
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


A_						       =				16
A_						       =				32






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			# Initialize accelerator
			if args.with_tracking:
						lowerCamelCase_ 		=     Accelerator(
						    cpu=args.cpu  ,mixed_precision=args.mixed_precision  ,log_with='''all'''  ,project_dir=args.project_dir	)
			else:
						lowerCamelCase_ 		=     Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision	)

			if hasattr(args.checkpointing_steps  ,'''isdigit'''	):
						if args.checkpointing_steps == "epoch":
									lowerCamelCase_ 		=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase_ 		=     int(args.checkpointing_steps	)
						else:
									raise ValueError(
									    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
			else:
						lowerCamelCase_ 		=     None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase_ 		=     config['''lr''']
			lowerCamelCase_ 		=     int(config['''num_epochs''']	)
			lowerCamelCase_ 		=     int(config['''seed''']	)
			lowerCamelCase_ 		=     int(config['''batch_size''']	)

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						lowerCamelCase_ 		=     os.path.split(lowerCAmelCase__	)[-1].split('''.'''	)[0]
						accelerator.init_trackers(lowerCAmelCase__  ,lowerCAmelCase__	)

			lowerCamelCase_ 		=     AutoTokenizer.from_pretrained('''bert-base-cased'''	)
			lowerCamelCase_ 		=     load_dataset('''glue'''  ,'''mrpc'''	)
			lowerCamelCase_ 		=     evaluate.load('''glue'''  ,'''mrpc'''	)

			def tokenize_function(lowerCAmelCase__	):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase_ 		=     tokenizer(examples['''sentence1''']  ,examples['''sentence2''']  ,truncation=lowerCAmelCase__  ,max_length=lowerCAmelCase__	)
						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():
						lowerCamelCase_ 		=     datasets.map(
						    lowerCAmelCase__  ,batched=lowerCAmelCase__  ,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
			lowerCamelCase_ 		=     tokenized_datasets.rename_column('''label'''  ,'''labels'''	)

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

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

						return tokenizer.pad(
						    lowerCAmelCase__  ,padding='''longest'''  ,max_length=lowerCAmelCase__  ,pad_to_multiple_of=lowerCAmelCase__  ,return_tensors='''pt'''  ,)

			# Instantiate dataloaders.
			lowerCamelCase_ 		=     DataLoader(
			    tokenized_datasets['''train''']  ,shuffle=lowerCAmelCase__  ,collate_fn=lowerCAmelCase__  ,batch_size=lowerCAmelCase__	)
			lowerCamelCase_ 		=     DataLoader(
			    tokenized_datasets['''validation''']  ,shuffle=lowerCAmelCase__  ,collate_fn=lowerCAmelCase__  ,batch_size=lowerCAmelCase__	)

			set_seed(lowerCAmelCase__	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase_ 		=     AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,return_dict=lowerCAmelCase__	)

			# 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).
			lowerCamelCase_ 		=     model.to(accelerator.device	)

			# Instantiate optimizer
			lowerCamelCase_ 		=     AdamW(params=model.parameters()  ,lr=lowerCAmelCase__	)

			# Instantiate scheduler
			lowerCamelCase_ 		=     get_linear_schedule_with_warmup(
			    optimizer=lowerCAmelCase__  ,num_warmup_steps=100  ,num_training_steps=(len(lowerCAmelCase__	) * 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.
			lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.prepare(
			    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	)

			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase_ 		=     0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase_ 		=     0

			# Potentially load in the weights and states from a previous save
			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	)
									lowerCamelCase_ 		=     os.path.basename(args.resume_from_checkpoint	)
						else:
									# Get the most recent checkpoint
									lowerCamelCase_ 		=     [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
									dirs.sort(key=os.path.getctime	)
									lowerCamelCase_ 		=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCamelCase_ 		=     os.path.splitext(lowerCAmelCase__	)[0]

						if "epoch" in training_difference:
									lowerCamelCase_ 		=     int(training_difference.replace('''epoch_'''  ,''''''	)	) + 1
									lowerCamelCase_ 		=     None
						else:
									lowerCamelCase_ 		=     int(training_difference.replace('''step_'''  ,''''''	)	)
									lowerCamelCase_ 		=     resume_step // len(lowerCAmelCase__	)
									resume_step -= starting_epoch * len(lowerCAmelCase__	)

    # Now we train the model
			for epoch in range(lowerCAmelCase__  ,lowerCAmelCase__	):
						model.train()
						if args.with_tracking:
									lowerCamelCase_ 		=     0
						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
									lowerCamelCase_ 		=     accelerator.skip_first_batches(lowerCAmelCase__  ,lowerCAmelCase__	)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCamelCase_ 		=     train_dataloader
						for step, batch in enumerate(lowerCAmelCase__	):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device	)
									lowerCamelCase_ 		=     model(**lowerCAmelCase__	)
									lowerCamelCase_ 		=     outputs.loss
									lowerCamelCase_ 		=     loss / gradient_accumulation_steps
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(lowerCAmelCase__	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(lowerCAmelCase__  ,lowerCAmelCase__	):
												lowerCamelCase_ 		=     f"step_{overall_step}"
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase_ 		=     os.path.join(args.output_dir  ,lowerCAmelCase__	)
															accelerator.save_state(lowerCAmelCase__	)

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

						lowerCamelCase_ 		=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"  ,lowerCAmelCase__	)
						if args.with_tracking:
									accelerator.log(
									    {
									        '''accuracy''': eval_metric['''accuracy'''],
									        '''f1''': eval_metric['''f1'''],
									        '''train_loss''': total_loss.item() / len(lowerCAmelCase__	),
									        '''epoch''': epoch,
									    }  ,step=lowerCAmelCase__  ,)

						if checkpointing_steps == "epoch":
									lowerCamelCase_ 		=     f"epoch_{epoch}"
									if args.output_dir is not None:
												lowerCamelCase_ 		=     os.path.join(args.output_dir  ,lowerCAmelCase__	)
									accelerator.save_state(lowerCAmelCase__	)

			if args.with_tracking:
						accelerator.end_training()






def    lowercase	(  ):
			lowerCamelCase_ 		=     argparse.ArgumentParser(description='''Simple example of training script.'''	)
			parser.add_argument(
			    '''--mixed_precision'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,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=lowerCAmelCase__  ,default=lowerCAmelCase__  ,help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,)
			parser.add_argument(
			    '''--resume_from_checkpoint'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,help='''If the training should continue from a checkpoint folder.'''  ,)
			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(
			    '''--output_dir'''  ,type=lowerCAmelCase__  ,default='''.'''  ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,)
			parser.add_argument(
			    '''--project_dir'''  ,type=lowerCAmelCase__  ,default='''logs'''  ,help='''Location on where to store experiment tracking logs` and relevent project information'''  ,)
			lowerCamelCase_ 		=     parser.parse_args()
			lowerCamelCase_ 		=     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(lowerCAmelCase__  ,lowerCAmelCase__	)


if __name__ == "__main__":
						main()





"""simple docstring"""


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


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Dict									=						'yoso'




				def __init__(							self       ,	UpperCAmelCase=5_0265       ,	UpperCAmelCase=768       ,	UpperCAmelCase=12       ,	UpperCAmelCase=12       ,	UpperCAmelCase=3072       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=4096       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=1e-1_2       ,	UpperCAmelCase="absolute"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=9       ,	UpperCAmelCase=64       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0       ,	UpperCAmelCase=2       ,	**UpperCAmelCase       ,	):
							super().__init__(pad_token_id=UpperCAmelCase       ,	bos_token_id=UpperCAmelCase       ,	eos_token_id=UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     layer_norm_eps
							lowerCamelCase_ 		=     position_embedding_type
							lowerCamelCase_ 		=     use_expectation
							lowerCamelCase_ 		=     hash_code_len
							lowerCamelCase_ 		=     num_hash
							lowerCamelCase_ 		=     conv_window
							lowerCamelCase_ 		=     use_fast_hash
							lowerCamelCase_ 		=     lsh_backward



