

"""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


_a       =							16
_a       =							32




def   _A  (  UpperCamelCase_     :       Accelerator,			UpperCamelCase_     :       int = 16,			UpperCamelCase_     :       str = "bert-base-cased")	->						List[str]:
   '''simple docstring'''


   __lowercase								=						AutoTokenizer.from_pretrained(UpperCamelCase_)
   __lowercase								=						load_dataset("glue",			"mrpc")

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

   def collate_fn(UpperCamelCase_     :       Tuple):
      # 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.
   __lowercase								=						DataLoader(
       tokenized_datasets["train"],			shuffle=UpperCamelCase_,			collate_fn=UpperCamelCase_,			batch_size=UpperCamelCase_)
   __lowercase								=						DataLoader(
       tokenized_datasets["validation"],			shuffle=UpperCamelCase_,			collate_fn=UpperCamelCase_,			batch_size=UpperCamelCase_)

   return train_dataloader, eval_dataloader




def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Tuple)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						Accelerator()

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

   set_seed(UpperCamelCase_)
   __lowercase		,__lowercase								=						get_dataloaders(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

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

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

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

   # We need to keep track of how many total steps we have iterated over
   __lowercase								=						0
   # We also need to keep track of the stating epoch so files are named properly
   __lowercase								=						0

   # Now we train the model
   __lowercase								=						evaluate.load("glue",			"mrpc")
   __lowercase								=						0
   __lowercase								=						{}
   for epoch in range(UpperCamelCase_,			UpperCamelCase_):
      model.train()
      for step, batch in enumerate(UpperCamelCase_):
         __lowercase								=						model(**UpperCamelCase_)
         __lowercase								=						outputs.loss
         __lowercase								=						loss / gradient_accumulation_steps
         accelerator.backward(UpperCamelCase_)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

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

      __lowercase								=						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:""",			UpperCamelCase_)
      __lowercase								=						eval_metric["accuracy"]

      if best_performance < eval_metric["accuracy"]:
         __lowercase								=						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(UpperCamelCase_,			UpperCamelCase_)




def   _A  (  )	->						List[str]:
   '''simple docstring'''


   __lowercase								=						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(
       "--performance_lower_bound",			type=UpperCamelCase_,			default=UpperCamelCase_,			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=UpperCamelCase_,			default=3,			help="Number of train epochs.",			)
   __lowercase								=						parser.parse_args()
   __lowercase								=						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(UpperCamelCase_,			UpperCamelCase_)


if __name__ == "__main__":
   main()








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




						@property
						def   lowercase_       ( self							)    ->			Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''size'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_       :					Tuple			 = self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 8_4, '''width''': 8_4}							)




						def   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  Image.Image							)

										# Test not batched input
										lowerCAmelCase_       :					Tuple			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  np.ndarray							)

										# Test not batched input
										lowerCAmelCase_       :					Any			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Tuple			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )




						def   lowercase_       ( self							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_       :					List[Any]			 = image_processing(image_inputs[0]      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )

										# Test batched
										lowerCAmelCase_       :					Dict			 = image_processing(__lowercase      ,  return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape      ,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )      ,  )