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


SCREAMING_SNAKE_CASE				:Tuple									=							16
SCREAMING_SNAKE_CASE				:List[str]									=							32


def 		UpperCAmelCase     (      a_						,  a_ = 1_6		)						->      Union[str, Any]:







				"""simple docstring"""



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

				def tokenize_function(a_		):
								# 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_		):
								# 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

					SCREAMING_SNAKE_CASE				:str									=							mocked_dataloaders  # noqa: F811


def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[Any]:







				"""simple docstring"""



				if os.environ.get("TESTING_MOCKED_DATALOADERS"						,  a_		) == "1":
								__A       =					2
				# Initialize accelerator
				__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"]		)

				__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

				set_seed(a_		)
				__A     ,				__A       =					get_dataloaders(a_						,  a_		)
				# 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_		)

				# Now we train the model
				for epoch in range(a_		):
								model.train()
								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
												__A       =					loss / gradient_accumulation_steps
												accelerator.backward(a_		)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__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		)
												with torch.no_grad():
																__A       =					model(**a_		)
												__A       =					outputs.logits.argmax(dim=-1		)
												__A     ,				__A       =					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(a_		) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__A       =					predictions[: len(eval_dataloader.dataset		) - samples_seen]
																				__A       =					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=a_						,  references=a_						,  )

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


def 		UpperCAmelCase     (      )						->      Tuple:







				"""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."		)
				__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()



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






