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE_          =  d[k]  # restore
      return da


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # prep
      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

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

      SCREAMING_SNAKE_CASE_          =  chkpt["cfg"]["model"]

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

      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      VOCAB_FILES_NAMES["merges_file"]				)
      shutil.copyfile(__UpperCamelCase     ,      __UpperCamelCase				)

      # model config
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      "config.json"				)

      SCREAMING_SNAKE_CASE_          =  {
          "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
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      __UpperCamelCase				)

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

      # model
      SCREAMING_SNAKE_CASE_          =  chkpt["model"]

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

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

      SCREAMING_SNAKE_CASE_          =  BioGptConfig.from_pretrained(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  BioGptForCausalLM(__UpperCamelCase				)

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

      # save
      SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      __UpperCamelCase				)
      print(F'''Generating {pytorch_weights_dump_path}'''				)
      torch.save(__UpperCamelCase     ,      __UpperCamelCase				)

      print("Conversion is done!"				)


if __name__ == "__main__":
      A     :  Dict					     =       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)



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


