





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 the experiment tracking capability,
# 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__   : Any			=				1_6
lowerCamelCase__   : Optional[int]			=				3_2







def        __lowerCamelCase (							__a       :Accelerator   ,					__a       :int = 1_6   )		->    Optional[Any]:






		"""simple docstring"""

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

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

		def collate_fn(__a       :Any   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				A__  				=       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__  				=       1_6
				elif accelerator.mixed_precision != "no":
						A__  				=       8
				else:
						A__  				=       None

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

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

					lowerCamelCase__   : List[str]			=				mocked_dataloaders  # noqa: F811







def        __lowerCamelCase (							__a       :List[Any]   ,					__a       :List[str]   )		->    Optional[Any]:






		"""simple docstring"""

		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,					__a   ) == "1":
				A__  				=       2
		# Initialize Accelerator

		# New Code #
		# We pass in "all" to `log_with` to grab all available trackers in the environment
		# Note: If using a custom `Tracker` class, should be passed in here such as:
		# >>> log_with = ["all", MyCustomTrackerClassInstance()]
		if args.with_tracking:
				A__  				=       Accelerator(
				    cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with="""all"""   ,					project_dir=args.project_dir   )
		else:
				A__  				=       Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision   )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A__  				=       config["""lr"""]
		A__  				=       int(config["""num_epochs"""]   )
		A__  				=       int(config["""seed"""]   )
		A__  				=       int(config["""batch_size"""]   )
		set_seed(__a   )

		A__				,      A__  				=       get_dataloaders(__a   ,					__a   )
		A__  				=       evaluate.load("""glue"""   ,					"""mrpc"""   )

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

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A__  				=       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).
		A__  				=       model.to(accelerator.device   )

		# Instantiate optimizer
		A__  				=       AdamW(params=model.parameters()   ,					lr=__a   )

		# Instantiate scheduler
		A__  				=       get_linear_schedule_with_warmup(
		    optimizer=__a   ,					num_warmup_steps=1_0_0   ,					num_training_steps=(len(__a   ) * 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__  				=       accelerator.prepare(
		    __a   ,					__a   ,					__a   ,					__a   ,					__a   )

		# New Code #
		# We need to initialize the trackers we use. Overall configurations can also be stored
		if args.with_tracking:
				A__  				=       os.path.split(__a   )[-1].split("""."""   )[0]
				accelerator.init_trackers(__a   ,					__a   )

		# Now we train the model
		for epoch in range(__a   ):
				model.train()
				# New Code #
				# For our tracking example, we will log the total loss of each epoch
				if args.with_tracking:
						A__  				=       0
				for step, batch in enumerate(__a   ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device   )
						A__  				=       model(**__a   )
						A__  				=       outputs.loss
						# New Code #
						if args.with_tracking:
								total_loss += loss.detach().float()
						A__  				=       loss / gradient_accumulation_steps
						accelerator.backward(__a   )
						if step % gradient_accumulation_steps == 0:
								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` (the default).
						batch.to(accelerator.device   )
						with torch.no_grad():
								A__  				=       model(**__a   )
						A__  				=       outputs.logits.argmax(dim=-1   )
						A__				,      A__  				=       accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
						metric.add_batch(
						    predictions=__a   ,					references=__a   ,					)

				A__  				=       metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}:'   ,					__a   )

				# New Code #
				# To actually log, we call `Accelerator.log`
				# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
				if args.with_tracking:
						accelerator.log(
						    {
						        """accuracy""": eval_metric["""accuracy"""],
						        """f1""": eval_metric["""f1"""],
						        """train_loss""": total_loss.item() / len(__a   ),
						        """epoch""": epoch,
						    }   ,					step=__a   ,					)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
		if args.with_tracking:
				accelerator.end_training()







def        __lowerCamelCase (							)		->    int:






		"""simple docstring"""

		A__  				=       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."""   ,					)
		parser.add_argument("""--cpu"""   ,					action="""store_true"""   ,					help="""If passed, will train on the CPU."""   )
		parser.add_argument(
		    """--with_tracking"""   ,					action="""store_true"""   ,					help="""Whether to load in all available experiment trackers from the environment and use them for logging."""   ,					)
		parser.add_argument(
		    """--project_dir"""   ,					type=__a   ,					default="""logs"""   ,					help="""Location on where to store experiment tracking logs` and relevent project information"""   ,					)
		A__  				=       parser.parse_args()
		A__  				=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
		training_function(__a   ,					__a   )


if __name__ == "__main__":
					main()










import math







def        __lowerCamelCase (							)		->    None:






		"""simple docstring"""

		A__  				=       input("""Enter message: """   )
		A__  				=       int(input(F'Enter key [2-{len(__a   ) - 1}]: '   )   )
		A__  				=       input("""Encryption/Decryption [e/d]: """   )

		if mode.lower().startswith("""e"""   ):
				A__  				=       encrypt_message(__a   ,					__a   )
		elif mode.lower().startswith("""d"""   ):
				A__  				=       decrypt_message(__a   ,					__a   )

		# Append pipe symbol (vertical bar) to identify spaces at the end.
		print(F'Output:\n{text + "|"}'   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       [""""""] * key
		for col in range(__a   ):
				A__  				=       col
				while pointer < len(__a   ):
						cipher_text[col] += message[pointer]
						pointer += key
		return "".join(__a   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       math.ceil(len(__a   ) / key   )
		A__  				=       key
		A__  				=       (num_cols * num_rows) - len(__a   )
		A__  				=       [""""""] * num_cols
		A__  				=       0
		A__  				=       0

		for symbol in message:
				plain_text[col] += symbol
				col += 1

				if (
				    (col == num_cols)
				    or (col == num_cols - 1)
				    and (row >= num_rows - num_shaded_boxes)
				):
						A__  				=       0
						row += 1

		return "".join(__a   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()



