


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
#
########################################################################


UpperCAmelCase    =					16
UpperCAmelCase    =					32






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = 16      )->						List[Any]:




      """simple docstring"""

      snake_case_ =   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
      snake_case_ =   DatasetDict(
          {
              '''train''': dataset['''train'''].select(SCREAMING_SNAKE_CASE      ),
              '''validation''': dataset['''train'''].select(SCREAMING_SNAKE_CASE      ),
              '''test''': dataset['''validation'''],
          }      )

      def tokenize_function(SCREAMING_SNAKE_CASE      ):
            # max_length=None => use the model max length (it's actually the default)
            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
      # starting with the main process first:
      with accelerator.main_process_first():
            snake_case_ =   datasets.map(
                SCREAMING_SNAKE_CASE      ,      batched=SCREAMING_SNAKE_CASE      ,      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
      snake_case_ =   tokenized_datasets.rename_column('''label'''      ,      '''labels'''      )

      def collate_fn(SCREAMING_SNAKE_CASE      ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case_ =   128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case_ =   16
            elif accelerator.mixed_precision != "no":
                  snake_case_ =   8
            else:
                  snake_case_ =   None

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

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

      snake_case_ =   DataLoader(
          tokenized_datasets['''test''']      ,      shuffle=SCREAMING_SNAKE_CASE      ,      collate_fn=SCREAMING_SNAKE_CASE      ,      batch_size=SCREAMING_SNAKE_CASE      )

      return train_dataloader, eval_dataloader, test_dataloader






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

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

      snake_case_ =   evaluate.load('''glue'''      ,      '''mrpc'''      )

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

      set_seed(SCREAMING_SNAKE_CASE      )

      # New Code #
      # Create our folds:
      snake_case_ =   kfold.split(np.zeros(datasets['''train'''].num_rows      )      ,      datasets['''train''']['''label''']      )
      snake_case_ =   []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(SCREAMING_SNAKE_CASE      ):
            snake_case_ ,					snake_case_ ,					snake_case_ =   get_fold_dataloaders(
                SCREAMING_SNAKE_CASE      ,      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)
            snake_case_ =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''      ,      return_dict=SCREAMING_SNAKE_CASE      )

            # 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).
            snake_case_ =   model.to(accelerator.device      )

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

            # Instantiate scheduler
            snake_case_ =   get_linear_schedule_with_warmup(
                optimizer=SCREAMING_SNAKE_CASE      ,      num_warmup_steps=100      ,      num_training_steps=(len(SCREAMING_SNAKE_CASE      ) * 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.
            snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ ,					snake_case_ =   accelerator.prepare(
                SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

            # Now we train the model
            for epoch in range(SCREAMING_SNAKE_CASE      ):
                  model.train()
                  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      )
                        snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
                        snake_case_ =   outputs.loss
                        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()

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

                  snake_case_ =   metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f'''epoch {epoch}:'''      ,      SCREAMING_SNAKE_CASE      )

            # New Code #
            # We also run predictions on the test set at the very end
            snake_case_ =   []
            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():
                        snake_case_ =   model(**SCREAMING_SNAKE_CASE      )
                  snake_case_ =   outputs.logits
                  snake_case_ ,					snake_case_ =   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(SCREAMING_SNAKE_CASE      ,      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:
      snake_case_ =   torch.cat(SCREAMING_SNAKE_CASE      ,      dim=0      )
      snake_case_ =   torch.stack(SCREAMING_SNAKE_CASE      ,      dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
      snake_case_ =   metric.compute(predictions=SCREAMING_SNAKE_CASE      ,      references=SCREAMING_SNAKE_CASE      )
      accelerator.print('''Average test metrics from all folds:'''      ,      SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   argparse.ArgumentParser(description='''Simple example of training script.'''      )
      parser.add_argument(
          '''--mixed_precision'''      ,      type=SCREAMING_SNAKE_CASE      ,      default=SCREAMING_SNAKE_CASE      ,      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=SCREAMING_SNAKE_CASE      ,      default=3      ,      help='''The number of splits to perform across the dataset'''      )
      snake_case_ =   parser.parse_args()
      snake_case_ =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   main()



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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

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







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

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







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   args
       snake_case_ =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

       with open(self.args.csv_file	,   newline=''''''      ) as csv_file:
             snake_case_ =   csv.DictReader(_UpperCAmelCase      )
             for row in reader:
                   snake_case_ =   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
                         snake_case_ =   int(row['''result''']      )
                   elif can_convert_to_float(row['''result''']      ):
                         # value is not None
                         snake_case_ =   float(row['''result''']      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   plt.subplots()
       snake_case_ =   '''Time usage''' if self.args.is_time else '''Memory usage'''
       snake_case_ =   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()      ):
             snake_case_ =   sorted(set(self.result_dict[model_name]['''bsz''']      )      )
             snake_case_ =   sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
             snake_case_ =   self.result_dict[model_name]['''result''']

             ((snake_case_) ,					(snake_case_)) =   (
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             snake_case_ =   (
                 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:
                         snake_case_ =   np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,   dtype=_UpperCAmelCase	,   )
                   else:
                         snake_case_ =   np.asarray(
                             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,   dtype=np.floataa	,   )

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

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

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

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

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

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






def  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()