
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__    :		List[str]          =   1_6
lowerCamelCase__    :		Optional[Any]          =   3_2

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = 16   )		->	Optional[Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      AutoTokenizer.from_pretrained("""bert-base-cased"""   )
  lowercase__  :   List[str]    		=      DatasetDict(
      {
          """train""": dataset["""train"""].select(lowercase_   ),
          """validation""": dataset["""train"""].select(lowercase_   ),
          """test""": dataset["""validation"""],
      }   )

  def tokenize_function(lowercase_   ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase__  :   str    		=      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
  # starting with the main process first:
  with accelerator.main_process_first():
    lowercase__  :   Optional[int]    		=      datasets.map(
        lowercase_ , batched=lowercase_ , 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
  lowercase__  :   Union[str, 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.
    lowercase__  :   Any    		=      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":
      lowercase__  :   Optional[int]    		=      16
    elif accelerator.mixed_precision != "no":
      lowercase__  :   Tuple    		=      8
    else:
      lowercase__  :   Optional[Any]    		=      None

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

  # Instantiate dataloaders.
  lowercase__  :   str    		=      DataLoader(
      tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )
  lowercase__  :   str    		=      DataLoader(
      tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )

  lowercase__  :   Dict    		=      DataLoader(
      tokenized_datasets["""test"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_   )

  return train_dataloader, eval_dataloader, test_dataloader

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Optional[Any]:


  '''simple docstring'''






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

  lowercase__  :   List[str]    		=      evaluate.load("""glue""" , """mrpc"""   )

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

  set_seed(lowercase_   )

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

    # 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).
    lowercase__  :   int    		=      model.to(accelerator.device   )

    # Instantiate optimizer
    lowercase__  :   Any    		=      AdamW(params=model.parameters() , lr=lowercase_   )

    # Instantiate scheduler
    lowercase__  :   Dict    		=      get_linear_schedule_with_warmup(
        optimizer=lowercase_ , num_warmup_steps=1_00 , num_training_steps=(len(lowercase_   ) * 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.
    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__		,    lowercase__  :   List[str]    		=      accelerator.prepare(
        lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_   )

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

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

      lowercase__  :   Optional[Any]    		=      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'epoch {epoch}:' , lowercase_   )

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

def 		UpperCamelCase	(      )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      argparse.ArgumentParser(description="""Simple example of training script."""   )
  parser.add_argument(
      """--mixed_precision""" , type=lowercase_ , default=lowercase_ , 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=lowercase_ , default=3 , help="""The number of splits to perform across the dataset"""   )
  lowercase__  :   Any    		=      parser.parse_args()
  lowercase__  :   Union[str, Any]    		=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase_ , lowercase_   )


if __name__ == "__main__":
     main()








from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if components is None:
     lowercase__  :   List[str]    		=      []
   lowercase__  :   Dict    		=      list(SCREAMING_SNAKE_CASE_)


 def __len__(					self):


   '''simple docstring'''




   return len(self.__components)


 def __str__(					self):


   '''simple docstring'''




   return "(" + ",".join(map(SCREAMING_SNAKE_CASE_					,  self.__components)) + ")"


 def __add__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      len(self)
   if size == len(SCREAMING_SNAKE_CASE_):
     lowercase__  :   List[str]    		=      [self.__components[i] + other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
     return Vector(SCREAMING_SNAKE_CASE_)
   else:
     raise Exception("""must have the same size""")


 def __sub__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      len(self)
   if size == len(SCREAMING_SNAKE_CASE_):
     lowercase__  :   Optional[Any]    		=      [self.__components[i] - other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
     return Vector(SCREAMING_SNAKE_CASE_)
   else:  # error case
     raise Exception("""must have the same size""")


 @overload
 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   ...


 @overload
 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   ...


 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if isinstance(SCREAMING_SNAKE_CASE_					,  (float, int)):
     lowercase__  :   Optional[int]    		=      [c * other for c in self.__components]
     return Vector(SCREAMING_SNAKE_CASE_)
   elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) and len(self) == len(SCREAMING_SNAKE_CASE_):
     lowercase__  :   Dict    		=      len(self)
     lowercase__  :   Optional[Any]    		=      [self.__components[i] * other.component(SCREAMING_SNAKE_CASE_) for i in range(SCREAMING_SNAKE_CASE_)]
     return sum(SCREAMING_SNAKE_CASE_)
   else:  # error case
     raise Exception("""invalid operand!""")


 def      lowercase__     (					self):


   '''simple docstring'''




   return Vector(self.__components)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_) and -len(self.__components) <= i < len(self.__components):
     return self.__components[i]
   else:
     raise Exception("""index out of range""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   assert -len(self.__components) <= pos < len(self.__components)
   lowercase__  :   List[Any]    		=      value


 def      lowercase__     (					self):


   '''simple docstring'''




   if len(self.__components) == 0:
     raise Exception("""Vector is empty""")
   lowercase__  :   Union[str, Any]    		=      [c**2 for c in self.__components]
   return math.sqrt(sum(SCREAMING_SNAKE_CASE_))



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = False):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      self * other
   lowercase__  :   Optional[Any]    		=      self.euclidean_length() * other.euclidean_length()
   if deg:
     return math.degrees(math.acos(num / den))
   else:
     return math.acos(num / den)

def 		UpperCamelCase	(      lowercase_   )		->	Vector:


  '''simple docstring'''






  assert isinstance(lowercase_ , lowercase_   )
  return Vector([0] * dimension   )

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Vector:


  '''simple docstring'''






  assert isinstance(lowercase_ , lowercase_   ) and (isinstance(lowercase_ , lowercase_   ))
  lowercase__  :   Union[str, Any]    		=      [0] * dimension
  lowercase__  :   Any    		=      1
  return Vector(lowercase_   )

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Vector:


  '''simple docstring'''






  assert (
      isinstance(lowercase_ , lowercase_   )
      and isinstance(lowercase_ , lowercase_   )
      and (isinstance(lowercase_ , (int, float)   ))
  )
  return x * scalar + y

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Vector:


  '''simple docstring'''






  random.seed(lowercase_   )
  lowercase__  :   int    		=      [random.randint(lowercase_ , lowercase_   ) for _ in range(lowercase_   )]
  return Vector(lowercase_   )



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      matrix
   lowercase__  :   Any    		=      w
   lowercase__  :   Any    		=      h


 def __str__(					self):


   '''simple docstring'''




   lowercase__  :   str    		=      """"""
   for i in range(self.__height):
     ans += "|"
     for j in range(self.__width):
       if j < self.__width - 1:
         ans += str(self.__matrix[i][j]) + ","
       else:
         ans += str(self.__matrix[i][j]) + "|\n"
   return ans


 def __add__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.__width == other.width() and self.__height == other.height():
     lowercase__  :   Tuple    		=      []
     for i in range(self.__height):
       lowercase__  :   Tuple    		=      [
           self.__matrix[i][j] + other.component(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
           for j in range(self.__width)
       ]
       matrix.append(SCREAMING_SNAKE_CASE_)
     return Matrix(SCREAMING_SNAKE_CASE_					,  self.__width					,  self.__height)
   else:
     raise Exception("""matrix must have the same dimension!""")


 def __sub__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.__width == other.width() and self.__height == other.height():
     lowercase__  :   Optional[int]    		=      []
     for i in range(self.__height):
       lowercase__  :   List[str]    		=      [
           self.__matrix[i][j] - other.component(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
           for j in range(self.__width)
       ]
       matrix.append(SCREAMING_SNAKE_CASE_)
     return Matrix(SCREAMING_SNAKE_CASE_					,  self.__width					,  self.__height)
   else:
     raise Exception("""matrices must have the same dimension!""")


 @overload
 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   ...


 @overload
 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   ...


 def __mul__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):  # matrix-vector
     if len(SCREAMING_SNAKE_CASE_) == self.__width:
       lowercase__  :   List[Any]    		=      zero_vector(self.__height)
       for i in range(self.__height):
         lowercase__  :   Union[str, Any]    		=      [
             self.__matrix[i][j] * other.component(SCREAMING_SNAKE_CASE_)
             for j in range(self.__width)
         ]
         ans.change_component(SCREAMING_SNAKE_CASE_					,  sum(SCREAMING_SNAKE_CASE_))
       return ans
     else:
       raise Exception(
           """vector must have the same size as the """
           """number of columns of the matrix!""")
   elif isinstance(SCREAMING_SNAKE_CASE_					,  (int, float)):  # matrix-scalar
     lowercase__  :   Tuple    		=      [
         [self.__matrix[i][j] * other for j in range(self.__width)]
         for i in range(self.__height)
     ]
     return Matrix(SCREAMING_SNAKE_CASE_					,  self.__width					,  self.__height)
   return None


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.__height


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.__width


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if 0 <= x < self.__height and 0 <= y < self.__width:
     return self.__matrix[x][y]
   else:
     raise Exception("""change_component: indices out of bounds""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if 0 <= x < self.__height and 0 <= y < self.__width:
     lowercase__  :   Tuple    		=      value
   else:
     raise Exception("""change_component: indices out of bounds""")


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.__height != self.__width:
     raise Exception("""Matrix is not square""")
   lowercase__  :   List[Any]    		=      self.__matrix[:x] + self.__matrix[x + 1 :]
   for i in range(len(SCREAMING_SNAKE_CASE_)):
     lowercase__  :   List[str]    		=      minor[i][:y] + minor[i][y + 1 :]
   return Matrix(SCREAMING_SNAKE_CASE_					,  self.__width - 1					,  self.__height - 1).determinant()


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.__height != self.__width:
     raise Exception("""Matrix is not square""")
   if 0 <= x < self.__height and 0 <= y < self.__width:
     return (-1) ** (x + y) * self.minor(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)
   else:
     raise Exception("""Indices out of bounds""")



 def      lowercase__     (					self):


   '''simple docstring'''




   if self.__height != self.__width:
     raise Exception("""Matrix is not square""")
   if self.__height < 1:
     raise Exception("""Matrix has no element""")
   elif self.__height == 1:
     return self.__matrix[0][0]
   elif self.__height == 2:
     return (
         self.__matrix[0][0] * self.__matrix[1][1]
         - self.__matrix[0][1] * self.__matrix[1][0]
     )
   else:
     lowercase__  :   Optional[int]    		=      [
         self.__matrix[0][y] * self.cofactor(0					,  SCREAMING_SNAKE_CASE_) for y in range(self.__width)
     ]
     return sum(SCREAMING_SNAKE_CASE_)

def 		UpperCamelCase	(      lowercase_   )		->	Matrix:


  '''simple docstring'''






  lowercase__  :   list[list[float]]    		=      [[0] * n for _ in range(lowercase_   )]
  return Matrix(lowercase_ , lowercase_ , lowercase_   )



def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	Matrix:


  '''simple docstring'''






  random.seed(lowercase_   )
  lowercase__  :   list[list[float]]    		=      [
      [random.randint(lowercase_ , lowercase_   ) for _ in range(lowercase_   )] for _ in range(lowercase_   )
  ]
  return Matrix(lowercase_ , lowercase_ , lowercase_   )






