




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       :       Union[str, Any]        =    1_6
lowerCamelCase       :       int        =    3_2




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase = 16						,lowercase = "bert-base-cased"     )  ->   Dict:
   snake_case      :				List[str]				=       AutoTokenizer.from_pretrained(lowercase     )
   snake_case      :				Union[str, Any]				=       load_dataset("""glue"""						,"""mrpc"""     )

   def tokenize_function(lowercase     ):
      # max_length=None => use the model max length (it's actually the default)
      snake_case      :				List[Any]				=       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
   snake_case      :				List[Any]				=       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
   snake_case      :				Optional[Any]				=       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=128						,return_tensors="""pt"""     )
      return tokenizer.pad(lowercase						,padding="""longest"""						,return_tensors="""pt"""     )

   # Instantiate dataloaders.
   snake_case      :				List[str]				=       DataLoader(
       tokenized_datasets["""train"""]						,shuffle=lowercase						,collate_fn=lowercase						,batch_size=lowercase     )
   snake_case      :				List[Any]				=       DataLoader(
       tokenized_datasets["""validation"""]						,shuffle=lowercase						,collate_fn=lowercase						,batch_size=lowercase     )

   return train_dataloader, eval_dataloader




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   List[Any]:
   snake_case      :				Optional[Any]				=       Accelerator()

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

   set_seed(lowercase     )
   snake_case      :				Dict				=       get_dataloaders(lowercase						,lowercase						,lowercase     )

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

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

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

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

   # Now we train the model
   snake_case      :				Any				=       evaluate.load("""glue"""						,"""mrpc"""     )
   snake_case      :				Tuple				=       0
   snake_case      :				List[Any]				=       {}
   for epoch in range(lowercase						,lowercase     ):
      model.train()
      for step, batch in enumerate(lowercase     ):
         snake_case      :				List[str]				=       model(**lowercase     )
         snake_case      :				Dict				=       outputs.loss
         snake_case      :				Optional[int]				=       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()
      snake_case      :				str				=       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():
            snake_case      :				Optional[int]				=       model(**lowercase     )
         snake_case      :				List[Any]				=       outputs.logits.argmax(dim=-1     )
         # It is slightly faster to call this once, than multiple times
         snake_case      :				Optional[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(lowercase     ) - 1:
               snake_case      :				Dict				=       predictions[: len(eval_dataloader.dataset     ) - samples_seen]
               snake_case      :				List[str]				=       references[: len(eval_dataloader.dataset     ) - samples_seen]
            else:
               samples_seen += references.shape[0]
         metric.add_batch(
             predictions=lowercase						,references=lowercase						,)

      snake_case      :				List[str]				=       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""						,lowercase     )
      snake_case      :				Dict				=       eval_metric["""accuracy"""]

      if best_performance < eval_metric["accuracy"]:
         snake_case      :				str				=       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 						SCREAMING_SNAKE_CASE__					(    )  ->   Tuple:
   snake_case      :				List[str]				=       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."""						,)
   snake_case      :				str				=       parser.parse_args()
   snake_case      :				Any				=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
   training_function(lowercase						,lowercase     )


if __name__ == "__main__":
       main()













from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
