



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


snake_case    :						Union[str, Any]  =						1_6
snake_case    :						str  =						3_2
def 	snake_case__       (					__lowercase							,     __lowercase = 1_6       )		->		List[str]:







      """simple docstring"""

      A__	:							Any						    = AutoTokenizer.from_pretrained("bert-base-cased"       )
      A__	:							Dict						    = load_dataset("glue"							,     "mrpc"       )

      def tokenize_function(__lowercase       ):
            # max_length=None => use the model max length (it's actually the default)
            A__	:							List[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():
            A__	:							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
      A__	:							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.
            A__	:							Optional[int]						    = 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__	:							Optional[Any]						    = 1_6
            elif accelerator.mixed_precision != "no":
                  A__	:							str						    = 8
            else:
                  A__	:							Any						    = None

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

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

      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

  snake_case    :						int  =						mocked_dataloaders  # noqa: F811
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







      """simple docstring"""

      if os.environ.get("TESTING_MOCKED_DATALOADERS"							,     __lowercase       ) == "1":
            A__	:							Optional[Any]						    = 2
      # New Code #
      A__	:							Optional[int]						    = int(args.gradient_accumulation_steps       )
      # Initialize accelerator
      A__	:							int						    = Accelerator(
          cpu=args.cpu							,     mixed_precision=args.mixed_precision							,     gradient_accumulation_steps=__lowercase       )
      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
      A__	:							Tuple						    = config["lr"]
      A__	:							Dict						    = int(config["num_epochs"]       )
      A__	:							Optional[Any]						    = int(config["seed"]       )
      A__	:							Dict						    = int(config["batch_size"]       )

      A__	:							str						    = evaluate.load("glue"							,     "mrpc"       )

      set_seed(__lowercase       )
      A__	:							Union[str, Any]						    = get_dataloaders(__lowercase							,     __lowercase       )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A__	:							Tuple						    = 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).
      A__	:							int						    = model.to(accelerator.device       )

      # Instantiate optimizer
      A__	:							Dict						    = AdamW(params=model.parameters()							,     lr=__lowercase       )

      # Instantiate scheduler
      A__	:							str						    = get_linear_schedule_with_warmup(
          optimizer=__lowercase							,     num_warmup_steps=1_0_0							,     num_training_steps=(len(__lowercase       ) * 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.
      A__	:							Optional[int]						    = 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       )
                  # 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(__lowercase       ):
                        A__	:							List[str]						    = model(**__lowercase       )
                        A__	:							Any						    = output.loss
                        accelerator.backward(__lowercase       )
                        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():
                        A__	:							str						    = model(**__lowercase       )
                  A__	:							Dict						    = outputs.logits.argmax(dim=-1       )
                  A__	:							Optional[Any]						    = accelerator.gather_for_metrics((predictions, batch["labels"])       )
                  metric.add_batch(
                      predictions=__lowercase							,     references=__lowercase							,     )

            A__	:							Tuple						    = metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'							,     __lowercase       )
def 	snake_case__       (					)		->		Optional[int]:







      """simple docstring"""

      A__	:							Optional[Any]						    = 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."							,     )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"							,     type=__lowercase							,     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."       )
      A__	:							str						    = parser.parse_args()
      A__	:							Tuple						    = {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__lowercase							,     __lowercase       )


if __name__ == "__main__":
  main()




snake_case    :						Tuple  =						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def 	snake_case__       (					)		->		None:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = input("Enter message: "       )
						A__	:							Tuple						    = input("Enter key [alphanumeric]: "       )
						A__	:							Optional[Any]						    = input("Encrypt/Decrypt [e/d]: "       )

						if mode.lower().startswith("e"       ):
												A__	:							Tuple						    = "encrypt"
												A__	:							str						    = encrypt_message(__lowercase							,     __lowercase       )
						elif mode.lower().startswith("d"       ):
												A__	:							Optional[Any]						    = "decrypt"
												A__	:							Union[str, Any]						    = decrypt_message(__lowercase							,     __lowercase       )

						print(F'\n{mode.title()}ed message:'       )
						print(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "encrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "decrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Dict						    = []
						A__	:							Union[str, Any]						    = 0
						A__	:							List[Any]						    = key.upper()

						for symbol in message:
												A__	:							List[str]						    = LETTERS.find(symbol.upper()       )
												if num != -1:
																		if mode == "encrypt":
																								num += LETTERS.find(key[key_index]       )
																		elif mode == "decrypt":
																								num -= LETTERS.find(key[key_index]       )

																		num %= len(__lowercase       )

																		if symbol.isupper():
																								translated.append(LETTERS[num]       )
																		elif symbol.islower():
																								translated.append(LETTERS[num].lower()       )

																		key_index += 1
																		if key_index == len(__lowercase       ):
																								A__	:							Optional[int]						    = 0
												else:
																		translated.append(__lowercase       )
						return "".join(__lowercase       )


if __name__ == "__main__":
		main()