





'''simple docstring'''




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCAmelCase            =       16
__UpperCAmelCase            =       32



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_ = 16			,			lowerCamelCase_ = "bert-base-cased"	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[str]		=			AutoTokenizer.from_pretrained(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			load_dataset("""glue"""			,			"""mrpc"""	)

		def tokenize_function(lowerCamelCase_	):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE :				Optional[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
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			datasets.map(
		    lowerCamelCase_			,			batched=lowerCamelCase_			,			remove_columns=["""idx""", """sentence1""", """sentence2"""]			,			load_from_cache_file=lowerCamelCase_	)

		# 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 :				Dict		=			tokenized_datasets.rename_column("""label"""			,			"""labels"""	)

		def collate_fn(lowerCamelCase_	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(lowerCamelCase_			,			padding="""max_length"""			,			max_length=1_28			,			return_tensors="""pt"""	)
				return tokenizer.pad(lowerCamelCase_			,			padding="""longest"""			,			return_tensors="""pt"""	)

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

		return train_dataloader, eval_dataloader



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		model.eval()
		SCREAMING_SNAKE_CASE :				Any		=			0
		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 :				Any		=			model(**lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				str		=			outputs.logits.argmax(dim=-1	)
				# It is slightly faster to call this once, than multiple times
				SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[Any]		=			accelerator.gather(
				    (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(lowerCamelCase_	) - 1:
								SCREAMING_SNAKE_CASE :				Any		=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
								SCREAMING_SNAKE_CASE :				int		=			references[: len(eval_dataloader.dataset	) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=lowerCamelCase_			,			references=lowerCamelCase_			,			)

		SCREAMING_SNAKE_CASE :				Optional[Any]		=			metric.compute()
		return eval_metric["accuracy"]



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE :				List[str]		=			config["""lr"""]
		SCREAMING_SNAKE_CASE :				Tuple		=			int(config["""num_epochs"""]	)
		SCREAMING_SNAKE_CASE :				List[str]		=			int(config["""seed"""]	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			int(config["""batch_size"""]	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			args.model_name_or_path

		set_seed(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[Any]		=			get_dataloaders(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE :				Tuple		=			AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_			,			return_dict=lowerCamelCase_	)

		# Instantiate optimizer
		SCREAMING_SNAKE_CASE :				Dict		=			(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		SCREAMING_SNAKE_CASE :				Any		=			optimizer_cls(params=model.parameters()			,			lr=lowerCamelCase_	)

		if accelerator.state.deepspeed_plugin is not None:
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				SCREAMING_SNAKE_CASE :				List[Any]		=			1
		SCREAMING_SNAKE_CASE :				str		=			(len(lowerCamelCase_	) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_			,			num_warmup_steps=0			,			num_training_steps=lowerCamelCase_			,			)
		else:
				SCREAMING_SNAKE_CASE :				Any		=			DummyScheduler(lowerCamelCase_			,			total_num_steps=lowerCamelCase_			,			warmup_num_steps=0	)

		# 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 :				Optional[Any]		=			accelerator.prepare(
		    lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)

		# We need to keep track of how many total steps we have iterated over
		SCREAMING_SNAKE_CASE :				int		=			0
		# We also need to keep track of the stating epoch so files are named properly
		SCREAMING_SNAKE_CASE :				Tuple		=			0
		SCREAMING_SNAKE_CASE :				int		=			evaluate.load("""glue"""			,			"""mrpc"""	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			num_epochs

		if args.partial_train_epoch is not None:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint	)
				SCREAMING_SNAKE_CASE :				str		=			args.resume_from_checkpoint.split("""epoch_"""	)[1]
				SCREAMING_SNAKE_CASE :				Optional[int]		=			""""""
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			int(lowerCamelCase_	) + 1
				SCREAMING_SNAKE_CASE :				Dict		=			evaluation_loop(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				accelerator.print("""resumed checkpoint performance:"""			,			lowerCamelCase_	)
				accelerator.print("""resumed checkpoint's scheduler's lr:"""			,			lr_scheduler.get_lr()[0]	)
				accelerator.print("""resumed optimizers's lr:"""			,			optimizer.param_groups[0]["""lr"""]	)
				with open(os.path.join(args.output_dir			,			f'''state_{starting_epoch-1}.json'''	)			,			"""r"""	) as f:
						SCREAMING_SNAKE_CASE :				Tuple		=			json.load(lowerCamelCase_	)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{}
		for epoch in range(lowerCamelCase_			,			lowerCamelCase_	):
				model.train()
				for step, batch in enumerate(lowerCamelCase_	):
						SCREAMING_SNAKE_CASE :				List[Any]		=			model(**lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			outputs.loss
						SCREAMING_SNAKE_CASE :				Any		=			loss / gradient_accumulation_steps
						accelerator.backward(lowerCamelCase_	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				SCREAMING_SNAKE_CASE :				Optional[int]		=			f'''epoch_{epoch}'''
				SCREAMING_SNAKE_CASE :				List[str]		=			os.path.join(args.output_dir			,			lowerCamelCase_	)
				accelerator.save_state(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Dict		=			evaluation_loop(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			accuracy
				SCREAMING_SNAKE_CASE :				List[str]		=			lr_scheduler.get_lr()[0]
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			optimizer.param_groups[0]["""lr"""]
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			epoch
				SCREAMING_SNAKE_CASE :				List[str]		=			overall_step
				accelerator.print(f'''epoch {epoch}:'''			,			lowerCamelCase_	)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,			f'''state_{epoch}.json'''	)			,			"""w"""	) as f:
								json.dump(lowerCamelCase_			,			lowerCamelCase_	)



def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
		parser.add_argument(
		    """--model_name_or_path"""			,			type=lowerCamelCase_			,			default="""bert-base-cased"""			,			help="""Path to pretrained model or model identifier from huggingface.co/models."""			,			required=lowerCamelCase_			,			)
		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(
		    """--resume_from_checkpoint"""			,			type=lowerCamelCase_			,			default=lowerCamelCase_			,			help="""If the training should continue from a checkpoint folder."""			,			)
		parser.add_argument(
		    """--partial_train_epoch"""			,			type=lowerCamelCase_			,			default=lowerCamelCase_			,			help="""If passed, the training will stop after this number of epochs."""			,			)
		parser.add_argument(
		    """--num_epochs"""			,			type=lowerCamelCase_			,			default=2			,			help="""Number of train epochs."""			,			)
		SCREAMING_SNAKE_CASE :				List[Any]		=			parser.parse_args()
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

		training_function(lowerCamelCase_			,			lowerCamelCase_	)


if __name__ == "__main__":
							main()










'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



