'''simple docstring'''

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


lowerCAmelCase_ : int						  =      16
lowerCAmelCase_ : str						  =      32


def __a (	__lowerCamelCase			:						Accelerator ,		__lowerCamelCase			:						int = 16 ,		__lowerCamelCase			:						str = "bert-base-cased"				)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      AutoTokenizer.from_pretrained(__lowerCamelCase				)
      lowercase_     =      load_dataset("glue" ,		"mrpc"				)

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

      # Apply the method we just defined to all the examples in all the splits of the dataset
      lowercase_     =      datasets.map(
          __lowerCamelCase ,		batched=__lowerCamelCase ,		remove_columns=["idx", "sentence1", "sentence2"] ,		load_from_cache_file=__lowerCamelCase				)

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

      def collate_fn(__lowerCamelCase			:						Optional[int]				):
            # 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(__lowerCamelCase ,		padding="max_length" ,		max_length=128 ,		return_tensors="pt"				)
            return tokenizer.pad(__lowerCamelCase ,		padding="longest" ,		return_tensors="pt"				)

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

      return train_dataloader, eval_dataloader


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str]				)	->							Dict:
      '''simple docstring'''


      model.eval()
      lowercase_     =      0
      for step, batch in enumerate(__lowerCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                  lowercase_     =      model(**__lowerCamelCase				)
            lowercase_     =      outputs.logits.argmax(dim=-1				)
            # It is slightly faster to call this once, than multiple times
            lowercase_ ,							lowercase_     =      accelerator.gather(
                (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(__lowerCamelCase				) - 1:
                        lowercase_     =      predictions[: len(eval_dataloader.dataset				) - samples_seen]
                        lowercase_     =      references[: len(eval_dataloader.dataset				) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=__lowerCamelCase ,		references=__lowerCamelCase ,		)

      lowercase_     =      metric.compute()
      return eval_metric["accuracy"]


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Optional[int]				)	->							List[Any]:
      '''simple docstring'''


      lowercase_     =      Accelerator()

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

      set_seed(__lowerCamelCase				)
      lowercase_ ,							lowercase_     =      get_dataloaders(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)

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

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

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

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

      # We need to keep track of how many total steps we have iterated over
      lowercase_     =      0
      # We also need to keep track of the stating epoch so files are named properly
      lowercase_     =      0
      lowercase_     =      evaluate.load("glue" ,		"mrpc"				)
      lowercase_     =      num_epochs

      if args.partial_train_epoch is not None:
            lowercase_     =      args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint				)
            lowercase_     =      args.resume_from_checkpoint.split("epoch_"				)[1]
            lowercase_     =      ""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            lowercase_     =      int(__lowerCamelCase				) + 1
            lowercase_     =      evaluation_loop(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
            accelerator.print("resumed checkpoint performance:" ,		__lowerCamelCase				)
            accelerator.print("resumed checkpoint's scheduler's lr:" ,		lr_scheduler.get_lr()[0]				)
            accelerator.print("resumed optimizers's lr:" ,		optimizer.param_groups[0]["lr"]				)
            with open(os.path.join(args.output_dir ,		f'state_{starting_epoch-1}.json'				) ,		"r"				) as f:
                  lowercase_     =      json.load(__lowerCamelCase				)
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      lowercase_     =      {}
      for epoch in range(__lowerCamelCase ,		__lowerCamelCase				):
            model.train()
            for step, batch in enumerate(__lowerCamelCase				):
                  lowercase_     =      model(**__lowerCamelCase				)
                  lowercase_     =      outputs.loss
                  lowercase_     =      loss / gradient_accumulation_steps
                  accelerator.backward(__lowerCamelCase				)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            lowercase_     =      f'epoch_{epoch}'
            lowercase_     =      os.path.join(args.output_dir ,		__lowerCamelCase				)
            accelerator.save_state(__lowerCamelCase				)
            lowercase_     =      evaluation_loop(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase				)
            lowercase_     =      accuracy
            lowercase_     =      lr_scheduler.get_lr()[0]
            lowercase_     =      optimizer.param_groups[0]["lr"]
            lowercase_     =      epoch
            lowercase_     =      overall_step
            accelerator.print(f'epoch {epoch}:' ,		__lowerCamelCase				)

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir ,		f'state_{epoch}.json'				) ,		"w"				) as f:
                        json.dump(__lowerCamelCase ,		__lowerCamelCase				)


def __a (	)	->							Optional[Any]:
      '''simple docstring'''


      lowercase_     =      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."				)
      parser.add_argument(
          "--model_name_or_path" ,		type=__lowerCamelCase ,		default="bert-base-cased" ,		help="Path to pretrained model or model identifier from huggingface.co/models." ,		required=__lowerCamelCase ,		)
      parser.add_argument(
          "--output_dir" ,		type=__lowerCamelCase ,		default="." ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,		)
      parser.add_argument(
          "--resume_from_checkpoint" ,		type=__lowerCamelCase ,		default=__lowerCamelCase ,		help="If the training should continue from a checkpoint folder." ,		)
      parser.add_argument(
          "--partial_train_epoch" ,		type=__lowerCamelCase ,		default=__lowerCamelCase ,		help="If passed, the training will stop after this number of epochs." ,		)
      parser.add_argument(
          "--num_epochs" ,		type=__lowerCamelCase ,		default=2 ,		help="Number of train epochs." ,		)
      lowercase_     =      parser.parse_args()
      lowercase_     =      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

      training_function(__lowerCamelCase ,		__lowerCamelCase				)


if __name__ == "__main__":
       main()







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






