




'''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_			=						16
lowerCamelCase_			=						32
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Tuple	,					__A    :			Dict = 16	,					__A    :			Optional[Any] = "bert-base-cased"  )			->			Any:
   _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
   _SCREAMING_SNAKE_CASE	=				load_dataset("glue"	,					"mrpc"  )

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

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

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

   def collate_fn(__A    :			str  ):
      # 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(SCREAMING_SNAKE_CASE__	,					padding="max_length"	,					max_length=1_28	,					return_tensors="pt"  )
      return tokenizer.pad(SCREAMING_SNAKE_CASE__	,					padding="longest"	,					return_tensors="pt"  )

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

   return train_dataloader, eval_dataloader
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int	,					__A    :			Dict  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				Accelerator()

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

   set_seed(SCREAMING_SNAKE_CASE__  )
   _SCREAMING_SNAKE_CASE	=				get_dataloaders(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__  )

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

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

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

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

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

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

         overall_step += 1

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

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

      if best_performance < eval_metric["accuracy"]:
         _SCREAMING_SNAKE_CASE	=				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(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."  )
   parser.add_argument(
       "--model_name_or_path"	,					type=SCREAMING_SNAKE_CASE__	,					default="bert-base-cased"	,					help="Path to pretrained model or model identifier from huggingface.co/models."	,					required=SCREAMING_SNAKE_CASE__	,					)
   parser.add_argument(
       "--output_dir"	,					type=SCREAMING_SNAKE_CASE__	,					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=SCREAMING_SNAKE_CASE__	,					default=SCREAMING_SNAKE_CASE__	,					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=SCREAMING_SNAKE_CASE__	,					default=3	,					help="Number of train epochs."	,					)
   _SCREAMING_SNAKE_CASE	=				parser.parse_args()
   _SCREAMING_SNAKE_CASE	=				{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
 main()







'''simple docstring'''



import torch

from transformers import AutoModel







class 		lowercase_   (			torch.nn.Module       ):


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]="sayef/fsner-bert-base-uncased"   ):

      """simple docstring"""
      super(__lowerCamelCase  ,				self   ).__init__()

      _SCREAMING_SNAKE_CASE	=				AutoModel.from_pretrained(__lowerCamelCase  ,				return_dict=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.CosineSimilarity(3  ,				1e-08   )
      _SCREAMING_SNAKE_CASE	=				torch.nn.Softmax(dim=1   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      return self.bert(**__lowerCamelCase   ).last_hidden_state



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return token_embeddings.sum(2  ,				keepdim=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Tuple=1   ):

      """simple docstring"""
      return self.softmax(T * self.cos(__lowerCamelCase  ,				__lowerCamelCase   )   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				W_supports["sizes"].tolist()
      _SCREAMING_SNAKE_CASE	=				W_supports["start_token_id"].item()
      _SCREAMING_SNAKE_CASE	=				W_supports["end_token_id"].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.BERT(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == start_token_id
      _SCREAMING_SNAKE_CASE	=				W_supports["input_ids"] == end_token_id

      for i, size in enumerate(__lowerCamelCase   ):
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				0
         else:
            _SCREAMING_SNAKE_CASE	=				support_sizes[i - 1]

         _SCREAMING_SNAKE_CASE	=				S[s : s + size][start_token_masks[s : s + size]]
         _SCREAMING_SNAKE_CASE	=				S[s : s + size][end_token_masks[s : s + size]]

         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_start.T   ).sum(1   ).softmax(0   )
         _SCREAMING_SNAKE_CASE	=				torch.matmul(q[i]  ,				s_end.T   ).sum(1   ).softmax(0   )

         if p_starts is not None:
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_starts, p_start)   )
            _SCREAMING_SNAKE_CASE	=				torch.vstack((p_ends, p_end)   )
         else:
            _SCREAMING_SNAKE_CASE	=				p_start
            _SCREAMING_SNAKE_CASE	=				p_end

      return p_starts, p_ends

