


'''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_		:       Any							       =		16
UpperCAmelCase_		:       Tuple							       =		32







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

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

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

				_SCREAMING_SNAKE_CASE			:	Dict							=				AutoTokenizer.from_pretrained("""bert-base-cased"""					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				load_dataset("""glue"""							,				"""mrpc"""					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				evaluate.load("""glue"""							,				"""mrpc"""					)

				def tokenize_function(SCREAMING_SNAKE_CASE__					):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE			:	int							=				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():
								_SCREAMING_SNAKE_CASE			:	Dict							=				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
				_SCREAMING_SNAKE_CASE			:	int							=				tokenized_datasets.rename_column("""label"""							,				"""labels"""					)

				# 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			:	Optional[int]							=				batch_size // MAX_GPU_BATCH_SIZE
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				MAX_GPU_BATCH_SIZE

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

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				DataLoader(
				    tokenized_datasets["""train"""]							,				shuffle=_lowerCAmelCase							,				collate_fn=_lowerCAmelCase							,				batch_size=_lowerCAmelCase					)
				_SCREAMING_SNAKE_CASE			:	Dict							=				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)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				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).
				_SCREAMING_SNAKE_CASE			:	Tuple							=				model.to(accelerator.device					)

				# Instantiate optimizer
				_SCREAMING_SNAKE_CASE			:	str							=				AdamW(params=model.parameters()							,				lr=_lowerCAmelCase					)

				# Instantiate scheduler
				_SCREAMING_SNAKE_CASE			:	str							=				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.
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	List[str]							=				accelerator.prepare(
				    _lowerCAmelCase							,				_lowerCAmelCase							,				_lowerCAmelCase							,				_lowerCAmelCase							,				_lowerCAmelCase					)

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

								if "epoch" in training_difference:
												_SCREAMING_SNAKE_CASE			:	List[str]							=				int(training_difference.replace("""epoch_"""							,				""""""					)					) + 1
												_SCREAMING_SNAKE_CASE			:	Any							=				None
								else:
												_SCREAMING_SNAKE_CASE			:	str							=				int(training_difference.replace("""step_"""							,				""""""					)					)
												_SCREAMING_SNAKE_CASE			:	str							=				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:
												_SCREAMING_SNAKE_CASE			:	Any							=				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
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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
												_SCREAMING_SNAKE_CASE			:	Any							=				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					)
												_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				model(**_lowerCAmelCase					)
												_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				outputs.loss
												_SCREAMING_SNAKE_CASE			:	Dict							=				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					):
																_SCREAMING_SNAKE_CASE			:	str							=				f"""step_{overall_step}"""
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								_SCREAMING_SNAKE_CASE			:	Tuple							=				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():
																_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model(**_lowerCAmelCase					)
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				outputs.logits.argmax(dim=-1					)
												_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	List[str]							=				accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
												metric.add_batch(
												    predictions=_lowerCAmelCase							,				references=_lowerCAmelCase							,				)

								_SCREAMING_SNAKE_CASE			:	List[Any]							=				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":
												_SCREAMING_SNAKE_CASE			:	Dict							=				f"""epoch_{epoch}"""
												if args.output_dir is not None:
																_SCREAMING_SNAKE_CASE			:	Dict							=				os.path.join(args.output_dir							,				_lowerCAmelCase					)
												accelerator.save_state(_lowerCAmelCase					)

				if args.with_tracking:
								accelerator.end_training()







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	str							=				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"""							,				)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				parser.parse_args()
				_SCREAMING_SNAKE_CASE			:	str							=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_lowerCAmelCase							,				_lowerCAmelCase					)


if __name__ == "__main__":
						main()








'''simple docstring'''


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  EsmTokenizer






							def UpperCAmelCase_     (   self  ):
											super().setUp()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tempfile.mkdtemp()
											# fmt: off
											_SCREAMING_SNAKE_CASE			:	List[str]							=				["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
											# fmt: on
											_SCREAMING_SNAKE_CASE			:	Any							=				os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["""vocab_file"""]  )
											with open(self.vocab_file   , """w"""   , encoding="""utf-8"""  ) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return [self.get_tokenizer(**__snake_case  )]






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.tokenize("""LAGVS"""  )
											self.assertListEqual(__snake_case   , ["""L""", """A""", """G""", """V""", """S"""]  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )   , [4, 5, 6, 7, 8]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""LAGVS"""
											self.assertListEqual(tokenizer.encode(__snake_case  )   , [0, 4, 5, 6, 7, 8, 2]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	str							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizers(fast=__snake_case  )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<unk>"""
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<mask>"""

																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.tokenize(__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.tokenize(__snake_case  )

																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(token_a[0]   , __snake_case  )
																			self.assertEqual(token_a[0]   , __snake_case  )




