


'''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 the checkpointing 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__  =     16
lowerCAmelCase__  =     32



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 16     ):






       """simple docstring"""





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

       def tokenize_function(UpperCAmelCase     ):
              # max_length=None => use the model max length (it's actually the default)
              snake_case__					:	List[Any]          =					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():
              snake_case__					:	List[str]          =					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
       snake_case__					:	Optional[Any]          =					tokenized_datasets.rename_column("""label"""    ,       """labels"""     )

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

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

       # Instantiate dataloaders.
       snake_case__					:	int          =					DataLoader(
           tokenized_datasets["""train"""]    ,       shuffle=lowercase_    ,       collate_fn=lowercase_    ,       batch_size=lowercase_     )
       snake_case__					:	Any          =					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

     lowerCAmelCase__  =     mocked_dataloaders  # noqa: F811



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





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

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps    ,       """isdigit"""     ):
              if args.checkpointing_steps == "epoch":
                     snake_case__					:	Optional[Any]          =					args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     snake_case__					:	Tuple          =					int(args.checkpointing_steps     )
              else:
                     raise ValueError(
                         f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
       else:
              snake_case__					:	Union[str, Any]          =					None

       set_seed(lowercase_     )

       snake_case__       ,   snake_case__					:	Union[str, Any]          =					get_dataloaders(lowercase_    ,       lowercase_     )
       snake_case__					:	str          =					evaluate.load("""glue"""    ,       """mrpc"""     )

       # If the batch size is too big we use gradient accumulation
       snake_case__					:	Optional[int]          =					1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              snake_case__					:	int          =					batch_size // MAX_GPU_BATCH_SIZE
              snake_case__					:	List[str]          =					MAX_GPU_BATCH_SIZE

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

       # Instantiate optimizer
       snake_case__					:	Tuple          =					AdamW(params=model.parameters()    ,       lr=lowercase_     )

       # Instantiate scheduler
       snake_case__					:	Any          =					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.
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__					:	Any          =					accelerator.prepare(
           lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_    ,       lowercase_     )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       snake_case__					:	Any          =					0
       # We also need to keep track of the stating epoch so files are named properly
       snake_case__					:	Dict          =					0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
                     accelerator.load_state(args.resume_from_checkpoint     )
                     snake_case__					:	str          =					os.path.basename(args.resume_from_checkpoint     )
              else:
                     # Get the most recent checkpoint
                     snake_case__					:	List[Any]          =					[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime     )
                     snake_case__					:	str          =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              snake_case__					:	Any          =					os.path.splitext(lowercase_     )[0]

              if "epoch" in training_difference:
                     snake_case__					:	List[Any]          =					int(training_difference.replace("""epoch_"""    ,       """"""     )     ) + 1
                     snake_case__					:	str          =					None
              else:
                     snake_case__					:	Optional[Any]          =					int(training_difference.replace("""step_"""    ,       """"""     )     )
                     snake_case__					:	Union[str, Any]          =					resume_step // len(lowercase_     )
                     resume_step -= starting_epoch * len(lowercase_     )

    # Now we train the model
       for epoch in range(lowercase_    ,       lowercase_     ):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     snake_case__					:	Optional[int]          =					accelerator.skip_first_batches(lowercase_    ,       lowercase_     )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     snake_case__					:	List[str]          =					train_dataloader
              for step, batch in enumerate(lowercase_     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device     )
                     snake_case__					:	List[Any]          =					model(**lowercase_     )
                     snake_case__					:	Tuple          =					outputs.loss
                     snake_case__					:	List[str]          =					loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowercase_    ,       lowercase_     ):
                            snake_case__					:	List[Any]          =					f"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          snake_case__					:	Optional[Any]          =					os.path.join(args.output_dir    ,       lowercase_     )
                                   accelerator.save_state(lowercase_     )

              model.eval()
              for step, batch in enumerate(lowercase_     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device     )
                     with torch.no_grad():
                            snake_case__					:	Tuple          =					model(**lowercase_     )
                     snake_case__					:	Dict          =					outputs.logits.argmax(dim=-1     )
                     snake_case__       ,   snake_case__					:	Optional[Any]          =					accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                     metric.add_batch(
                         predictions=lowercase_    ,       references=lowercase_    ,       )

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

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     snake_case__					:	Union[str, Any]          =					f"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            snake_case__					:	Optional[Any]          =					os.path.join(args.output_dir    ,       lowercase_     )
                     accelerator.save_state(lowercase_     )



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	str          =					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."""     )
       parser.add_argument(
           """--checkpointing_steps"""    ,       type=lowercase_    ,       default=lowercase_    ,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""    ,       )
       parser.add_argument(
           """--output_dir"""    ,       type=lowercase_    ,       default="""."""    ,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""    ,       )
       parser.add_argument(
           """--resume_from_checkpoint"""    ,       type=lowercase_    ,       default=lowercase_    ,       help="""If the training should continue from a checkpoint folder."""    ,       )
       snake_case__					:	int          =					parser.parse_args()
       snake_case__					:	List[str]          =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(lowercase_    ,       lowercase_     )


if __name__ == "__main__":
     main()










'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			bin(int.from_bytes(text.encode(lowercase_		,				lowercase_    )		,				"big"    )    )[2:]
		return bits.zfill(8 * ((len(lowercase_    ) + 7) // 8)    )






def 						_lowercase						(      lowercase_		,				lowercase_="utf-8"		,				lowercase_="surrogatepass"    ):



		'''simple docstring'''







		_snake_case      			=			int(lowercase_		,				2    )
		return n.to_bytes((n.bit_length() + 7) // 8		,				"big"    ).decode(lowercase_		,				lowercase_    ) or "\0"






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		if size_par + len(lowercase_    ) <= 2**size_par - (len(lowercase_    ) - 1):
				raise ValueError("size of parity don't match with size of data"    )

		_snake_case      			=			[]
		_snake_case      			=			[]
		_snake_case      			=			[bin(lowercase_    )[2:] for x in range(1		,				size_par + len(lowercase_    ) + 1    )]

		# sorted information data for the size of the output data
		_snake_case      			=			[]
		# data position template + parity
		_snake_case      			=			[]
		# parity bit counter
		_snake_case      			=			0
		# counter position of data bits
		_snake_case      			=			0

		for x in range(1		,				size_par + len(lowercase_    ) + 1    ):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(lowercase_    ) / np.log(2    )).is_integer():
								data_out_gab.append("P"    )
								_snake_case      			=			qtd_bp + 1
						else:
								data_out_gab.append("D"    )
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]    )
						cont_data += 1
				else:
						data_ord.append(lowercase_    )

    # Calculates parity
		_snake_case      			=			0  # parity bit counter
		for bp in range(1		,				size_par + 1    ):
				# Bit counter one for a given parity
				_snake_case      			=			0
				# counter to control the loop reading
				_snake_case      			=			0
				for x in data_ord:
						if x is not None:
								try:
										_snake_case      			=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										_snake_case      			=			"0"
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2    )

				qtd_bp += 1

		# Mount the message
		_snake_case      			=			0  # parity bit counter
		for x in range(0		,				size_par + len(lowercase_    )    ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]    )    )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]    )

		return data_out






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			[]
		# Parity bit counter
		_snake_case      			=			0
		# Counter p data bit reading
		_snake_case      			=			0
		# list of parity received
		_snake_case      			=			[]
		_snake_case      			=			[]

		for x in range(1		,				len(lowercase_    ) + 1    ):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(lowercase_    ) / np.log(2    )).is_integer():
						data_out_gab.append("P"    )
						_snake_case      			=			qtd_bp + 1
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]    )
				else:
						parity_received.append(data[cont_data]    )
				cont_data += 1

		# -----------calculates the parity with the data
		_snake_case      			=			[]
		_snake_case      			=			[]
		_snake_case      			=			[bin(lowercase_    )[2:] for x in range(1		,				size_par + len(lowercase_    ) + 1    )]

		#  sorted information data for the size of the output data
		_snake_case      			=			[]
		# Data position feedback + parity
		_snake_case      			=			[]
		# Parity bit counter
		_snake_case      			=			0
		# Counter p data bit reading
		_snake_case      			=			0

		for x in range(1		,				size_par + len(lowercase_    ) + 1    ):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(lowercase_    ) / np.log(2    )).is_integer():
						data_out_gab.append("P"    )
						_snake_case      			=			qtd_bp + 1
				else:
						data_out_gab.append("D"    )

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]    )
						cont_data += 1
				else:
						data_ord.append(lowercase_    )

    # Calculates parity
		_snake_case      			=			0  # parity bit counter
		for bp in range(1		,				size_par + 1    ):
				# Bit counter one for a certain parity
				_snake_case      			=			0
				# Counter to control loop reading
				_snake_case      			=			0
				for x in data_ord:
						if x is not None:
								try:
										_snake_case      			=			(bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										_snake_case      			=			"0"
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2    )    )

				qtd_bp += 1

		# Mount the message
		_snake_case      			=			0  # Parity bit counter
		for x in range(0		,				size_par + len(lowercase_    )    ):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]    )    )
						cont_bp += 1
				else:
						data_out.append(data_ord[x]    )

		_snake_case      			=			parity_received == parity
		return data_output, ack





