



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_						:     Tuple     =		2





class    lowerCamelCase__  :
     """simple docstring"""



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






           '''simple docstring'''



           lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		bos, unk, pad, eos
           lowerCamelCase								=		[]
           lowerCamelCase								=		[]
           lowerCamelCase								=		{}
           lowerCamelCase								=		self.add_symbol(__a    )
           lowerCamelCase								=		self.add_symbol(__a    )
           lowerCamelCase								=		self.add_symbol(__a    )
           lowerCamelCase								=		self.add_symbol(__a    )
           if extra_special_symbols:
                 for s in extra_special_symbols:
                       self.add_symbol(__a    )
           lowerCamelCase								=		len(self.symbols    )







     def __eq__(self				,			__a    ):






           '''simple docstring'''



           return self.indices == other.indices







     def __getitem__(self				,			__a    ):






           '''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				,			__a    ):






           '''simple docstring'''



           return sym in self.indices







     @classmethod
     def 						_a      (cls				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		cls()
           d.add_from_file(__a    )
           return d







     def 						_a      (self				,			__a				,			__a=1				,			__a=False    ):






           '''simple docstring'''



           if word in self.indices and not overwrite:
                 lowerCamelCase								=		self.indices[word]
                 lowerCamelCase								=		self.count[idx] + n
                 return idx
           else:
                 lowerCamelCase								=		len(self.symbols    )
                 lowerCamelCase								=		idx
                 self.symbols.append(__a    )
                 self.count.append(__a    )
                 return idx







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return 0







     def 						_a      (self				,			__a    ):






           '''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								=		f.readlines()
           lowerCamelCase								=		self._load_meta(__a    )

           for line in lines[indices_start_line:]:
                 try:
                       lowerCamelCase			,							lowerCamelCase								=		line.rstrip().rsplit(" "				,			1    )
                       if field == "#fairseq:overwrite":
                             lowerCamelCase								=		True
                             lowerCamelCase			,							lowerCamelCase								=		line.rsplit(" "				,			1    )
                       else:
                             lowerCamelCase								=		False
                       lowerCamelCase								=		int(__a    )
                       lowerCamelCase								=		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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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

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

      lowerCamelCase								=		chkpt["cfg"]["model"]

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

      lowerCamelCase								=		os.path.join(UpperCAmelCase__   ,   VOCAB_FILES_NAMES["merges_file"] )
      shutil.copyfile(UpperCAmelCase__   ,   UpperCAmelCase__ )

      # model config
      lowerCamelCase								=		os.path.join(UpperCAmelCase__   ,   "config.json" )

      lowerCamelCase								=		{
          "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": 0.0_2,
          "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
      lowerCamelCase								=		os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )

      lowerCamelCase								=		{
          "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
      lowerCamelCase								=		chkpt["model"]

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

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

      lowerCamelCase								=		BioGptConfig.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		BioGptForCausalLM(UpperCAmelCase__ )

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

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

      print("Conversion is done!" )


if __name__ == "__main__":
     a_						:     List[Any]     =		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 json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_						:     Any	     =		1_6
a_						:     Any	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16   ,   UpperCAmelCase__ = "bert-base-cased" ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

      def tokenize_function(UpperCAmelCase__ ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase								=		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
      lowerCamelCase								=		datasets.map(
          UpperCAmelCase__   ,   batched=UpperCAmelCase__   ,   remove_columns=["idx", "sentence1", "sentence2"]   ,   load_from_cache_file=UpperCAmelCase__ )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      lowerCamelCase								=		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.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(UpperCAmelCase__   ,   padding="max_length"   ,   max_length=128   ,   return_tensors="pt" )
            return tokenizer.pad(UpperCAmelCase__   ,   padding="longest"   ,   return_tensors="pt" )

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

      return train_dataloader, eval_dataloader

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase								=		config["lr"]
      lowerCamelCase								=		int(config["num_epochs"] )
      lowerCamelCase								=		int(config["seed"] )
      lowerCamelCase								=		int(config["batch_size"] )
      lowerCamelCase								=		args.model_name_or_path

      set_seed(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase								=		AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__   ,   return_dict=UpperCAmelCase__ )

      # Instantiate optimizer
      lowerCamelCase								=		(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      lowerCamelCase								=		optimizer_cls(params=model.parameters()   ,   lr=UpperCAmelCase__ )

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase								=		accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase								=		1
      lowerCamelCase								=		(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            lowerCamelCase								=		get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase__   ,   num_warmup_steps=0   ,   num_training_steps=UpperCAmelCase__   ,   )
      else:
            lowerCamelCase								=		DummyScheduler(UpperCAmelCase__   ,   total_num_steps=UpperCAmelCase__   ,   warmup_num_steps=0 )

      # 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.
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		accelerator.prepare(
          UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase								=		0
      # We also need to keep track of the stating epoch so files are named properly
      lowerCamelCase								=		0

      # Now we train the model
      lowerCamelCase								=		evaluate.load("glue"   ,   "mrpc" )
      lowerCamelCase								=		0
      lowerCamelCase								=		{}
      for epoch in range(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            model.train()
            for step, batch in enumerate(UpperCAmelCase__ ):
                  lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.loss
                  lowerCamelCase								=		loss / gradient_accumulation_steps
                  accelerator.backward(UpperCAmelCase__ )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            lowerCamelCase								=		0
            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():
                        lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.logits.argmax(dim=-1 )
                  # It is slightly faster to call this once, than multiple times
                  lowerCamelCase			,							lowerCamelCase								=		accelerator.gather(
                      (predictions, batch["labels"]) )  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(UpperCAmelCase__ ) - 1:
                              lowerCamelCase								=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
                              lowerCamelCase								=		references[: len(eval_dataloader.dataset ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

            lowerCamelCase								=		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""   ,   UpperCAmelCase__ )
            lowerCamelCase								=		eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase								=		eval_metric["accuracy"]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir   ,   "all_results.json" )   ,   "w" ) as f:
                  json.dump(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
      parser.add_argument(
          "--model_name_or_path"   ,   type=UpperCAmelCase__   ,   default="bert-base-cased"   ,   help="Path to pretrained model or model identifier from huggingface.co/models."   ,   required=UpperCAmelCase__   ,   )
      parser.add_argument(
          "--output_dir"   ,   type=UpperCAmelCase__   ,   default="."   ,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,   )
      parser.add_argument(
          "--performance_lower_bound"   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,   )
      parser.add_argument(
          "--num_epochs"   ,   type=UpperCAmelCase__   ,   default=3   ,   help="Number of train epochs."   ,   )
      lowerCamelCase								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()