
'''simple docstring'''







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 					UpperCAmelCase_		(__a      :    List[str]=None ,	__a      :    Dict=None ):






   """simple docstring"""

   return field(default_factory=lambda: default ,	metadata=__a )







@dataclass
class UpperCAmelCase__	:





  """simple docstring"""

  __UpperCAmelCase				:				str   =					field(
      metadata={'''help''': '''The csv file to plot.'''}    ,				)
  __UpperCAmelCase				:				bool   =					field(
      default=lowercase__    ,				metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}    ,				)
  __UpperCAmelCase				:				bool   =					field(
      default=lowercase__    ,				metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}    ,				)
  __UpperCAmelCase				:				bool   =					field(
      default=lowercase__    ,				metadata={'''help''': '''Disable logarithmic scale when plotting'''}    ,				)
  __UpperCAmelCase				:				bool   =					field(
      default=lowercase__    ,				metadata={
          '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
      }    ,				)
  __UpperCAmelCase				:				Optional[str]   =					field(
      default=lowercase__    ,				metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}    ,				)
  __UpperCAmelCase				:				Optional[List[str]]   =					list_field(
      default=lowercase__    ,				metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}	)





def 					UpperCAmelCase_		(__a      :    Tuple ):






   """simple docstring"""

   try:
      int(__a )
      return True
   except ValueError:
      return False





def 					UpperCAmelCase_		(__a      :    Dict ):






   """simple docstring"""

   try:
      float(__a )
      return True
   except ValueError:
      return False







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     str							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   args
     _a				:	Tuple			=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

     with open(self.args.csv_file							,newline=''       ) as csv_file:
        _a				:	str			=   csv.DictReader(_a       )
        for row in reader:
           _a				:	Dict			=   row['model']
           self.result_dict[model_name]["bsz"].append(int(row['batch_size']       )       )
           self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']       )       )
           if can_convert_to_int(row['result']       ):
              # value is not None
              _a				:	Any			=   int(row['result']       )
           elif can_convert_to_float(row['result']       ):
              # value is not None
              _a				:	Tuple			=   float(row['result']       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a,  _a				:	Dict			=   plt.subplots()
     _a				:	Optional[Any]			=   'Time usage' if self.args.is_time else 'Memory usage'
     _a				:	Union[str, Any]			=   title_str + ' for training' if self.args.is_train else title_str + ' for inference'

     if not self.args.no_log_scale:
        # set logarithm scales
        ax.set_xscale('log'       )
        ax.set_yscale('log'       )

     for axis in [ax.xaxis, ax.yaxis]:
        axis.set_major_formatter(ScalarFormatter()       )

     for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
        _a				:	Optional[int]			=   sorted(set(self.result_dict[model_name]['bsz']       )       )
        _a				:	Dict			=   sorted(set(self.result_dict[model_name]['seq_len']       )       )
        _a				:	Any			=   self.result_dict[model_name]['result']

        ((_a),  (_a))				:	List[Any]			=   (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        _a				:	Union[str, Any]			=   (
            model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
        )

        for inner_loop_value in inner_loop_array:
           if self.args.plot_along_batch:
              _a				:	int			=   np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,dtype=_a							,)
           else:
              _a				:	Any			=   np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,dtype=np.floataa							,)

           ((_a),  (_a))				:	int			=   (
               ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
           )

           _a				:	Dict			=   np.asarray(_a							,_a       )[: len(_a       )]
           plt.scatter(
               _a							,_a							,label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""       )
           plt.plot(_a							,_a							,'--'       )

        title_str += F""" {label_model_name} vs."""

     _a				:	str			=   title_str[:-4]
     _a				:	List[str]			=   'Time in s' if self.args.is_time else 'Memory in MB'

     # plot
     plt.title(_a       )
     plt.xlabel(_a       )
     plt.ylabel(_a       )
     plt.legend()

     if self.args.figure_png_file is not None:
        plt.savefig(self.args.figure_png_file       )
     else:
        plt.show()





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Optional[int]			=   HfArgumentParser(__a )
   _a				:	Optional[Any]			=   parser.parse_args_into_dataclasses()[0]
   _a				:	Optional[Any]			=   Plot(args=__a )
   plot.plot()


if __name__ == "__main__":
  main()





'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


__lowerCAmelCase         =    1_6
__lowerCAmelCase         =    3_2





def 					UpperCAmelCase_		(__a      :    Accelerator ,	__a      :    DatasetDict ,	__a      :    List[int] ,	__a      :    List[int] ,	__a      :    int = 1_6 ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   AutoTokenizer.from_pretrained('bert-base-cased' )
   _a				:	str			=   DatasetDict(
       {
           'train': dataset['train'].select(__a ),
           'validation': dataset['train'].select(__a ),
           'test': dataset['validation'],
       } )

   def tokenize_function(__a      :    List[Any] ):
      # max_length=None => use the model max length (it's actually the default)
      _a				:	Optional[int]			=   tokenizer(examples['sentence1'] ,	examples['sentence2'] ,	truncation=__a ,	max_length=__a )
      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():
      _a				:	List[str]			=   datasets.map(
          __a ,	batched=__a ,	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
   _a				:	List[Any]			=   tokenized_datasets.rename_column('label' ,	'labels' )

   def collate_fn(__a      :    int ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _a				:	Dict			=   1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _a				:	Tuple			=   1_6
      elif accelerator.mixed_precision != "no":
         _a				:	List[Any]			=   8
      else:
         _a				:	List[Any]			=   None

      return tokenizer.pad(
          __a ,	padding='longest' ,	max_length=__a ,	pad_to_multiple_of=__a ,	return_tensors='pt' ,	)

   # Instantiate dataloaders.
   _a				:	Any			=   DataLoader(
       tokenized_datasets['train'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )
   _a				:	Optional[int]			=   DataLoader(
       tokenized_datasets['validation'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   _a				:	Optional[Any]			=   DataLoader(
       tokenized_datasets['test'] ,	shuffle=__a ,	collate_fn=__a ,	batch_size=__a )

   return train_dataloader, eval_dataloader, test_dataloader





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Union[str, Any] ):






   """simple docstring"""

   _a				:	Dict			=   []
   # Download the dataset
   _a				:	Tuple			=   load_dataset('glue' ,	'mrpc' )
   # Create our splits
   _a				:	Union[str, Any]			=   StratifiedKFold(n_splits=int(args.num_folds ) )
   # Initialize accelerator
   _a				:	Any			=   Accelerator(cpu=args.cpu ,	mixed_precision=args.mixed_precision )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _a				:	Optional[Any]			=   config['lr']
   _a				:	Optional[int]			=   int(config['num_epochs'] )
   _a				:	Dict			=   int(config['seed'] )
   _a				:	Dict			=   int(config['batch_size'] )

   _a				:	Optional[int]			=   evaluate.load('glue' ,	'mrpc' )

   # If the batch size is too big we use gradient accumulation
   _a				:	List[Any]			=   1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _a				:	Any			=   batch_size // MAX_GPU_BATCH_SIZE
      _a				:	List[str]			=   MAX_GPU_BATCH_SIZE

   set_seed(__a )

   # New Code #
   # Create our folds:
   _a				:	int			=   kfold.split(np.zeros(datasets['train'].num_rows ) ,	datasets['train']['label'] )
   _a				:	Any			=   []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(__a ):
      _a,  _a,  _a				:	Optional[Any]			=   get_fold_dataloaders(
          __a ,	__a ,	__a ,	__a ,	)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _a				:	Dict			=   AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,	return_dict=__a )

      # 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).
      _a				:	List[Any]			=   model.to(accelerator.device )

      # Instantiate optimizer
      _a				:	List[str]			=   AdamW(params=model.parameters() ,	lr=__a )

      # Instantiate scheduler
      _a				:	List[Any]			=   get_linear_schedule_with_warmup(
          optimizer=__a ,	num_warmup_steps=1_0_0 ,	num_training_steps=(len(__a ) * 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.
      _a,  _a,  _a,  _a,  _a				:	Union[str, Any]			=   accelerator.prepare(
          __a ,	__a ,	__a ,	__a ,	__a )

      # Now we train the model
      for epoch in range(__a ):
         model.train()
         for step, batch in enumerate(__a ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            _a				:	Dict			=   model(**__a )
            _a				:	int			=   outputs.loss
            _a				:	Any			=   loss / gradient_accumulation_steps
            accelerator.backward(__a )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(__a ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            with torch.no_grad():
               _a				:	Union[str, Any]			=   model(**__a )
            _a				:	Tuple			=   outputs.logits.argmax(dim=-1 )
            _a,  _a				:	Any			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=__a ,	references=__a ,	)

         _a				:	List[Any]			=   metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f"""epoch {epoch}:""" ,	__a )

      # New Code #
      # We also run predictions on the test set at the very end
      _a				:	Any			=   []
      for step, batch in enumerate(__a ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device )
         with torch.no_grad():
            _a				:	Tuple			=   model(**__a )
         _a				:	Dict			=   outputs.logits
         _a,  _a				:	Optional[int]			=   accelerator.gather_for_metrics((predictions, batch['labels']) )
         fold_predictions.append(predictions.cpu() )
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(__a ,	dim=0 ) )
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   _a				:	Dict			=   torch.cat(__a ,	dim=0 )
   _a				:	Any			=   torch.stack(__a ,	dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
   _a				:	str			=   metric.compute(predictions=__a ,	references=__a )
   accelerator.print('Average test metrics from all folds:' ,	__a )





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Any			=   argparse.ArgumentParser(description='Simple example of training script.' )
   parser.add_argument(
       '--mixed_precision' ,	type=__a ,	default=__a ,	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.' )
   # New Code #
   parser.add_argument('--num_folds' ,	type=__a ,	default=3 ,	help='The number of splits to perform across the dataset' )
   _a				:	Any			=   parser.parse_args()
   _a				:	int			=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
   training_function(__a ,	__a )


if __name__ == "__main__":
  main()



