




"""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
#
# 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
#
########################################################################


UpperCamelCase__			:     Dict			       =				16
UpperCamelCase__			:     Any			       =				32




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		Tuple		):







					if args.with_tracking:
										__snake_case        =						Accelerator(
										    cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       log_with="""all"""					,       project_dir=args.project_dir		)
					else:
										__snake_case        =						Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision		)

					if hasattr(args.checkpointing_steps					,       """isdigit"""		):
										if args.checkpointing_steps == "epoch":
															__snake_case        =						args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__snake_case        =						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        =						None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__snake_case        =						config["""lr"""]
					__snake_case        =						int(config["""num_epochs"""]		)
					__snake_case        =						int(config["""seed"""]		)
					__snake_case        =						int(config["""batch_size"""]		)

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

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

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

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

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

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

					# Instantiate dataloaders.
					__snake_case        =						DataLoader(
					    tokenized_datasets["""train"""]					,       shuffle=__UpperCamelCase					,       collate_fn=__UpperCamelCase					,       batch_size=__UpperCamelCase		)
					__snake_case        =						DataLoader(
					    tokenized_datasets["""validation"""]					,       shuffle=__UpperCamelCase					,       collate_fn=__UpperCamelCase					,       batch_size=__UpperCamelCase		)

					set_seed(__UpperCamelCase		)

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

					# 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        =						model.to(accelerator.device		)

					# Instantiate optimizer
					__snake_case        =						AdamW(params=model.parameters()					,       lr=__UpperCamelCase		)

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

					# We need to keep track of how many total steps we have iterated over
					__snake_case        =						0
					# We also need to keep track of the stating epoch so files are named properly
					__snake_case        =						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		)
															__snake_case        =						os.path.basename(args.resume_from_checkpoint		)
										else:
															# Get the most recent checkpoint
															__snake_case        =						[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
															dirs.sort(key=os.path.getctime		)
															__snake_case        =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__snake_case        =						os.path.splitext(__UpperCamelCase		)[0]

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

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

															overall_step += 1

															if isinstance(__UpperCamelCase					,       __UpperCamelCase		):
																				__snake_case        =						F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__snake_case        =						os.path.join(args.output_dir					,       __UpperCamelCase		)
																									accelerator.save_state(__UpperCamelCase		)

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

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

										if checkpointing_steps == "epoch":
															__snake_case        =						F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				__snake_case        =						os.path.join(args.output_dir					,       __UpperCamelCase		)
															accelerator.save_state(__UpperCamelCase		)

					if args.with_tracking:
										accelerator.end_training()




def    snake_case__						(							):
					__snake_case        =						argparse.ArgumentParser(description="""Simple example of training script."""		)
					parser.add_argument(
					    """--mixed_precision"""					,       type=__UpperCamelCase					,       default=__UpperCamelCase					,       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=__UpperCamelCase					,       default=__UpperCamelCase					,       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=__UpperCamelCase					,       default=__UpperCamelCase					,       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=__UpperCamelCase					,       default="""."""					,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					,       )
					parser.add_argument(
					    """--project_dir"""					,       type=__UpperCamelCase					,       default="""logs"""					,       help="""Location on where to store experiment tracking logs` and relevent project information"""					,       )
					__snake_case        =						parser.parse_args()
					__snake_case        =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(__UpperCamelCase					,       __UpperCamelCase		)


if __name__ == "__main__":
		main()










"""simple docstring"""


from typing import List, Union

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					max_encoder_position_embeddings
     __A		     =					max_decoder_position_embeddings
     __A		     =					d_model
     __A		     =					encoder_ffn_dim
     __A		     =					encoder_layers
     __A		     =					encoder_attention_heads
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_layers
     __A		     =					decoder_attention_heads
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					encoder_layerdrop
     __A		     =					decoder_layerdrop
     __A		     =					classifier_dropout
     __A		     =					use_cache
     __A		     =					encoder_layers
     __A		     =					attention_window

     super().__init__(
         pad_token_id=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			is_encoder_decoder=_lowerCamelCase,			decoder_start_token_id=_lowerCamelCase,			**_lowerCamelCase,			)




