






"""simple docstring"""





import warnings

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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    '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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"xlnet"
					UpperCAmelCase__								=				["mems"]
					UpperCAmelCase__								=				{
					    "n_token": "vocab_size",  # Backward compatibility
					    "hidden_size": "d_model",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      int=3_2_0_0_0     ,		__snake_case  :      Tuple=1_0_2_4     ,		__snake_case  :      List[str]=2_4     ,		__snake_case  :      Dict=1_6     ,		__snake_case  :      Tuple=4_0_9_6     ,		__snake_case  :      str="gelu"     ,		__snake_case  :      Dict=True     ,		__snake_case  :      Optional[Any]="bi"     ,		__snake_case  :      Any=0.02     ,		__snake_case  :      Tuple=1E-12     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      Any=5_1_2     ,		__snake_case  :      Optional[int]=None     ,		__snake_case  :      Any=True     ,		__snake_case  :      List[str]=False     ,		__snake_case  :      Dict=False     ,		__snake_case  :      List[Any]=-1     ,		__snake_case  :      List[Any]=False     ,		__snake_case  :      Tuple="last"     ,		__snake_case  :      Optional[Any]=True     ,		__snake_case  :      List[str]="tanh"     ,		__snake_case  :      Any=0.1     ,		__snake_case  :      str=5     ,		__snake_case  :      Optional[Any]=5     ,		__snake_case  :      List[str]=5     ,		__snake_case  :      str=1     ,		__snake_case  :      Dict=2     ,		**__snake_case  :      Dict     ,		)			->							Optional[int]:
											__magic_name__:	Dict							=       vocab_size
											__magic_name__:	List[Any]							=       d_model
											__magic_name__:	int							=       n_layer
											__magic_name__:	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})'					)
											__magic_name__:	str							=       d_model // n_head
											__magic_name__:	Optional[Any]							=       ff_activation
											__magic_name__:	Tuple							=       d_inner
											__magic_name__:	int							=       untie_r
											__magic_name__:	Optional[Any]							=       attn_type

											__magic_name__:	str							=       initializer_range
											__magic_name__:	List[Any]							=       layer_norm_eps

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

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

											__magic_name__:	Tuple							=       bos_token_id
											__magic_name__:	int							=       pad_token_id
											__magic_name__:	List[Any]							=       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."""     ,		__snake_case     ,		)
																	__magic_name__:	int							=       kwargs["""use_cache"""]

											__magic_name__:	str							=       use_mems_eval
											__magic_name__:	List[str]							=       use_mems_train
											super().__init__(pad_token_id=__snake_case     ,		bos_token_id=__snake_case     ,		eos_token_id=__snake_case     ,		**__snake_case					)

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											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   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Optional[Any]					)			->							str:
											# Message copied from Transformer-XL documentation
											raise NotImplementedError(
											    F'The model {self.model_type} is one of the few models that has no sequence length limit.'					)







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   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

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

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

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

     def collate_fn(_lowercase ):
          # 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(_lowercase					,  padding='max_length'					,  max_length=1_28					,  return_tensors='pt' )
          return tokenizer.pad(_lowercase					,  padding='longest'					,  return_tensors='pt' )

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

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			Accelerator()

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

     set_seed(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

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

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

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(len(_lowercase ) * num_epochs) // gradient_accumulation_steps

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

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

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

               overall_step += 1

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

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __UpperCamelCase =			eval_metric['accuracy']

          if best_performance < eval_metric["accuracy"]:
               __UpperCamelCase =			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(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
     parser.add_argument(
         '--model_name_or_path'					,  type=_lowercase					,  default='bert-base-cased'					,  help='Path to pretrained model or model identifier from huggingface.co/models.'					,  required=_lowercase					,  )
     parser.add_argument(
         '--output_dir'					,  type=_lowercase					,  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=_lowercase					,  default=_lowercase					,  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=_lowercase					,  default=3					,  help='Number of train epochs.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()


