



import unittest

from transformers.image_processing_utils import get_size_dict
class       _lowerCAmelCase (			unittest.TestCase      ):






			"""simple docstring"""





			def 		snake_case__      (					self						:							Union[str, Any] )    ->       Any:
									"""simple docstring"""







									__lowercase											=					{"""wrong_key""": 224}
									with self.assertRaises(_UpperCAmelCase ):
															get_size_dict(_UpperCAmelCase )

									__lowercase											=					{"""height""": 224}
									with self.assertRaises(_UpperCAmelCase ):
															get_size_dict(_UpperCAmelCase )

									__lowercase											=					{"""width""": 224, """shortest_edge""": 224}
									with self.assertRaises(_UpperCAmelCase ):
															get_size_dict(_UpperCAmelCase )

									# Test a dict with the correct keys is returned as is
									__lowercase											=					{"""height""": 224, """width""": 224}
									__lowercase											=					get_size_dict(_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  _UpperCAmelCase )

									__lowercase											=					{"""shortest_edge""": 224}
									__lowercase											=					get_size_dict(_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  {"""shortest_edge""": 224} )

									__lowercase											=					{"""longest_edge""": 224, """shortest_edge""": 224}
									__lowercase											=					get_size_dict(_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  {"""longest_edge""": 224, """shortest_edge""": 224} )

									# Test a single int value which  represents (size, size)
									__lowercase											=					get_size_dict(224 )
									self.assertEqual(_UpperCAmelCase      ,  {"""height""": 224, """width""": 224} )

									# Test a single int value which represents the shortest edge
									__lowercase											=					get_size_dict(224      ,  default_to_square=_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  {"""shortest_edge""": 224} )

									# Test a tuple of ints which represents (height, width)
									__lowercase											=					get_size_dict((150, 200) )
									self.assertEqual(_UpperCAmelCase      ,  {"""height""": 150, """width""": 200} )

									# Test a tuple of ints which represents (width, height)
									__lowercase											=					get_size_dict((150, 200)      ,  height_width_order=_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  {"""height""": 200, """width""": 150} )

									# Test an int representing the shortest edge and max_size which represents the longest edge
									__lowercase											=					get_size_dict(224      ,  max_size=256      ,  default_to_square=_UpperCAmelCase )
									self.assertEqual(_UpperCAmelCase      ,  {"""shortest_edge""": 224, """longest_edge""": 256} )

									# Test int with default_to_square=True and max_size fails
									with self.assertRaises(_UpperCAmelCase ):
															get_size_dict(224      ,  max_size=256      ,  default_to_square=_UpperCAmelCase )













import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCAmelCase					:						Union[str, Any]         =     16
_UpperCAmelCase					:						Dict         =     32






def  UpperCAmelCase__						(    lowerCamelCase	:			List[Any]			,lowerCamelCase	:			List[Any]    ):
   # Initialize accelerator
   if args.with_tracking:
      lowerCamelCase									=Accelerator(
          cpu=args.cpu			,mixed_precision=args.mixed_precision			,log_with="""all"""			,project_dir=args.project_dir    )
   else:
      lowerCamelCase									=Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision    )

   if hasattr(args.checkpointing_steps			,"""isdigit"""    ):
      if args.checkpointing_steps == "epoch":
         lowerCamelCase									=args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         lowerCamelCase									=int(args.checkpointing_steps    )
      else:
         raise ValueError(
             f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      lowerCamelCase									=None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCamelCase									=config["""lr"""]
   lowerCamelCase									=int(config["""num_epochs"""]    )
   lowerCamelCase									=int(config["""seed"""]    )
   lowerCamelCase									=int(config["""batch_size"""]    )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      lowerCamelCase									=os.path.split(lowerCamelCase    )[-1].split("""."""    )[0]
      accelerator.init_trackers(lowerCamelCase			,lowerCamelCase    )

   lowerCamelCase									=AutoTokenizer.from_pretrained("""bert-base-cased"""    )
   lowerCamelCase									=load_dataset("""glue"""			,"""mrpc"""    )
   lowerCamelCase									=evaluate.load("""glue"""			,"""mrpc"""    )

   def tokenize_function(lowerCamelCase	:			int    ):
      # max_length=None => use the model max length (it's actually the default)
      lowerCamelCase									=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
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCamelCase									=datasets.map(
          lowerCamelCase			,batched=lowerCamelCase			,remove_columns=["""idx""", """sentence1""", """sentence2"""]			,)

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

   # If the batch size is too big we use gradient accumulation
   lowerCamelCase									=1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowerCamelCase									=batch_size // MAX_GPU_BATCH_SIZE
      lowerCamelCase									=MAX_GPU_BATCH_SIZE

   def collate_fn(lowerCamelCase	:			Any    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCamelCase									=1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowerCamelCase									=16
      elif accelerator.mixed_precision != "no":
         lowerCamelCase									=8
      else:
         lowerCamelCase									=None

      return tokenizer.pad(
          lowerCamelCase			,padding="""longest"""			,max_length=lowerCamelCase			,pad_to_multiple_of=lowerCamelCase			,return_tensors="""pt"""			,)

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

   set_seed(lowerCamelCase    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase									=AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,return_dict=lowerCamelCase    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowerCamelCase									=model.to(accelerator.device    )

   # Instantiate optimizer
   lowerCamelCase									=AdamW(params=model.parameters()			,lr=lowerCamelCase    )

   # Instantiate scheduler
   lowerCamelCase									=get_linear_schedule_with_warmup(
       optimizer=lowerCamelCase			,num_warmup_steps=1_00			,num_training_steps=(len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps			,)

   # 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				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=accelerator.prepare(
       lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )

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

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         lowerCamelCase									=os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         lowerCamelCase									=[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         lowerCamelCase									=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      lowerCamelCase									=os.path.splitext(lowerCamelCase    )[0]

      if "epoch" in training_difference:
         lowerCamelCase									=int(training_difference.replace("""epoch_"""			,""""""    )    ) + 1
         lowerCamelCase									=None
      else:
         lowerCamelCase									=int(training_difference.replace("""step_"""			,""""""    )    )
         lowerCamelCase									=resume_step // len(lowerCamelCase    )
         resume_step -= starting_epoch * len(lowerCamelCase    )

    # Now we train the model
   for epoch in range(lowerCamelCase			,lowerCamelCase    ):
      model.train()
      if args.with_tracking:
         lowerCamelCase									=0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         lowerCamelCase									=accelerator.skip_first_batches(lowerCamelCase			,lowerCamelCase    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         lowerCamelCase									=train_dataloader
      for step, batch in enumerate(lowerCamelCase    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         lowerCamelCase									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.loss
         lowerCamelCase									=loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(lowerCamelCase    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(lowerCamelCase			,lowerCamelCase    ):
            lowerCamelCase									=f'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
               accelerator.save_state(lowerCamelCase    )

      model.eval()
      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									=model(**lowerCamelCase    )
         lowerCamelCase									=outputs.logits.argmax(dim=-1    )
         lowerCamelCase				,       lowerCamelCase									=accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
         metric.add_batch(
             predictions=lowerCamelCase			,references=lowerCamelCase			,)

      lowerCamelCase									=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,lowerCamelCase    )
      if args.with_tracking:
         accelerator.log(
             {
                 """accuracy""": eval_metric["""accuracy"""],
                 """f1""": eval_metric["""f1"""],
                 """train_loss""": total_loss.item() / len(lowerCamelCase    ),
                 """epoch""": epoch,
             }			,step=lowerCamelCase			,)

      if checkpointing_steps == "epoch":
         lowerCamelCase									=f'''epoch_{epoch}'''
         if args.output_dir is not None:
            lowerCamelCase									=os.path.join(args.output_dir			,lowerCamelCase    )
         accelerator.save_state(lowerCamelCase    )

   if args.with_tracking:
      accelerator.end_training()






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=argparse.ArgumentParser(description="""Simple example of training script."""    )
   parser.add_argument(
       """--mixed_precision"""			,type=lowerCamelCase			,default=lowerCamelCase			,choices=["""no""", """fp16""", """bf16""", """fp8"""]			,help="""Whether to use mixed precision. Choose"""
       """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
       """and an Nvidia Ampere GPU."""			,)
   parser.add_argument("""--cpu"""			,action="""store_true"""			,help="""If passed, will train on the CPU."""    )
   parser.add_argument(
       """--checkpointing_steps"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,)
   parser.add_argument(
       """--resume_from_checkpoint"""			,type=lowerCamelCase			,default=lowerCamelCase			,help="""If the training should continue from a checkpoint folder."""			,)
   parser.add_argument(
       """--with_tracking"""			,action="""store_true"""			,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,)
   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(
       """--project_dir"""			,type=lowerCamelCase			,default="""logs"""			,help="""Location on where to store experiment tracking logs` and relevent project information"""			,)
   lowerCamelCase									=parser.parse_args()
   lowerCamelCase									={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(lowerCamelCase			,lowerCamelCase    )


if __name__ == "__main__":
   main()