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)



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A     :  Optional[Any]					     =       16
A     :  Optional[Any]					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

      def tokenize_function(__UpperCamelCase				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_          =  tokenizer(examples["sentence1"]     ,      examples["sentence2"]     ,      truncation=__UpperCamelCase     ,      max_length=__UpperCamelCase				)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE_          =  datasets.map(
                __UpperCamelCase     ,      batched=__UpperCamelCase     ,      remove_columns=["idx", "sentence1", "sentence2"]     ,      )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      SCREAMING_SNAKE_CASE_          =  tokenized_datasets.rename_column("label"     ,      "labels"				)

      def collate_fn(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

            return tokenizer.pad(
                __UpperCamelCase     ,      padding="longest"     ,      max_length=__UpperCamelCase     ,      pad_to_multiple_of=__UpperCamelCase     ,      return_tensors="pt"     ,      )

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["train"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["validation"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A     :  Tuple					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "1":
            SCREAMING_SNAKE_CASE_          =  2
      # New Code #
      SCREAMING_SNAKE_CASE_          =  int(args.gradient_accumulation_steps				)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_          =  Accelerator(
          cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      gradient_accumulation_steps=__UpperCamelCase				)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_          =  config["lr"]
      SCREAMING_SNAKE_CASE_          =  int(config["num_epochs"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["seed"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["batch_size"]				)

      SCREAMING_SNAKE_CASE_          =  evaluate.load("glue"     ,      "mrpc"				)

      set_seed(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dataloaders(__UpperCamelCase     ,      __UpperCamelCase				)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_          =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,      return_dict=__UpperCamelCase				)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      SCREAMING_SNAKE_CASE_          =  model.to(accelerator.device				)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_          =  AdamW(params=model.parameters()     ,      lr=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      num_training_steps=(len(__UpperCamelCase				) * num_epochs)     ,      )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                        SCREAMING_SNAKE_CASE_          =  output.loss
                        accelerator.backward(__UpperCamelCase				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=__UpperCamelCase     ,      references=__UpperCamelCase     ,      )

            SCREAMING_SNAKE_CASE_          =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''     ,      __UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"     ,      type=__UpperCamelCase     ,      default=__UpperCamelCase     ,      choices=["no", "fp16", "bf16", "fp8"]     ,      help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU."     ,      )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"     ,      type=__UpperCamelCase     ,      default=1     ,      help="The number of minibatches to be ran before gradients are accumulated."     ,      )
      parser.add_argument("--cpu"     ,      action="store_true"     ,      help="If passed, will train on the CPU."				)
      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      SCREAMING_SNAKE_CASE_          =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()


