




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        =    16
__A        =    32







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      str = 16 ,     UpperCamelCase__				:      Optional[Any] = "bert-base-cased"      )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(UpperCamelCase__      )
				__lowerCamelCase		       =    load_dataset('glue' ,     'mrpc'      )

				def tokenize_function(UpperCamelCase__				:      Union[str, Any]      ):
								# max_length=None => use the model max length (it's actually the default)
								__lowerCamelCase		       =    tokenizer(examples['sentence1'] ,     examples['sentence2'] ,     truncation=UpperCamelCase__ ,     max_length=UpperCamelCase__      )
								return outputs

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

				# 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(UpperCamelCase__				:      Optional[Any]      ):
								# 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(UpperCamelCase__ ,     padding='max_length' ,     max_length=128 ,     return_tensors='pt'      )
								return tokenizer.pad(UpperCamelCase__ ,     padding='longest' ,     return_tensors='pt'      )

				# Instantiate dataloaders.
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['train'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )
				__lowerCamelCase		       =    DataLoader(
				    tokenized_datasets['validation'] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__      )

				return train_dataloader, eval_dataloader







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      List[str]      )     ->       List[str]:





				"""simple docstring"""


				model.eval()
				__lowerCamelCase		       =    0
				for step, batch in enumerate(UpperCamelCase__      ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device      )
								with torch.no_grad():
												__lowerCamelCase		       =    model(**UpperCamelCase__      )
								__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(UpperCamelCase__      ) - 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=UpperCamelCase__ ,     references=UpperCamelCase__ ,     )

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				__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(UpperCamelCase__      )
				__lowerCamelCase       ,						__lowerCamelCase		       =    get_dataloaders(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

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

				# 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=UpperCamelCase__      )

				if accelerator.state.deepspeed_plugin is not None:
								__lowerCamelCase		       =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								__lowerCamelCase		       =    1
				__lowerCamelCase		       =    (len(UpperCamelCase__      ) * 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=UpperCamelCase__ ,     num_warmup_steps=0 ,     num_training_steps=UpperCamelCase__ ,     )
				else:
								__lowerCamelCase		       =    DummyScheduler(UpperCamelCase__ ,     total_num_steps=UpperCamelCase__ ,     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(
				    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				# 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(UpperCamelCase__      ) + 1
								__lowerCamelCase		       =    evaluation_loop(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								accelerator.print('resumed checkpoint performance:' ,     UpperCamelCase__      )
								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(UpperCamelCase__      )
												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(UpperCamelCase__ ,     UpperCamelCase__      ):
								model.train()
								for step, batch in enumerate(UpperCamelCase__      ):
												__lowerCamelCase		       =    model(**UpperCamelCase__      )
												__lowerCamelCase		       =    outputs.loss
												__lowerCamelCase		       =    loss / gradient_accumulation_steps
												accelerator.backward(UpperCamelCase__      )
												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 ,     UpperCamelCase__      )
								accelerator.save_state(UpperCamelCase__      )
								__lowerCamelCase		       =    evaluation_loop(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    accuracy
								__lowerCamelCase		       =    lr_scheduler.get_lr()[0]
								__lowerCamelCase		       =    optimizer.param_groups[0]['lr']
								__lowerCamelCase		       =    epoch
								__lowerCamelCase		       =    overall_step
								accelerator.print(F"""epoch {epoch}:""" ,     UpperCamelCase__      )

								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(UpperCamelCase__ ,     UpperCamelCase__      )







def 			lowerCamelCase_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'      )
				parser.add_argument(
				    '--model_name_or_path' ,     type=UpperCamelCase__ ,     default='bert-base-cased' ,     help='Path to pretrained model or model identifier from huggingface.co/models.' ,     required=UpperCamelCase__ ,     )
				parser.add_argument(
				    '--output_dir' ,     type=UpperCamelCase__ ,     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=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='If the training should continue from a checkpoint folder.' ,     )
				parser.add_argument(
				    '--partial_train_epoch' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='If passed, the training will stop after this number of epochs.' ,     )
				parser.add_argument(
				    '--num_epochs' ,     type=UpperCamelCase__ ,     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(UpperCamelCase__ ,     UpperCamelCase__      )


if __name__ == "__main__":
					main()






'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCamelCase							:   List[str]  						=	argparse.ArgumentParser(
     description=(
         'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
         ' Distillation'
     )
 )
 parser.add_argument('--model_type', default='bert', choices=['bert'])
 parser.add_argument('--model_name', default='bert-base-uncased', type=str)
 parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
 parser.add_argument('--vocab_transform', action='store_true')
 lowerCamelCase							:   str  						=	parser.parse_args()

 if args.model_type == "bert":
  lowerCamelCase							:   List[Any]  						=	BertForMaskedLM.from_pretrained(args.model_name)
  lowerCamelCase							:   Any  						=	'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 lowerCamelCase							:   int  						=	model.state_dict()
 lowerCamelCase							:   int  						=	{}

 for w in ["word_embeddings", "position_embeddings"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.{w}.weight"""]
 for w in ["weight", "bias"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

 lowerCamelCase							:   Tuple  						=	0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
   ]
   lowerCamelCase							:   List[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
   ]

   lowerCamelCase							:   Tuple  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   lowerCamelCase							:   Optional[int]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

   lowerCamelCase							:   Optional[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
   ]
   lowerCamelCase							:   Any  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
   ]
  std_idx += 1

 lowerCamelCase							:   Optional[int]  						=	state_dict['cls.predictions.decoder.weight']
 lowerCamelCase							:   str  						=	state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   str  						=	state_dict[f"""cls.predictions.transform.dense.{w}"""]
   lowerCamelCase							:   Any  						=	state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

 print(f"""N layers selected for distillation: {std_idx}""")
 print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

 print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
 torch.save(compressed_sd, args.dump_checkpoint)




