



'''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 :					Any 							=16
lowerCAmelCase :					Tuple 							=32
def 							UpperCAmelCase_  (						__lowerCamelCase : Accelerator     ,__lowerCamelCase : int = 16     ,__lowerCamelCase : str = "bert-base-cased"			):
   lowercase_      :Dict         =			AutoTokenizer.from_pretrained(__lowerCamelCase			)
   lowercase_      :Any         =			load_dataset("glue"     ,"mrpc"			)

   def tokenize_function(__lowerCamelCase : Optional[Any]			):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_      :Any         =			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_      :Optional[int]         =			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_      :List[Any]         =			tokenized_datasets.rename_column("label"     ,"labels"			)

   def collate_fn(__lowerCamelCase : Dict			):
      # 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=1_28     ,return_tensors="pt"			)
      return tokenizer.pad(__lowerCamelCase     ,padding="longest"     ,return_tensors="pt"			)

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

   return train_dataloader, eval_dataloader
def 							UpperCAmelCase_  (						__lowerCamelCase : Any     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Tuple			):
   model.eval()
   lowercase_      :Tuple         =			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_      :str         =			model(**__lowerCamelCase			)
      lowercase_      :Any         =			outputs.logits.argmax(dim=-1			)
      # It is slightly faster to call this once, than multiple times
      lowercase_      :List[Any]         =			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_      :Tuple         =			predictions[: len(eval_dataloader.dataset			) - samples_seen]
            lowercase_      :Tuple         =			references[: len(eval_dataloader.dataset			) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=__lowerCamelCase     ,references=__lowerCamelCase     ,)

   lowercase_      :Optional[Any]         =			metric.compute()
   return eval_metric["accuracy"]
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]     ,__lowerCamelCase : Optional[Any]			):
   # Initialize accelerator
   lowercase_      :List[str]         =			Accelerator()

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

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

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

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

   if accelerator.state.deepspeed_plugin is not None:
      lowercase_      :Any         =			accelerator.state.deepspeed_plugin.deepspeed_config[
          """gradient_accumulation_steps"""
      ]
   else:
      lowercase_      :str         =			1
   lowercase_      :Optional[Any]         =			(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_      :List[str]         =			get_linear_schedule_with_warmup(
          optimizer=__lowerCamelCase     ,num_warmup_steps=0     ,num_training_steps=__lowerCamelCase     ,)
   else:
      lowercase_      :Optional[Any]         =			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_      :Dict         =			accelerator.prepare(
       __lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)

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

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

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint			)
      lowercase_      :Tuple         =			args.resume_from_checkpoint.split("epoch_"			)[1]
      lowercase_      :List[Any]         =			""""""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      lowercase_      :str         =			int(__lowerCamelCase			) + 1
      lowercase_      :str         =			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_      :Optional[Any]         =			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_      :List[Any]         =			{}
   for epoch in range(__lowerCamelCase     ,__lowerCamelCase			):
      model.train()
      for step, batch in enumerate(__lowerCamelCase			):
         lowercase_      :int         =			model(**__lowerCamelCase			)
         lowercase_      :Optional[int]         =			outputs.loss
         lowercase_      :Optional[Any]         =			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_      :Tuple         =			F'epoch_{epoch}'
      lowercase_      :List[Any]         =			os.path.join(args.output_dir     ,__lowerCamelCase			)
      accelerator.save_state(__lowerCamelCase			)
      lowercase_      :Any         =			evaluation_loop(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
      lowercase_      :List[Any]         =			accuracy
      lowercase_      :str         =			lr_scheduler.get_lr()[0]
      lowercase_      :str         =			optimizer.param_groups[0]["""lr"""]
      lowercase_      :Optional[int]         =			epoch
      lowercase_      :Optional[int]         =			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 							UpperCAmelCase_  (						):
   lowercase_      :Union[str, Any]         =			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_      :Optional[Any]         =			parser.parse_args()
   lowercase_      :Union[str, Any]         =			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

   training_function(__lowerCamelCase     ,__lowerCamelCase			)


if __name__ == "__main__":
 main()









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)

lowerCAmelCase :					Optional[Any] 							={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "gpt_neo"
     __A								=      ["past_key_values"]
     __A								=      {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





     def __init__(			self		: Union[str, Any]     ,				lowercase		: Tuple=50_257     ,				lowercase		: Optional[Any]=2_048     ,				lowercase		: Union[str, Any]=2_048     ,				lowercase		: int=24     ,				lowercase		: Optional[Any]=[[["global", "local"], 12]]     ,				lowercase		: List[Any]=16     ,				lowercase		: List[str]=None     ,				lowercase		: Union[str, Any]=256     ,				lowercase		: Optional[Any]="gelu_new"     ,				lowercase		: Any=0.0     ,				lowercase		: List[Any]=0.0     ,				lowercase		: Any=0.0     ,				lowercase		: str=0.1     ,				lowercase		: Dict=1e-5     ,				lowercase		: List[str]=0.02     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: int=50_256     ,				lowercase		: Union[str, Any]=50_256     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




        lowercase_      :str         =			vocab_size
        lowercase_      :Tuple         =			max_position_embeddings
        lowercase_      :Tuple         =			hidden_size
        lowercase_      :List[str]         =			num_layers
        lowercase_      :int         =			num_heads
        lowercase_      :Union[str, Any]         =			intermediate_size
        lowercase_      :Tuple         =			window_size
        lowercase_      :Any         =			activation_function
        lowercase_      :Tuple         =			resid_dropout
        lowercase_      :Any         =			embed_dropout
        lowercase_      :str         =			attention_dropout
        lowercase_      :List[str]         =			classifier_dropout
        lowercase_      :List[Any]         =			layer_norm_epsilon
        lowercase_      :List[str]         =			initializer_range
        lowercase_      :int         =			use_cache

        lowercase_      :Tuple         =			bos_token_id
        lowercase_      :Optional[Any]         =			eos_token_id

        lowercase_      :int         =			attention_types
        lowercase_      :Tuple         =			self.expand_attention_types_params(lowercase						)

        if len(self.attention_layers						) != self.num_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.attention_layers)` == `config.num_layers` "
               F'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
               F'`config.num_layers = {self.num_layers}`. '
               "`config.attention_layers` is prepared using `config.attention_types`. "
               "Please verify the value of `config.attention_types` argument."						)

        super().__init__(bos_token_id=lowercase     ,				eos_token_id=lowercase     ,				**lowercase						)







     @staticmethod
     def 							lowercase__				(			lowercase		: str						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			[]
        for item in attention_types:
           for _ in range(item[1]						):
              attentions.extend(item[0]						)
        return attentions







def 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Dict			):
   import torch

   lowercase_      :List[str]         =			input.size()
   lowercase_      :Union[str, Any]         =			len(__lowerCamelCase			)
   lowercase_      :Any         =			shape[dimension]

   lowercase_      :str         =			torch.arange(0     ,__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			torch.div(sizedim - size     ,__lowerCamelCase     ,rounding_mode="floor"			) + 1
   lowercase_      :int         =			torch.arange(__lowerCamelCase			) + low_indices[:min_length][:, None]

   lowercase_      :List[Any]         =			[slice(__lowerCamelCase			)] * rank
   lowercase_      :int         =			indices
   lowercase_      :Dict         =			input[s]

   lowercase_      :List[str]         =			list(range(0     ,rank + 1			)			)
   perm.append(perm.pop(dimension + 1			)			)

   return sliced.permute(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Any			):
   import torch

   lowercase_      :List[Any]         =			torch.arange(1     ,__lowerCamelCase			)
   lowercase_      :int         =			torch.remainder(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Optional[int]         =			remainders == 0
   lowercase_      :int         =			candidates[divisor_indices]
   lowercase_      :Tuple         =			torch.max(__lowerCamelCase			)
   return largest_divisor, torch.div(__lowerCamelCase     ,__lowerCamelCase     ,rounding_mode="floor"			)


class 				a_       (    _lowerCAmelCase							):





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :int         =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
        if self.use_past:
           self.fill_with_past_key_values_(lowercase     ,				direction="inputs"						)
           lowercase_      :Union[str, Any]         =			{0: "batch", 1: "past_sequence + sequence"}
        else:
           lowercase_      :str         =			{0: "batch", 1: "sequence"}

        return common_inputs





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self._config.num_heads





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: PreTrainedTokenizer     ,				lowercase		: int = -1     ,				lowercase		: int = -1     ,				lowercase		: bool = False     ,				lowercase		: Optional[TensorType] = None     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			super(lowercase     ,				self						).generate_dummy_inputs(
            lowercase     ,				batch_size=lowercase     ,				seq_length=lowercase     ,				is_pair=lowercase     ,				framework=lowercase						)

        # We need to order the input in the way they appears in the forward()
        lowercase_      :Tuple         =			OrderedDict({"input_ids": common_inputs["input_ids"]}						)

        # Need to add the past_keys
        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
           else:
              import torch

              lowercase_      ,							lowercase_      :Tuple         =			common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              lowercase_      :Any         =			seqlen + 2
              lowercase_      :List[Any]         =			(
                  batch,
                  self.num_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // self.num_attention_heads,
              )
              lowercase_      :Dict         =			[
                  (torch.zeros(lowercase						), torch.zeros(lowercase						)) for _ in range(self.num_layers						)
              ]

        lowercase_      :Tuple         =			common_inputs["attention_mask"]
        if self.use_past:
           lowercase_      :Optional[int]         =			ordered_inputs["attention_mask"].dtype
           lowercase_      :List[Any]         =			torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowercase     ,				lowercase     ,				dtype=lowercase						)]     ,				dim=1						)

        return ordered_inputs







     @property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return 13


