
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


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			Union[str, Any]    =	32







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"    )	->						str:
				UpperCamelCase_:				List[Any]						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				Any						=    load_dataset("""glue"""  ,     """mrpc"""    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Any						=    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
				UpperCamelCase_:				Tuple						=    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
				UpperCamelCase_:				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.
				UpperCamelCase_:				Union[str, Any]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Any						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				model.eval()
				UpperCamelCase_:				Union[str, 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():
												UpperCamelCase_:				Optional[int]						=    model(**lowerCamelCase    )
								UpperCamelCase_:				Dict						=    outputs.logits.argmax(dim=-1    )
								# It is slightly faster to call this once, than multiple times
								UpperCamelCase_:				Union[str, 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:
																UpperCamelCase_:				Any						=    predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																UpperCamelCase_:				Any						=    references[: len(eval_dataloader.dataset    ) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=lowerCamelCase  ,     references=lowerCamelCase  ,     )

				UpperCamelCase_:				Dict						=    metric.compute()
				return eval_metric["accuracy"]







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				int						=    Accelerator()

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

				set_seed(lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

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

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

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Optional[Any]						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								UpperCamelCase_:				str						=    1
				UpperCamelCase_:				Optional[int]						=    (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
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				str						=    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.
				UpperCamelCase_:				Tuple						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

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

				if args.partial_train_epoch is not None:
								UpperCamelCase_:				Dict						=    args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint    )
								UpperCamelCase_:				Dict						=    args.resume_from_checkpoint.split("""epoch_"""    )[1]
								UpperCamelCase_:				Union[str, Any]						=    ""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								UpperCamelCase_:				Any						=    int(lowerCamelCase    ) + 1
								UpperCamelCase_:				str						=    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:
												UpperCamelCase_:				Union[str, Any]						=    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
				UpperCamelCase_:				List[str]						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								model.train()
								for step, batch in enumerate(lowerCamelCase    ):
												UpperCamelCase_:				Optional[int]						=    model(**lowerCamelCase    )
												UpperCamelCase_:				List[Any]						=    outputs.loss
												UpperCamelCase_:				Dict						=    loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								UpperCamelCase_:				int						=    F'''epoch_{epoch}'''
								UpperCamelCase_:				int						=    os.path.join(args.output_dir  ,     lowerCamelCase    )
								accelerator.save_state(lowerCamelCase    )
								UpperCamelCase_:				Tuple						=    evaluation_loop(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
								UpperCamelCase_:				Optional[Any]						=    accuracy
								UpperCamelCase_:				Dict						=    lr_scheduler.get_lr()[0]
								UpperCamelCase_:				List[Any]						=    optimizer.param_groups[0]["lr"]
								UpperCamelCase_:				int						=    epoch
								UpperCamelCase_:				int						=    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__       (    )	->						int:
				UpperCamelCase_:				int						=    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."""  ,     )
				UpperCamelCase_:				List[Any]						=    parser.parse_args()
				UpperCamelCase_:				Tuple						=    {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor



@require_torch
@require_vision
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				SCREAMING_SNAKE_CASE_				:int				     =						AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				model.to(UpperCAmelCase)

				from datasets import load_dataset

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						load_dataset("nielsr/rvlcdip-demo")

				SCREAMING_SNAKE_CASE_				:Dict				     =						dataset["train"][0]["image"].convert("RGB")

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_processor(UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.logits

				SCREAMING_SNAKE_CASE_				:int				     =						torch.Size((1, 16))
				self.assertEqual(logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.tensor(
				    [-0.4158, -0.4092, -0.4347]   ,  device=UpperCAmelCase   ,  dtype=torch.float   ,  )
				self.assertTrue(torch.allclose(logits[0, :3]   ,  UpperCAmelCase   ,  atol=1E-4))






