

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


UpperCAmelCase__ :     Optional[int]				    =    16
UpperCAmelCase__ :     Optional[int]				    =    32






def 					__lowercase      (     _A						,    _A = 16			)     ->						Any:
						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   AutoTokenizer.from_pretrained("""bert-base-cased"""			)
						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   load_dataset("""glue"""						,    """mrpc"""			)

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

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

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE :      str       =   DataLoader(
						    tokenized_datasets["""train"""]						,    shuffle=lowercase_						,    collate_fn=lowercase_						,    batch_size=lowercase_			)
						SCREAMING_SNAKE_CASE :      Optional[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

		UpperCAmelCase__ :     int				    =    mocked_dataloaders  # noqa: F811






def 					__lowercase      (     _A						,    _A			)     ->						int:

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

						SCREAMING_SNAKE_CASE :      Tuple       =   evaluate.load("""glue"""						,    """mrpc"""			)

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE :      List[str]       =   batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE :      Dict       =   MAX_GPU_BATCH_SIZE

						set_seed(lowercase_			)
						SCREAMING_SNAKE_CASE :      List[str]       =   get_dataloaders(lowercase_						,    lowercase_			)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE :      List[Any]       =   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).
						SCREAMING_SNAKE_CASE :      List[str]       =   model.to(accelerator.device			)

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE :      Any       =   AdamW(params=model.parameters()						,    lr=lowercase_			)

						# Instantiate scheduler
						SCREAMING_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.
						SCREAMING_SNAKE_CASE :      List[str]       =   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			)
																		SCREAMING_SNAKE_CASE :      List[str]       =   model(**lowercase_			)
																		SCREAMING_SNAKE_CASE :      str       =   outputs.loss
																		SCREAMING_SNAKE_CASE :      Tuple       =   loss / gradient_accumulation_steps
																		accelerator.backward(lowercase_			)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												SCREAMING_SNAKE_CASE :      Dict       =   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():
																								SCREAMING_SNAKE_CASE :      List[str]       =   model(**lowercase_			)
																		SCREAMING_SNAKE_CASE :      List[Any]       =   outputs.logits.argmax(dim=-1			)
																		SCREAMING_SNAKE_CASE :      Any       =   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
																														SCREAMING_SNAKE_CASE :      Dict       =   predictions[: len(eval_dataloader.dataset			) - samples_seen]
																														SCREAMING_SNAKE_CASE :      List[str]       =   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_						,    )

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






def 					__lowercase      (     )     ->						int:
						SCREAMING_SNAKE_CASE :      List[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."""			)
						SCREAMING_SNAKE_CASE :      Optional[Any]       =   parser.parse_args()
						SCREAMING_SNAKE_CASE :      Any       =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(lowercase_						,    lowercase_			)


if __name__ == "__main__":
		main()







def 					__lowercase      (     _A						,    _A			)     ->						int:
      assert x is not None
      assert y is not None

      SCREAMING_SNAKE_CASE :      int       =   len(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   len(_A			)

      # declaring the array for storing the dp values
      SCREAMING_SNAKE_CASE :      Tuple       =   [[0] * (n + 1) for _ in range(m + 1			)]  # noqa: E741

      for i in range(1						,    m + 1			):
            for j in range(1						,    n + 1			):
                  SCREAMING_SNAKE_CASE :      List[Any]       =   1 if x[i - 1] == y[j - 1] else 0

                  SCREAMING_SNAKE_CASE :      int       =   max(l[i - 1][j]						,    l[i][j - 1]						,    l[i - 1][j - 1] + match			)

      SCREAMING_SNAKE_CASE :      int       =   """"""
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      int       =   m, n
      while i > 0 and j > 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   1 if x[i - 1] == y[j - 1] else 0

            if l[i][j] == l[i - 1][j - 1] + match:
                  if match == 1:
                        SCREAMING_SNAKE_CASE :      List[Any]       =   x[i - 1] + seq
                  i -= 1
                  j -= 1
            elif l[i][j] == l[i - 1][j]:
                  i -= 1
            else:
                  j -= 1

      return l[m][n], seq


if __name__ == "__main__":
  UpperCAmelCase__ :     str				    =    """AGGTAB"""
  UpperCAmelCase__ :     Optional[int]				    =    """GXTXAYB"""
  UpperCAmelCase__ :     Tuple				    =    4
  UpperCAmelCase__ :     Dict				    =    """GTAB"""

  UpperCAmelCase__    ,   UpperCAmelCase__ :     Optional[int]				    =    longest_common_subsequence(a, b)
  print("""len =""", ln, """, sub-sequence =""", subseq)
  import doctest

  doctest.testmod()




