



'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowercase_      :				Optional[int]   					=    16
lowercase_      :				Tuple   					=    32







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Accelerator ,  lowercase_    :      int = 16						):
						lowercase        =							AutoTokenizer.from_pretrained("""bert-base-cased"""						)
						lowercase        =							load_dataset("""glue""" ,  """mrpc"""						)

						def tokenize_function(lowercase_    :      int						):
												# max_length=None => use the model max length (it's actually the default)
												lowercase        =							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        =							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        =							tokenized_datasets.rename_column("""label""" ,  """labels"""						)

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

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

						# Instantiate dataloaders.
						lowercase        =							DataLoader(
						    tokenized_datasets["""train"""] ,  shuffle=lowercase_ ,  collate_fn=lowercase_ ,  batch_size=lowercase_						)
						lowercase        =							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

						lowercase_      :				Any   					=    mocked_dataloaders  # noqa: F811







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Dict						):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,  lowercase_						) == "1":
												lowercase        =							2
						# Initialize accelerator
						lowercase        =							Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase        =							config["""lr"""]
						lowercase        =							int(config["""num_epochs"""]						)
						lowercase        =							int(config["""seed"""]						)
						lowercase        =							int(config["""batch_size"""]						)

						lowercase        =							evaluate.load("""glue""" ,  """mrpc"""						)

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

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

						# Instantiate optimizer
						lowercase        =							AdamW(params=model.parameters() ,  lr=lowercase_						)

						# Instantiate scheduler
						lowercase        =							get_linear_schedule_with_warmup(
						    optimizer=lowercase_ ,  num_warmup_steps=100 ,  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        =							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        =							model(**lowercase_						)
																		lowercase        =							outputs.loss
																		lowercase        =							loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												lowercase        =							0
												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        =							model(**lowercase_						)
																		lowercase        =							outputs.logits.argmax(dim=-1						)
																		lowercase     ,   lowercase        =							accelerator.gather((predictions, batch["""labels"""])						)
																		# New Code #
																		# First we check if it's a distributed system
																		if accelerator.use_distributed:
																								# Then see if we're on the last batch of our eval dataloader
																								if step == len(lowercase_						) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														lowercase        =							predictions[: len(eval_dataloader.dataset						) - samples_seen]
																														lowercase        =							references[: len(eval_dataloader.dataset						) - samples_seen]
																								else:
																														# Otherwise we add the number of samples seen
																														samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																		metric.add_batch(
																		    predictions=lowercase_ ,  references=lowercase_ ,  )

												lowercase        =							metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:""" ,  lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							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."""						)
						lowercase        =							parser.parse_args()
						lowercase        =							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowercase_ ,  lowercase_						)


if __name__ == "__main__":
						main()







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      str						):
						lowercase        =							""""""
						for i in table:
												res += inp[i - 1]
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):
						return data[1:] + data[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict						):
						lowercase        =							""""""
						for i in range(len(lowercase_						)						):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							int("""0b""" + data[0] + data[-1] ,  2						)
						lowercase        =							int("""0b""" + data[1:3] ,  2						)
						return bin(s[row][col]						)[2:]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      Tuple ,  lowercase_    :      Tuple ,  lowercase_    :      str ,  lowercase_    :      Any						):
						lowercase        =							message[:4]
						lowercase        =							message[4:]
						lowercase        =							apply_table(lowercase_ ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						lowercase        =							apply_sbox(lowercase_ ,  temp[:4]						)  # noqa: E741
						lowercase        =							apply_sbox(lowercase_ ,  temp[4:]						)
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + l  # noqa: E741
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + r
						lowercase        =							apply_table(l + r ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						return temp + right


if __name__ == "__main__":
						lowercase_      :				Tuple   					=    input('''Enter 10 bit key: ''')
						lowercase_      :				Any   					=    input('''Enter 8 bit message: ''')

						lowercase_      :				Dict   					=    [6, 3, 7, 4, 8, 5, 10, 9]
						lowercase_      :				str   					=    [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
						lowercase_      :				List[Any]   					=    [2, 4, 3, 1]
						lowercase_      :				List[str]   					=    [2, 6, 3, 1, 4, 8, 5, 7]
						lowercase_      :				Tuple   					=    [4, 1, 3, 5, 7, 2, 8, 6]
						lowercase_      :				Optional[Any]   					=    [4, 1, 2, 3, 2, 3, 4, 1]
						lowercase_      :				List[str]   					=    [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
						lowercase_      :				List[Any]   					=    [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

						# key generation
						lowercase_      :				Union[str, Any]   					=    apply_table(key, paa_table)
						lowercase_      :				Optional[Any]   					=    temp[:5]
						lowercase_      :				int   					=    temp[5:]
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				int   					=    left_shift(right)
						lowercase_      :				Tuple   					=    apply_table(left + right, pa_table)
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				Optional[Any]   					=    left_shift(right)
						lowercase_      :				Union[str, Any]   					=    left_shift(left)
						lowercase_      :				Union[str, Any]   					=    left_shift(right)
						lowercase_      :				Optional[int]   					=    apply_table(left + right, pa_table)

						# encryption
						lowercase_      :				int   					=    apply_table(message, IP)
						lowercase_      :				Dict   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Any   					=    temp[4:] + temp[:4]
						lowercase_      :				List[Any]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Tuple   					=    apply_table(temp, IP_inv)
						print('''Cipher text is:''', CT)

						# decryption
						lowercase_      :				List[str]   					=    apply_table(CT, IP)
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    temp[4:] + temp[:4]
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    apply_table(temp, IP_inv)
						print('''Plain text after decypting is:''', PT)


