'''simple docstring'''

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
# and perform gradient accumulation
#
# 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 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__     :	Optional[Any]				=			16
UpperCamelCase__     :	Dict				=			32







def __UpperCamelCase(   _A					:				Union[str, Any]  ,			_A					:				Dict = 16    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
   UpperCAmelCase__							:				Optional[int]   				=  load_dataset('''glue'''  ,			'''mrpc'''    )

   def tokenize_function(_A					:				str    ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCAmelCase__							:				Optional[Any]   				=  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():
      UpperCAmelCase__							:				Tuple   				=  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
   UpperCAmelCase__							:				Union[str, Any]   				=  tokenized_datasets.rename_column('''label'''  ,			'''labels'''    )

   def collate_fn(_A					:				Union[str, Any]    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCAmelCase__							:				int   				=  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":
         UpperCAmelCase__							:				Optional[Any]   				=  16
      elif accelerator.mixed_precision != "no":
         UpperCAmelCase__							:				int   				=  8
      else:
         UpperCAmelCase__							:				Optional[int]   				=  None

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

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

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 UpperCamelCase__     :	List[Any]				=			mocked_dataloaders  # noqa: F811







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,			__A    ) == "1":
      UpperCAmelCase__							:				Any   				=  2
   # New Code #
   UpperCAmelCase__							:				Any   				=  int(args.gradient_accumulation_steps    )
   # Initialize accelerator
   UpperCAmelCase__							:				Union[str, Any]   				=  Accelerator(
       cpu=args.cpu  ,			mixed_precision=args.mixed_precision  ,			gradient_accumulation_steps=__A    )
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''    )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__							:				List[str]   				=  config["""lr"""]
   UpperCAmelCase__							:				Tuple   				=  int(config['''num_epochs''']    )
   UpperCAmelCase__							:				Tuple   				=  int(config['''seed''']    )
   UpperCAmelCase__							:				Union[str, Any]   				=  int(config['''batch_size''']    )

   UpperCAmelCase__							:				List[str]   				=  evaluate.load('''glue'''  ,			'''mrpc'''    )

   set_seed(__A    )
   UpperCAmelCase__							:				str   				=  get_dataloaders(__A  ,			__A    )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCAmelCase__							:				str   				=  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).
   UpperCAmelCase__							:				str   				=  model.to(accelerator.device    )

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

   # Instantiate scheduler
   UpperCAmelCase__							:				Any   				=  get_linear_schedule_with_warmup(
       optimizer=__A  ,			num_warmup_steps=1_00  ,			num_training_steps=(len(__A    ) * num_epochs)  ,			)

   # 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.
   UpperCAmelCase__							:				str   				=  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    )
         # New code #
         # We use the new `accumulate` context manager to perform gradient accumulation
         # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
         with accelerator.accumulate(__A    ):
            UpperCAmelCase__							:				Dict   				=  model(**__A    )
            UpperCAmelCase__							:				str   				=  output.loss
            accelerator.backward(__A    )
            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():
            UpperCAmelCase__							:				Optional[Any]   				=  model(**__A    )
         UpperCAmelCase__							:				Optional[int]   				=  outputs.logits.argmax(dim=-1    )
         UpperCAmelCase__							:				Union[str, Any]   				=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
         metric.add_batch(
             predictions=__A  ,			references=__A  ,			)

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







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  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.'''  ,			)
   # New Code #
   parser.add_argument(
       '''--gradient_accumulation_steps'''  ,			type=__A  ,			default=1  ,			help='''The number of minibatches to be ran before gradients are accumulated.'''  ,			)
   parser.add_argument('''--cpu'''  ,			action='''store_true'''  ,			help='''If passed, will train on the CPU.'''    )
   UpperCAmelCase__							:				Any   				=  parser.parse_args()
   UpperCAmelCase__							:				Dict   				=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(__A  ,			__A    )


if __name__ == "__main__":
 main()





'''simple docstring'''

# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   return 1 / (1 + np.exp(-z    ))







def __UpperCamelCase(   _A					:				int  ,			_A					:				str    ):
   '''simple docstring'''
   return (-y * np.log(_A    ) - (1 - y) * np.log(1 - h    )).mean()







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Tuple  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  np.dot(_A  ,			_A    )
   return np.sum(y * scores - np.log(1 + np.exp(_A    )    )    )







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Optional[int]  ,			_A					:				List[Any]  ,			_A					:				str=7_00_00    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  np.zeros(x.shape[1]    )

   for iterations in range(_A    ):
      UpperCAmelCase__							:				Tuple   				=  np.dot(_A  ,			_A    )
      UpperCAmelCase__							:				Optional[Any]   				=  sigmoid_function(_A    )
      UpperCAmelCase__							:				Optional[int]   				=  np.dot(x.T  ,			h - y    ) / y.size
      UpperCAmelCase__							:				List[Any]   				=  theta - alpha * gradient  # updating the weights
      UpperCAmelCase__							:				Tuple   				=  np.dot(_A  ,			_A    )
      UpperCAmelCase__							:				Tuple   				=  sigmoid_function(_A    )
      UpperCAmelCase__							:				Optional[int]   				=  cost_function(_A  ,			_A    )
      if iterations % 1_00 == 0:
         print(F'''loss: {j} \t'''    )  # printing the loss after every 100 iterations
   return theta


# In[68]:

if __name__ == "__main__":
  UpperCamelCase__     :	str				=			datasets.load_iris()
  UpperCamelCase__     :	Tuple				=			iris.data[:, :2]
  UpperCamelCase__     :	str				=			(iris.target != 0) * 1

  UpperCamelCase__     :	Any				=			0.1
  UpperCamelCase__     :	List[str]				=			logistic_reg(alpha, x, y, max_iterations=70_000)
  print('theta: ', theta)  # printing the theta i.e our weights vector







  def __UpperCamelCase(   _A					:				Any    ):
     '''simple docstring'''
     return sigmoid_function(
         np.dot(_A  ,			_A    )    )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Any				=			(x[:, 0].min(), x[:, 0].max())
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Optional[Any]				=			(x[:, 1].min(), x[:, 1].max())
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Any				=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  UpperCamelCase__     :	str				=			np.c_[xxa.ravel(), xxa.ravel()]
  UpperCamelCase__     :	Union[str, Any]				=			predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()
  plt.show()




