





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    :					int =      1_6
A    :					Dict =      3_2







def    __lowerCamelCase					(							__a   :Any    ,     __a   :Optional[int] = 1_6    ,     __a   :Optional[Any] = "bert-base-cased"    )		->				str:






		"""simple docstring"""

		A__        =   AutoTokenizer.from_pretrained(lowerCAmelCase_    )
		A__        =   load_dataset("""glue"""    ,     """mrpc"""    )

		def tokenize_function(__a   :List[Any]    ):
				# max_length=None => use the model max length (it's actually the default)
				A__        =   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
		A__        =   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
		A__        =   tokenized_datasets.rename_column("""label"""    ,     """labels"""    )

		def collate_fn(__a   :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(lowerCAmelCase_    ,     padding="""max_length"""    ,     max_length=1_2_8    ,     return_tensors="""pt"""    )
				return tokenizer.pad(lowerCAmelCase_    ,     padding="""longest"""    ,     return_tensors="""pt"""    )

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

		return train_dataloader, eval_dataloader







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :List[Any]    ,     __a   :Any    ,     __a   :Dict    )		->				List[Any]:






		"""simple docstring"""

		model.eval()
		A__        =   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():
						A__        =   model(**lowerCAmelCase_    )
				A__        =   outputs.logits.argmax(dim=-1    )
				# It is slightly faster to call this once, than multiple times
				A__        =   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:
								A__        =   predictions[: len(eval_dataloader.dataset    ) - samples_seen]
								A__        =   references[: len(eval_dataloader.dataset    ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=lowerCAmelCase_    ,     references=lowerCAmelCase_    ,     )

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







def    __lowerCamelCase					(							__a   :Tuple    ,     __a   :Tuple    )		->				Any:






		"""simple docstring"""

		A__        =   Accelerator()

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

		set_seed(lowerCAmelCase_    )
		A__        =   get_dataloaders(lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    )

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

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

		if accelerator.state.deepspeed_plugin is not None:
				A__        =   accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				A__        =   1
		A__        =   (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
		):
				A__        =   get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_    ,     num_warmup_steps=0    ,     num_training_steps=lowerCAmelCase_    ,     )
		else:
				A__        =   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.
		A__        =   accelerator.prepare(
		    lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    )

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

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

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

						overall_step += 1
				A__        =   F'epoch_{epoch}'
				A__        =   os.path.join(args.output_dir    ,     lowerCAmelCase_    )
				accelerator.save_state(lowerCAmelCase_    )
				A__        =   evaluation_loop(lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    ,     lowerCAmelCase_    )
				A__        =   accuracy
				A__        =   lr_scheduler.get_lr()[0]
				A__        =   optimizer.param_groups[0]['lr']
				A__        =   epoch
				A__        =   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    __lowerCamelCase					(							)		->				List[Any]:






		"""simple docstring"""

		A__        =   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."""    ,     )
		A__        =   parser.parse_args()
		A__        =   {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

		training_function(lowerCAmelCase_    ,     lowerCAmelCase_    )


if __name__ == "__main__":
					main()








'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCAmelCase									=   logging.getLogger()

@unittest.skip('Temporarily disable the doc tests.'   )
@require_torch
@require_tf
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Path	,_UpperCAmelCase					:       Union[str, None] = None	,_UpperCAmelCase					:       Union[List[str], None] = None	,_UpperCAmelCase					:       Union[str, List[str], None] = None	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Dict        =							[file for file in os.listdir(_UpperCAmelCase		) if os.path.isfile(os.path.join(_UpperCAmelCase	,_UpperCAmelCase		)		)]

									if identifier is not None:
											_a     :      str        =							[file for file in files if identifier in file]

									if n_identifier is not None:
											if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													for n_ in n_identifier:
															_a     :      int        =							[file for file in files if n_ not in file]
											else:
													_a     :      Optional[Any]        =							[file for file in files if n_identifier not in file]

									_a     :      Dict        =							ignore_files or []
									ignore_files.append('__init__.py'		)
									_a     :      List[str]        =							[file for file in files if file not in ignore_files]

									for file in files:
											# Open all files
											print('Testing'	,_UpperCAmelCase		)

											if only_modules:
													_a     :      Any        =							file.split('.'		)[0]
													try:
															_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,_UpperCAmelCase		)
															_a     :      Dict        =							doctest.DocTestSuite(_UpperCAmelCase		)
															_a     :      Optional[int]        =							unittest.TextTestRunner().run(_UpperCAmelCase		)
															self.assertIs(len(result.failures		)	,0		)
													except AttributeError:
															logger.info(F"""{module_identifier} is not a module."""		)
											else:
													_a     :      str        =							doctest.testfile(str('..' / directory / file		)	,optionflags=doctest.ELLIPSIS		)
													self.assertIs(result.failed	,0		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[Any]        =							Path('src/transformers'		)
									_a     :      Optional[Any]        =							'modeling'
									_a     :      Union[str, Any]        =							[
									    'modeling_ctrl.py',
									    'modeling_tf_ctrl.py',
									]
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase	,ignore_files=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      str        =							Path('src/transformers'		)
									_a     :      List[str]        =							'tokenization'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Any        =							Path('src/transformers'		)
									_a     :      str        =							'configuration'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Tuple        =							Path('src/transformers'		)
									_a     :      Optional[int]        =							['configuration', 'modeling', 'tokenization']
									self.analyze_directory(_UpperCAmelCase	,n_identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							Path('docs/source'		)
									_a     :      List[str]        =							['favicon.ico']
									self.analyze_directory(_UpperCAmelCase	,ignore_files=_UpperCAmelCase	,only_modules=_UpperCAmelCase		)




