



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


_snake_case	=						16
_snake_case	=						32






def        A       (      _lowerCamelCase       ,				_lowerCamelCase = 16       ,				_lowerCamelCase = "bert-base-cased"						):





       '''simple docstring'''




       _lowerCAmelCase  :							Union[str, Any]				     =					AutoTokenizer.from_pretrained(_lowerCamelCase						)
       _lowerCAmelCase  :							Any				     =					load_dataset("glue"       ,				"mrpc"						)

       def tokenize_function(_lowerCamelCase						):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCAmelCase  :							Tuple				     =					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
       _lowerCAmelCase  :							List[str]				     =					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
       _lowerCAmelCase  :							Dict				     =					tokenized_datasets.rename_column("label"       ,				"labels"						)

       def collate_fn(_lowerCamelCase						):
              # 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.
       _lowerCAmelCase  :							Dict				     =					DataLoader(
           tokenized_datasets["train"]       ,				shuffle=_lowerCamelCase       ,				collate_fn=_lowerCamelCase       ,				batch_size=_lowerCamelCase						)
       _lowerCAmelCase  :							Tuple				     =					DataLoader(
           tokenized_datasets["validation"]       ,				shuffle=_lowerCamelCase       ,				collate_fn=_lowerCamelCase       ,				batch_size=_lowerCamelCase						)

       return train_dataloader, eval_dataloader






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




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

       _lowerCAmelCase  :							str				     =					metric.compute()
       return eval_metric["accuracy"]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




       _lowerCAmelCase  :							Union[str, Any]				     =					Accelerator()

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

       set_seed(_lowerCamelCase						)
       _lowerCAmelCase  :							Optional[int]				     =					get_dataloaders(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCAmelCase  :							List[Any]				     =					AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase       ,				return_dict=_lowerCamelCase						)

       # Instantiate optimizer
       _lowerCAmelCase  :							Dict				     =					(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _lowerCAmelCase  :							Tuple				     =					optimizer_cls(params=model.parameters()       ,				lr=_lowerCamelCase						)

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

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

       if args.partial_train_epoch is not None:
              _lowerCAmelCase  :							int				     =					args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint						)
              _lowerCAmelCase  :							int				     =					args.resume_from_checkpoint.split("epoch_"						)[1]
              _lowerCAmelCase  :							Optional[int]				     =					""""""
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              _lowerCAmelCase  :							int				     =					int(_lowerCamelCase						) + 1
              _lowerCAmelCase  :							Dict				     =					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:
                     _lowerCAmelCase  :							List[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
       _lowerCAmelCase  :							Dict				     =					{}
       for epoch in range(_lowerCamelCase       ,				_lowerCamelCase						):
              model.train()
              for step, batch in enumerate(_lowerCamelCase						):
                     _lowerCAmelCase  :							Union[str, Any]				     =					model(**_lowerCamelCase						)
                     _lowerCAmelCase  :							str				     =					outputs.loss
                     _lowerCAmelCase  :							List[str]				     =					loss / gradient_accumulation_steps
                     accelerator.backward(_lowerCamelCase						)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              _lowerCAmelCase  :							int				     =					F"epoch_{epoch}"
              _lowerCAmelCase  :							Optional[int]				     =					os.path.join(args.output_dir       ,				_lowerCamelCase						)
              accelerator.save_state(_lowerCamelCase						)
              _lowerCAmelCase  :							Optional[int]				     =					evaluation_loop(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
              _lowerCAmelCase  :							Optional[Any]				     =					accuracy
              _lowerCAmelCase  :							str				     =					lr_scheduler.get_lr()[0]
              _lowerCAmelCase  :							List[str]				     =					optimizer.param_groups[0]["""lr"""]
              _lowerCAmelCase  :							Union[str, Any]				     =					epoch
              _lowerCAmelCase  :							str				     =					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       (      ):





       '''simple docstring'''




       _lowerCAmelCase  :							Tuple				     =					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."       ,				)
       _lowerCAmelCase  :							List[str]				     =					parser.parse_args()
       _lowerCAmelCase  :							List[str]				     =					{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

       training_function(_lowerCamelCase       ,				_lowerCamelCase						)


if __name__ == "__main__":
      main()





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'xlnet'
							lowerCamelCase__         =	['mems']
							lowerCamelCase__         =	{
							    'n_token': 'vocab_size',  # Backward compatibility
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}







							def __init__(     self,    __a=3_2000,    __a=1024,    __a=24,    __a=16,    __a=4096,    __a="gelu",    __a=True,    __a="bi",    __a=0.02,    __a=1E-12,    __a=0.1,    __a=512,    __a=None,    __a=True,    __a=False,    __a=False,    __a=-1,    __a=False,    __a="last",    __a=True,    __a="tanh",    __a=0.1,    __a=5,    __a=5,    __a=5,    __a=1,    __a=2,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					d_model
														_lowerCAmelCase  :							Tuple				     =					n_layer
														_lowerCAmelCase  :							List[Any]				     =					n_head
														if d_model % n_head != 0:
																					raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})")
														_lowerCAmelCase  :							Optional[int]				     =					d_model // n_head
														_lowerCAmelCase  :							List[str]				     =					ff_activation
														_lowerCAmelCase  :							Tuple				     =					d_inner
														_lowerCAmelCase  :							List[Any]				     =					untie_r
														_lowerCAmelCase  :							List[str]				     =					attn_type

														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Any				     =					layer_norm_eps

														_lowerCAmelCase  :							List[Any]				     =					dropout
														_lowerCAmelCase  :							Optional[int]				     =					mem_len
														_lowerCAmelCase  :							Union[str, Any]				     =					reuse_len
														_lowerCAmelCase  :							List[str]				     =					bi_data
														_lowerCAmelCase  :							List[str]				     =					clamp_len
														_lowerCAmelCase  :							Any				     =					same_length

														_lowerCAmelCase  :							List[str]				     =					summary_type
														_lowerCAmelCase  :							int				     =					summary_use_proj
														_lowerCAmelCase  :							Optional[Any]				     =					summary_activation
														_lowerCAmelCase  :							Tuple				     =					summary_last_dropout
														_lowerCAmelCase  :							Union[str, Any]				     =					start_n_top
														_lowerCAmelCase  :							Optional[int]				     =					end_n_top

														_lowerCAmelCase  :							Tuple				     =					bos_token_id
														_lowerCAmelCase  :							List[Any]				     =					pad_token_id
														_lowerCAmelCase  :							Dict				     =					eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
																					    " instead.",    __a,    )
																					_lowerCAmelCase  :							Union[str, Any]				     =					kwargs["use_cache"]

														_lowerCAmelCase  :							Union[str, Any]				     =					use_mems_eval
														_lowerCAmelCase  :							Any				     =					use_mems_train
														super().__init__(pad_token_id=__a,    bos_token_id=__a,    eos_token_id=__a,    **__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
														return -1







							@max_position_embeddings.setter
							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														raise NotImplementedError(
														    f"The model {self.model_type} is one of the few models that has no sequence length limit.")
