

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_	:			Tuple  	=      {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Dict  	=      [
	    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SEWForCTC',
	    'SEWForSequenceClassification',
	    'SEWModel',
	    'SEWPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

	A_	:			str  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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


A_	:			Optional[Any]  	=      16
A_	:			Optional[int]  	=      32

def 						UpperCamelCase (lowercase_:			Accelerator   ,				lowercase_:			int = 16   ,				lowercase_:			str = "bert-base-cased" )					->				List[str]:
	A__							:   int					       =		AutoTokenizer.from_pretrained(lowercase_ )
	A__							:   Union[str, Any]					       =		load_dataset("""glue"""   ,				"""mrpc""" )

	def tokenize_function(lowercase_:			Tuple ):
		# max_length=None => use the model max length (it's actually the default)
		A__							:   Optional[Any]					       =		tokenizer(examples["""sentence1"""]   ,				examples["""sentence2"""]   ,				truncation=lowercase_   ,				max_length=lowercase_ )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	A__							:   int					       =		datasets.map(
	    lowercase_   ,				batched=lowercase_   ,				remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,				load_from_cache_file=lowercase_ )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A__							:   int					       =		tokenized_datasets.rename_column("""label"""   ,				"""labels""" )

	def collate_fn(lowercase_:			Optional[int] ):
		# 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(lowercase_   ,				padding="""max_length"""   ,				max_length=128   ,				return_tensors="""pt""" )
		return tokenizer.pad(lowercase_   ,				padding="""longest"""   ,				return_tensors="""pt""" )

	# Instantiate dataloaders.
	A__							:   int					       =		DataLoader(
	    tokenized_datasets["""train"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )
	A__							:   Optional[Any]					       =		DataLoader(
	    tokenized_datasets["""validation"""]   ,				shuffle=lowercase_   ,				collate_fn=lowercase_   ,				batch_size=lowercase_ )

	return train_dataloader, eval_dataloader

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			Dict   ,				lowercase_:			Tuple   ,				lowercase_:			Optional[int] )					->				int:
	model.eval()
	A__							:   str					       =		0
	for step, batch in enumerate(lowercase_ ):
		# We could avoid this line since we set the accelerator with `device_placement=True`.
		batch.to(accelerator.device )
		with torch.no_grad():
			A__							:   Any					       =		model(**lowercase_ )
		A__							:   List[Any]					       =		outputs.logits.argmax(dim=-1 )
		# It is slightly faster to call this once, than multiple times
		A__       ,       A__							:   str					       =		accelerator.gather(
		    (predictions, batch["""labels"""]) )  # If we are in a multiprocess environment, the last batch has duplicates
		if accelerator.use_distributed:
			if step == len(lowercase_ ) - 1:
				A__							:   List[str]					       =		predictions[: len(eval_dataloader.dataset ) - samples_seen]
				A__							:   Dict					       =		references[: len(eval_dataloader.dataset ) - samples_seen]
			else:
				samples_seen += references.shape[0]
		metric.add_batch(
		    predictions=lowercase_   ,				references=lowercase_   ,				)

	A__							:   int					       =		metric.compute()
	return eval_metric["accuracy"]

def 						UpperCamelCase (lowercase_:			List[Any]   ,				lowercase_:			str )					->				List[str]:
	# Initialize accelerator
	A__							:   str					       =		Accelerator()

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

	set_seed(lowercase_ )
	A__       ,       A__							:   Optional[Any]					       =		get_dataloaders(lowercase_   ,				lowercase_   ,				lowercase_ )

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

	# Instantiate optimizer
	A__							:   Dict					       =		(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A__							:   Union[str, Any]					       =		optimizer_cls(params=model.parameters()   ,				lr=lowercase_ )

	if accelerator.state.deepspeed_plugin is not None:
		A__							:   List[Any]					       =		accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		A__							:   Optional[int]					       =		1
	A__							:   Optional[int]					       =		(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		A__							:   Optional[int]					       =		get_linear_schedule_with_warmup(
		    optimizer=lowercase_   ,				num_warmup_steps=0   ,				num_training_steps=lowercase_   ,				)
	else:
		A__							:   int					       =		DummyScheduler(lowercase_   ,				total_num_steps=lowercase_   ,				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.
	A__       ,       A__       ,       A__       ,       A__       ,       A__							:   str					       =		accelerator.prepare(
	    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )

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

	if args.partial_train_epoch is not None:
		A__							:   Tuple					       =		args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint )
		A__							:   Dict					       =		args.resume_from_checkpoint.split("""epoch_""" )[1]
		A__							:   int					       =		""""""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		A__							:   Any					       =		int(lowercase_ ) + 1
		A__							:   Any					       =		evaluation_loop(lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_ )
		accelerator.print("""resumed checkpoint performance:"""   ,				lowercase_ )
		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:
			A__							:   int					       =		json.load(lowercase_ )
			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
	A__							:   Optional[Any]					       =		{}
	for epoch in range(lowercase_   ,				lowercase_ ):
		model.train()
		for step, batch in enumerate(lowercase_ ):
			A__							:   int					       =		model(**lowercase_ )
			A__							:   int					       =		outputs.loss
			A__							:   int					       =		loss / gradient_accumulation_steps
			accelerator.backward(lowercase_ )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		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(lowercase_   ,				lowercase_ )

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

	training_function(lowercase_   ,				lowercase_ )


if __name__ == "__main__":
	main()



