
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment






def 							UpperCAmelCase_		(							):
	lowercase       =    torch.nn.Linear(2 ,							4							)
	lowercase       =    torch.optim.AdamW(model.parameters() ,							lr=1.0							)
	lowercase       =    torch.optim.lr_scheduler.OneCycleLR(__SCREAMING_SNAKE_CASE ,							max_lr=0.01 ,							steps_per_epoch=2 ,							epochs=1							)
	lowercase       =    DataLoader(TensorDataset(torch.tensor([1, 2, 3]							)							)							)
	lowercase       =    DataLoader(TensorDataset(torch.tensor([4, 5, 6]							)							)							)

	return model, optimizer, scheduler, train_dl, valid_dl






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    torch.nn.Linear(*tuple(model.weight.T.shape							)							).state_dict()
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	@require_cuda
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		assert PartialState._shared_state["_cpu"] is False
		assert PartialState._shared_state["device"].type == "cuda"
		with self.assertRaises(snake_case       ):
			lowercase       =    Accelerator(cpu=snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase       =    GradientState()
		assert state.num_steps == 1
		lowercase       =    4
		assert state.num_steps == 4

		assert state.sync_gradients is True
		lowercase       =    False
		assert state.sync_gradients is False
		GradientState._reset_state()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()

		(
		    (
		    lowercase
		)  ,					(
		    lowercase
		)  ,					(
		    lowercase
		)  ,					(
		    lowercase
		)  ,					(
		    lowercase
		)  ,					
		)       =    accelerator.prepare(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )

		self.assertTrue(prepared_model in accelerator._models       )
		self.assertTrue(prepared_optimizer in accelerator._optimizers       )
		self.assertTrue(prepared_scheduler in accelerator._schedulers       )
		self.assertTrue(prepared_train_dl in accelerator._dataloaders       )
		self.assertTrue(prepared_valid_dl in accelerator._dataloaders       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()
		accelerator.prepare(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )
		accelerator.free_memory()

		self.assertTrue(len(accelerator._models       ) == 0       )
		self.assertTrue(len(accelerator._optimizers       ) == 0       )
		self.assertTrue(len(accelerator._schedulers       ) == 0       )
		self.assertTrue(len(accelerator._dataloaders       ) == 0       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		PartialState._reset_state()

		# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
		def noop(*snake_case		,    **snake_case       ):
			pass

		with patch('torch.cuda.set_device'		,    snake_case       ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'       ):
			lowercase       =    Accelerator()
			self.assertEqual(str(accelerator.state.device       )		,    'cuda:64'       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()
		accelerator.prepare(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )

		lowercase       =    get_signature(snake_case       )

		with tempfile.TemporaryDirectory() as tmpdirname:
			accelerator.save_state(snake_case       )

			# make sure random weights don't match
			load_random_weights(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) > 1E-3       )

			# make sure loaded weights match
			accelerator.load_state(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) < 1E-3       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()
		accelerator.prepare(snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )

		lowercase       =    get_signature(snake_case       )

		# saving hook
		def save_config(snake_case		,    snake_case		,    snake_case       ):
			lowercase       =    {'class_name': models[0].__class__.__name__}

			with open(os.path.join(snake_case		,    'data.json'       )		,    'w'       ) as f:
				json.dump(snake_case		,    snake_case       )

        # loading hook
		def load_config(snake_case		,    snake_case       ):
			with open(os.path.join(snake_case		,    'data.json'       )		,    'r'       ) as f:
				lowercase       =    json.load(snake_case       )

			lowercase       =    config['class_name']

		lowercase       =    accelerator.register_save_state_pre_hook(snake_case       )
		lowercase       =    accelerator.register_load_state_pre_hook(snake_case       )

		with tempfile.TemporaryDirectory() as tmpdirname:
			accelerator.save_state(snake_case       )

			# make sure random weights don't match with hooks
			load_random_weights(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) > 1E-3       )

			# random class name to verify correct one is loaded
			lowercase       =    'random'

			# make sure loaded weights match with hooks
			accelerator.load_state(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) < 1E-3       )

			# mode.class_name is loaded from config
			self.assertTrue(model.class_name == model.__class__.__name__       )

		# remove hooks
		save_hook.remove()
		load_hook.remove()

		with tempfile.TemporaryDirectory() as tmpdirname:
			accelerator.save_state(snake_case       )

			# make sure random weights don't match with hooks removed
			load_random_weights(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) > 1E-3       )

			# random class name to verify correct one is loaded
			lowercase       =    'random'

			# make sure loaded weights match with hooks removed
			accelerator.load_state(snake_case       )
			self.assertTrue(abs(model_signature - get_signature(snake_case       )       ) < 1E-3       )

			# mode.class_name is NOT loaded from config
			self.assertTrue(model.class_name != model.__class__.__name__       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()
		lowercase       =    None

		# This should work
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    accelerator.prepare(
		    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )
		self.assertTrue(dummy_obj is None       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Accelerator()
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    create_components()
		lowercase       =    [1, 2, 3]

		# This should work
		lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    accelerator.prepare(
		    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case       )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Dummy object should have `_is_accelerate_prepared` set to `True`'		,    )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Model is missing `_is_accelerator_prepared` or is set to `False`'		,    )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'		,    )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'		,    )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'		,    )
		self.assertEqual(
		    getattr(snake_case		,    '_is_accelerate_prepared'		,    snake_case       )		,    snake_case		,    'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'		,    )





	@slow
	@require_bnb
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import AutoModelForCausalLM

		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    'EleutherAI/gpt-neo-125m'		,    load_in_abit=snake_case		,    device_map={'': 0}		,    )
		lowercase       =    Accelerator()

		# This should work
		lowercase       =    accelerator.prepare(snake_case       )





	@slow
	@require_bnb
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import AutoModelForCausalLM

		lowercase       =    Accelerator()

		with init_empty_weights():
			lowercase       =    AutoModelForCausalLM.from_pretrained(
			    'EleutherAI/gpt-neo-125m'		,    )
			model.tie_weights()
			lowercase       =    infer_auto_device_map(snake_case       )
			lowercase       =    'cpu'

		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    'EleutherAI/gpt-neo-125m'		,    device_map=snake_case		,    load_in_abit=snake_case		,    llm_inta_enable_fpaa_cpu_offload=snake_case       )

		# This should not work and get value error
		with self.assertRaises(snake_case       ):
			lowercase       =    accelerator.prepare(snake_case       )





	@slow
	@require_bnb
	@require_multi_gpu
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import AutoModelForCausalLM

		lowercase       =    {'distributed_type': DistributedType.MULTI_GPU}

		with init_empty_weights():
			lowercase       =    AutoModelForCausalLM.from_pretrained(
			    'EleutherAI/gpt-neo-125m'		,    )
			model.tie_weights()
			lowercase       =    infer_auto_device_map(snake_case       )
			lowercase       =    1

		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    'EleutherAI/gpt-neo-125m'		,    load_in_abit=snake_case		,    device_map=snake_case		,    )
		lowercase       =    Accelerator()

		# This should not work and get value error
		with self.assertRaises(snake_case       ):
			lowercase       =    accelerator.prepare(snake_case       )

		PartialState._reset_state()





	@slow
	@require_bnb
	@require_multi_gpu
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		from transformers import AutoModelForCausalLM

		with init_empty_weights():
			lowercase       =    AutoModelForCausalLM.from_pretrained(
			    'EleutherAI/gpt-neo-125m'		,    )
			lowercase       =    infer_auto_device_map(snake_case       )
			lowercase       =    1

		lowercase       =    AutoModelForCausalLM.from_pretrained(
		    'EleutherAI/gpt-neo-125m'		,    load_in_abit=snake_case		,    device_map=snake_case		,    )
		lowercase       =    Accelerator()

		# This should work
		lowercase       =    accelerator.prepare(snake_case       )





	@require_cuda
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    torch.nn.Linear(10		,    10       )
		lowercase       =    torch.optim.SGD(model.parameters()		,    lr=0.01       )
		lowercase       =    Accelerator(cpu=snake_case       )
		lowercase       =    accelerator.prepare(snake_case       )



import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCAmelCase	      = logging.getLogger(__name__)







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[int]							 =       """sequence-classification"""





	def __init__(       self		,    snake_case       ):
		if type(snake_case       ) == dict:
			lowercase       =    Namespace(**snake_case       )
		lowercase       =    glue_output_modes[hparams.task]
		lowercase       =    glue_tasks_num_labels[hparams.task]

		super().__init__(snake_case		,    snake_case		,    self.mode       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return self.model(**snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase       =    outputs[0]

		lowercase       =    self.trainer.lr_schedulers[0]['scheduler']
		lowercase       =    {'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
		return {"loss": loss, "log": tensorboard_logs}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.hparams
		lowercase       =    processors[args.task]()
		lowercase       =    processor.get_labels()

		for mode in ["train", "dev"]:
			lowercase       =    self._feature_file(snake_case       )
			if os.path.exists(snake_case       ) and not args.overwrite_cache:
				logger.info('Loading features from cached file %s'		,    snake_case       )
			else:
				logger.info('Creating features from dataset file at %s'		,    args.data_dir       )
				lowercase       =    (
				    processor.get_dev_examples(args.data_dir       )
				    if mode == 'dev'
				    else processor.get_train_examples(args.data_dir       )
				)
				lowercase       =    convert_examples_to_features(
				    snake_case		,    self.tokenizer		,    max_length=args.max_seq_length		,    label_list=self.labels		,    output_mode=args.glue_output_mode		,    )
				logger.info('Saving features into cached file %s'		,    snake_case       )
				torch.save(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = False       ):
		lowercase       =    'dev' if mode == 'test' else mode

		lowercase       =    self._feature_file(snake_case       )
		logger.info('Loading features from cached file %s'		,    snake_case       )
		lowercase       =    torch.load(snake_case       )
		lowercase       =    torch.tensor([f.input_ids for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long       )
		lowercase       =    torch.tensor([f.token_type_ids for f in features]		,    dtype=torch.long       )
		if self.hparams.glue_output_mode == "classification":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.long       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    torch.tensor([f.label for f in features]		,    dtype=torch.float       )

		return DataLoader(
		    TensorDataset(snake_case		,    snake_case		,    snake_case		,    snake_case       )		,    batch_size=snake_case		,    shuffle=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

		if self.config.model_type not in ["distilbert", "bart"]:
			lowercase       =    batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

		lowercase       =    self(**snake_case       )
		lowercase  ,					lowercase       =    outputs[:2]
		lowercase       =    logits.detach().cpu().numpy()
		lowercase       =    inputs['labels'].detach().cpu().numpy()

		return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    torch.stack([x['val_loss'] for x in outputs]       ).mean().detach().cpu().item()
		lowercase       =    np.concatenate([x['pred'] for x in outputs]		,    axis=0       )

		if self.hparams.glue_output_mode == "classification":
			lowercase       =    np.argmax(snake_case		,    axis=1       )
		elif self.hparams.glue_output_mode == "regression":
			lowercase       =    np.squeeze(snake_case       )

		lowercase       =    np.concatenate([x['target'] for x in outputs]		,    axis=0       )
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]
		lowercase       =    [[] for _ in range(out_label_ids.shape[0]       )]

		lowercase       =    {**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,    snake_case		,    snake_case       )}

		lowercase       =    dict(results.items()       )
		lowercase       =    results
		return ret, preds_list, out_label_list





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase  ,					lowercase  ,					lowercase       =    self._eval_end(snake_case       )
		lowercase       =    ret['log']
		# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
		return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def 	SCREAMING_SNAKE_CASE__						(       snake_case		,    snake_case       ):
		BaseTransformer.add_model_specific_args(snake_case		,    snake_case       )
		parser.add_argument(
		    '--max_seq_length'		,    default=128		,    type=snake_case		,    help=(
		        'The maximum total input sequence length after tokenization. Sequences longer '
		        'than this will be truncated, sequences shorter will be padded.'
		    )		,    )

		parser.add_argument(
		    '--task'		,    default=''		,    type=snake_case		,    required=snake_case		,    help='The GLUE task to run'		,    )
		parser.add_argument(
		    '--gpus'		,    default=0		,    type=snake_case		,    help='The number of GPUs allocated for this, it is by default 0 meaning none'		,    )

		parser.add_argument(
		    '--overwrite_cache'		,    action='store_true'		,    help='Overwrite the cached training and evaluation sets'       )

		return parser






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser()
	add_generic_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    GLUETransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE ,							os.getcwd()							)
	lowercase       =    parser.parse_args()

	# If output_dir not provided, a folder will be generated in pwd
	if args.output_dir is None:
		lowercase       =    os.path.join(
		    './results' ,							F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'							)}''' ,							)
		os.makedirs(args.output_dir							)

	lowercase       =    GLUETransformer(__SCREAMING_SNAKE_CASE							)
	lowercase       =    generic_train(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	# Optionally, predict on dev set and write to output_dir
	if args.do_predict:
		lowercase       =    sorted(glob.glob(os.path.join(args.output_dir ,							'checkpoint-epoch=*.ckpt'							) ,							recursive=__SCREAMING_SNAKE_CASE							)							)
		lowercase       =    model.load_from_checkpoint(checkpoints[-1]							)
		return trainer.test(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	main()

