




"""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


lowerCAmelCase							:    Dict  							=							16
lowerCAmelCase							:    int  							=							32



def 					a__				(       snake_case__ ,				snake_case__ = 16 ,				snake_case__ = "bert-base-cased"       )		->      int:
			lowerCamelCase       =      AutoTokenizer.from_pretrained(snake_case__       )
			lowerCamelCase       =      load_dataset("""glue""" ,				"""mrpc"""       )

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

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

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

			def collate_fn(snake_case__       ):
						# 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(snake_case__ ,				padding="""max_length""" ,				max_length=1_28 ,				return_tensors="""pt"""       )
						return tokenizer.pad(snake_case__ ,				padding="""longest""" ,				return_tensors="""pt"""       )

			# Instantiate dataloaders.
			lowerCamelCase       =      DataLoader(
			    tokenized_datasets["""train"""] ,				shuffle=snake_case__ ,				collate_fn=snake_case__ ,				batch_size=snake_case__       )
			lowerCamelCase       =      DataLoader(
			    tokenized_datasets["""validation"""] ,				shuffle=snake_case__ ,				collate_fn=snake_case__ ,				batch_size=snake_case__       )

			return train_dataloader, eval_dataloader



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

			lowerCamelCase       =      metric.compute()
			return eval_metric["accuracy"]



def 					a__				(       snake_case__ ,				snake_case__       )		->      List[str]:
			# Initialize accelerator
			lowerCamelCase       =      Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase       =      config["""lr"""]
			lowerCamelCase       =      int(config["""num_epochs"""]       )
			lowerCamelCase       =      int(config["""seed"""]       )
			lowerCamelCase       =      int(config["""batch_size"""]       )
			lowerCamelCase       =      args.model_name_or_path

			set_seed(snake_case__       )
			lowerCamelCase   ,     lowerCamelCase       =      get_dataloaders(snake_case__ ,				snake_case__ ,				snake_case__       )

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

			# Instantiate optimizer
			lowerCamelCase       =      (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowerCamelCase       =      optimizer_cls(params=model.parameters() ,				lr=snake_case__       )

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase       =      accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						lowerCamelCase       =      1
			lowerCamelCase       =      (len(snake_case__       ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						lowerCamelCase       =      get_linear_schedule_with_warmup(
						    optimizer=snake_case__ ,				num_warmup_steps=0 ,				num_training_steps=snake_case__ ,				)
			else:
						lowerCamelCase       =      DummyScheduler(snake_case__ ,				total_num_steps=snake_case__ ,				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.
			lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      accelerator.prepare(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )

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

			if args.partial_train_epoch is not None:
						lowerCamelCase       =      args.partial_train_epoch

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

									overall_step += 1
						lowerCamelCase       =      F'epoch_{epoch}'
						lowerCamelCase       =      os.path.join(args.output_dir ,				snake_case__       )
						accelerator.save_state(snake_case__       )
						lowerCamelCase       =      evaluation_loop(snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )
						lowerCamelCase       =      accuracy
						lowerCamelCase       =      lr_scheduler.get_lr()[0]
						lowerCamelCase       =      optimizer.param_groups[0]["""lr"""]
						lowerCamelCase       =      epoch
						lowerCamelCase       =      overall_step
						accelerator.print(F'epoch {epoch}:' ,				snake_case__       )

						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(snake_case__ ,				snake_case__       )



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

			training_function(snake_case__ ,				snake_case__       )


if __name__ == "__main__":
					main()









"""simple docstring"""




import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


lowerCAmelCase							:    Optional[Any]  							=							"""sshleifer/bart-tiny-random"""
lowerCAmelCase							:    List[Any]  							=							"""patrickvonplaten/t5-tiny-random"""






@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@cached_property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return AutoConfig.from_pretrained(_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=1				)
							self.assertEqual(student.config.num_hidden_layers	,				1				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=_a				)
							self.assertEqual(student.config.encoder_layers	,				1				)
							self.assertEqual(student.config.decoder_layers	,				self.teacher_config.encoder_layers				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=1				)
							self.assertEqual(student.config.encoder_layers	,				1				)
							self.assertEqual(student.config.decoder_layers	,				1				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							with self.assertRaises(_a				):
										create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=_a	,				d=_a				)



