





'''simple docstring'''




import argparse

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)
#
# 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
#
########################################################################


lowercase__    :      Dict						=    16
lowercase__    :      Optional[Any]						=    32






def        _lowerCAmelCase			(  __snake_case  : Accelerator ,							__snake_case  : int = 16			)     ->     List[Any]:
     __A						: Any      =       AutoTokenizer.from_pretrained('bert-base-cased'			)
     __A						: int      =       load_dataset('glue' ,							'mrpc'			)

     def tokenize_function(__snake_case  : Optional[Any]			):
          # max_length=None => use the model max length (it's actually the default)
          __A						: Any      =       tokenizer(examples['sentence1'] ,							examples['sentence2'] ,							truncation=__snake_case ,							max_length=__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():
          __A						: Dict      =       datasets.map(
              __snake_case ,							batched=__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
     __A						: Tuple      =       tokenized_datasets.rename_column('label' ,							'labels'			)

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

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

     # Instantiate dataloaders.
     __A						: List[Any]      =       DataLoader(
         tokenized_datasets['train'] ,							shuffle=__snake_case ,							collate_fn=__snake_case ,							batch_size=__snake_case ,							drop_last=__snake_case			)
     __A						: Any      =       DataLoader(
         tokenized_datasets['validation'] ,							shuffle=__snake_case ,							collate_fn=__snake_case ,							batch_size=__snake_case ,							drop_last=(accelerator.mixed_precision == 'fp8') ,							)

     return train_dataloader, eval_dataloader






def        _lowerCAmelCase			(  __snake_case  : Union[str, Any] ,							__snake_case  : Dict			)     ->     List[Any]:
     # Initialize accelerator
     __A						: int      =       Accelerator(cpu=args.cpu ,							mixed_precision=args.mixed_precision			)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A						: Tuple      =       config['lr']
     __A						: str      =       int(config['num_epochs']			)
     __A						: str      =       int(config['seed']			)
     __A						: List[str]      =       int(config['batch_size']			)

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

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

     set_seed(__snake_case			)
     __A ,__A						: str      =       get_dataloaders(__snake_case ,							__snake_case			)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A						: str      =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,							return_dict=__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).
     __A						: Tuple      =       model.to(accelerator.device			)
     # Instantiate optimizer
     __A						: int      =       AdamW(params=model.parameters() ,							lr=__snake_case			)

     # Instantiate scheduler
     __A						: str      =       get_linear_schedule_with_warmup(
         optimizer=__snake_case ,							num_warmup_steps=1_00 ,							num_training_steps=(len(__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.

     __A ,__A ,__A ,__A ,__A						: int      =       accelerator.prepare(
         __snake_case ,							__snake_case ,							__snake_case ,							__snake_case ,							__snake_case			)

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

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

          __A						: Union[str, Any]      =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:' ,							__snake_case			)






def        _lowerCAmelCase			(  )     ->     int:
     __A						: List[str]      =       argparse.ArgumentParser(description='Simple example of training script.'			)
     parser.add_argument(
         '--mixed_precision' ,							type=__snake_case ,							default=__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.'			)
     __A						: Union[str, Any]      =       parser.parse_args()
     __A						: List[Any]      =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(__snake_case ,							__snake_case			)


if __name__ == "__main__":
  main()






'''simple docstring'''




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
  lowercase__    :      List[Any]						=    pd.read_csv('''sample_data.csv''', header=None)
  lowercase__    :      Union[str, Any]						=    df.shape[:1][0]
  # If you're using some other dataset input the target column
  lowercase__    :      Any						=    df.iloc[:, 1:2]
  lowercase__    :      int						=    actual_data.values.reshape(len_data, 1)
  lowercase__    :      int						=    MinMaxScaler().fit_transform(actual_data)
  lowercase__    :      Dict						=    10
  lowercase__    :      List[str]						=    5
  lowercase__    :      Dict						=    20
  lowercase__    :      Dict						=    len_data - periods * look_back
  lowercase__    :      Any						=    actual_data[:division]
  lowercase__    :      Optional[int]						=    actual_data[division - look_back :]
  lowercase__	,							lowercase__    :      Optional[Any]						=    [], []
  lowercase__	,							lowercase__    :      Tuple						=    [], []

  for i in range(0, len(train_data) - forward_days - look_back + 1):
    train_x.append(train_data[i : i + look_back])
    train_y.append(train_data[i + look_back : i + look_back + forward_days])
  for i in range(0, len(test_data) - forward_days - look_back + 1):
    test_x.append(test_data[i : i + look_back])
    test_y.append(test_data[i + look_back : i + look_back + forward_days])
  lowercase__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)