






"""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_       			=   16
lowerCamelCase_       			=   32

def        snake_case   (     A__			,A__ = 16			,A__ = "bert-base-cased"		):
			UpperCAmelCase_  :				Optional[int]      =      AutoTokenizer.from_pretrained(A__		)
			UpperCAmelCase_  :				Optional[int]      =      load_dataset("glue"			,"mrpc"		)

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

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

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

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

			# Instantiate dataloaders.
			UpperCAmelCase_  :				List[str]      =      DataLoader(
			    tokenized_datasets["train"]			,shuffle=A__			,collate_fn=A__			,batch_size=A__		)
			UpperCAmelCase_  :				Any      =      DataLoader(
			    tokenized_datasets["validation"]			,shuffle=A__			,collate_fn=A__			,batch_size=A__		)

			return train_dataloader, eval_dataloader

def        snake_case   (     A__			,A__		):
			# Initialize accelerator
			UpperCAmelCase_  :				Optional[Any]      =      Accelerator()

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

			set_seed(A__		)
			UpperCAmelCase_  :				Union[str, Any]      =      get_dataloaders(A__			,A__			,A__		)

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

			# Instantiate optimizer
			UpperCAmelCase_  :				Tuple      =      (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_  :				List[str]      =      optimizer_cls(params=model.parameters()			,lr=A__		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_  :				str      =      accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						UpperCAmelCase_  :				Dict      =      1
			UpperCAmelCase_  :				int      =      (len(A__		) * 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_  :				Union[str, Any]      =      get_linear_schedule_with_warmup(
						    optimizer=A__			,num_warmup_steps=0			,num_training_steps=A__			,)
			else:
						UpperCAmelCase_  :				Dict      =      DummyScheduler(A__			,total_num_steps=A__			,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_  :				Any      =      accelerator.prepare(
			    A__			,A__			,A__			,A__			,A__		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_  :				Union[str, Any]      =      0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_  :				Optional[int]      =      0

			# Now we train the model
			UpperCAmelCase_  :				List[Any]      =      evaluate.load("glue"			,"mrpc"		)
			UpperCAmelCase_  :				Optional[int]      =      0
			UpperCAmelCase_  :				Optional[int]      =      {}
			for epoch in range(A__			,A__		):
						model.train()
						for step, batch in enumerate(A__		):
									UpperCAmelCase_  :				Dict      =      model(**A__		)
									UpperCAmelCase_  :				List[Any]      =      outputs.loss
									UpperCAmelCase_  :				List[str]      =      loss / gradient_accumulation_steps
									accelerator.backward(A__		)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						UpperCAmelCase_  :				Any      =      0
						for step, batch in enumerate(A__		):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device		)
									with torch.no_grad():
												UpperCAmelCase_  :				Any      =      model(**A__		)
									UpperCAmelCase_  :				List[Any]      =      outputs.logits.argmax(dim=-1		)
									# It is slightly faster to call this once, than multiple times
									UpperCAmelCase_  :				List[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(A__		) - 1:
															UpperCAmelCase_  :				Dict      =      predictions[: len(eval_dataloader.dataset		) - samples_seen]
															UpperCAmelCase_  :				Union[str, Any]      =      references[: len(eval_dataloader.dataset		) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=A__			,references=A__			,)

						UpperCAmelCase_  :				str      =      metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}:"""			,A__		)
						UpperCAmelCase_  :				Union[str, Any]      =      eval_metric["accuracy"]

						if best_performance < eval_metric["accuracy"]:
									UpperCAmelCase_  :				List[Any]      =      eval_metric["accuracy"]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,"all_results.json"		)			,"w"		) as f:
									json.dump(A__			,A__		)

def        snake_case   (     ):
			UpperCAmelCase_  :				Union[str, Any]      =      argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
			parser.add_argument(
			    "--model_name_or_path"			,type=A__			,default="bert-base-cased"			,help="Path to pretrained model or model identifier from huggingface.co/models."			,required=A__			,)
			parser.add_argument(
			    "--output_dir"			,type=A__			,default="."			,help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,)
			parser.add_argument(
			    "--performance_lower_bound"			,type=A__			,default=A__			,help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."			,)
			parser.add_argument(
			    "--num_epochs"			,type=A__			,default=3			,help="Number of train epochs."			,)
			UpperCAmelCase_  :				Tuple      =      parser.parse_args()
			UpperCAmelCase_  :				Optional[int]      =      {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
			training_function(A__			,A__		)


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     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Path			,			lowerCAmelCase_      :				Union[str, None] = None			,			lowerCAmelCase_      :				Union[List[str], None] = None			,			lowerCAmelCase_      :				Union[str, List[str], None] = None			,			lowerCAmelCase_      :				bool = True			,			)		->    Tuple:
				UpperCAmelCase_  :				Optional[Any]      =      [file for file in os.listdir(lowerCAmelCase_		) if os.path.isfile(os.path.join(lowerCAmelCase_			,			lowerCAmelCase_		)		)]

				if identifier is not None:
							UpperCAmelCase_  :				Optional[int]      =      [file for file in files if identifier in file]

				if n_identifier is not None:
							if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
										for n_ in n_identifier:
													UpperCAmelCase_  :				List[Any]      =      [file for file in files if n_ not in file]
							else:
										UpperCAmelCase_  :				Optional[Any]      =      [file for file in files if n_identifier not in file]

				UpperCAmelCase_  :				List[Any]      =      ignore_files or []
				ignore_files.append("__init__.py"		)
				UpperCAmelCase_  :				Optional[Any]      =      [file for file in files if file not in ignore_files]

				for file in files:
							# Open all files
							print("Testing"			,			lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    List[Any]:
				UpperCAmelCase_  :				Optional[int]      =      Path("src/transformers"		)
				UpperCAmelCase_  :				List[Any]      =      "modeling"
				UpperCAmelCase_  :				Tuple      =      [
				    "modeling_ctrl.py",
				    "modeling_tf_ctrl.py",
				]
				self.analyze_directory(lowerCAmelCase_			,			identifier=lowerCAmelCase_			,			ignore_files=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				UpperCAmelCase_  :				List[str]      =      Path("src/transformers"		)
				UpperCAmelCase_  :				int      =      "tokenization"
				self.analyze_directory(lowerCAmelCase_			,			identifier=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				int      =      Path("src/transformers"		)
				UpperCAmelCase_  :				Any      =      "configuration"
				self.analyze_directory(lowerCAmelCase_			,			identifier=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Any:
				UpperCAmelCase_  :				int      =      Path("src/transformers"		)
				UpperCAmelCase_  :				List[Any]      =      ["configuration", "modeling", "tokenization"]
				self.analyze_directory(lowerCAmelCase_			,			n_identifier=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Dict:
				UpperCAmelCase_  :				List[str]      =      Path("docs/source"		)
				UpperCAmelCase_  :				int      =      ["favicon.ico"]
				self.analyze_directory(lowerCAmelCase_			,			ignore_files=lowerCAmelCase_			,			only_modules=lowerCAmelCase_		)



