





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

UpperCamelCase__     =	16
UpperCamelCase__     =	32





def       _UpperCamelCase       (a__				:Accelerator				,     a__				:int = 16    ):






							"""simple docstring"""





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

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

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

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

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

					UpperCamelCase__     =	mocked_dataloaders  # noqa: F811





def       _UpperCamelCase       (a__				:List[Any]				,     a__				:Optional[Any]    ):






							"""simple docstring"""





							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     a__    ) == "1":
														UpperCamelCase__			       =     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:
														UpperCamelCase__			       =     Accelerator(
														    cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     log_with="""all"""				,     project_dir=args.project_dir    )
							else:
														UpperCamelCase__			       =     Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision    )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCamelCase__			       =     config["""lr"""]
							UpperCamelCase__			       =     int(config["""num_epochs"""]    )
							UpperCamelCase__			       =     int(config["""seed"""]    )
							UpperCamelCase__			       =     int(config["""batch_size"""]    )
							set_seed(a__    )

							UpperCamelCase__       ,       UpperCamelCase__			       =     get_dataloaders(a__				,     a__    )
							UpperCamelCase__			       =     evaluate.load("""glue"""				,     """mrpc"""    )

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

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

							# Instantiate optimizer
							UpperCamelCase__			       =     AdamW(params=model.parameters()				,     lr=a__    )

							# Instantiate scheduler
							UpperCamelCase__			       =     get_linear_schedule_with_warmup(
							    optimizer=a__				,     num_warmup_steps=100				,     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.
							UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__			       =     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:
														UpperCamelCase__			       =     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:
																					UpperCamelCase__			       =     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    )
																					UpperCamelCase__			       =     model(**a__    )
																					UpperCamelCase__			       =     outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					UpperCamelCase__			       =     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():
																												UpperCamelCase__			       =     model(**a__    )
																					UpperCamelCase__			       =     outputs.logits.argmax(dim=-1    )
																					UpperCamelCase__       ,       UpperCamelCase__			       =     accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
																					metric.add_batch(
																					    predictions=a__				,     references=a__				,     )

														UpperCamelCase__			       =     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       _UpperCamelCase       ():






							"""simple docstring"""





							UpperCamelCase__			       =     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"""				,     )
							UpperCamelCase__			       =     parser.parse_args()
							UpperCamelCase__			       =     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(a__				,     a__    )


if __name__ == "__main__":
					main()









def       _UpperCamelCase       (a__				:str    ):






							"""simple docstring"""





							UpperCamelCase__			       =     0

							# if input_string is "aba" than new_input_string become "a|b|a"
							UpperCamelCase__			       =     """"""
							UpperCamelCase__			       =     """"""

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(a__    ) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							UpperCamelCase__       ,       UpperCamelCase__			       =     0, 0

							# length[i] shows the length of palindromic substring with center i
							UpperCamelCase__			       =     [1 for i in range(len(a__    )    )]

							# for each character in new_string find corresponding palindromic string
							UpperCamelCase__			       =     0
							for j in range(len(a__    )    ):
														UpperCamelCase__			       =     1 if j > r else min(length[l + r - j] // 2				,     r - j + 1    )
														while (
														    j - k >= 0
														    and j + k < len(a__    )
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														UpperCamelCase__			       =     2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					UpperCamelCase__			       =     j - k + 1  # noqa: E741
																					UpperCamelCase__			       =     j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					UpperCamelCase__			       =     length[j]
																					UpperCamelCase__			       =     j

    # create that string
							UpperCamelCase__			       =     new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


if __name__ == "__main__":
					import doctest

					doctest.testmod()




