






"""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


lowercase_				     =     16
lowercase_				     =     32



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6		,       __UpperCamelCase = "bert-base-cased" ):




  """simple docstring"""







  __A									= AutoTokenizer.from_pretrained(__UpperCamelCase )
  __A									= load_dataset('''glue'''		,       '''mrpc''' )

  def tokenize_function(__UpperCamelCase ):
    # max_length=None => use the model max length (it's actually the default)
    __A									= 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
  __A									= 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
  __A									= tokenized_datasets.rename_column('''label'''		,       '''labels''' )

  def collate_fn(__UpperCamelCase ):
    # 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=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

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

  return train_dataloader, eval_dataloader



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """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(__UpperCamelCase )
  __A			,    __A									= get_dataloaders(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

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

  # 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=__UpperCamelCase )

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

  # 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

  # Now we train the model
  __A									= evaluate.load('''glue'''		,       '''mrpc''' )
  __A									= 0
  __A									= {}
  for epoch in range(__UpperCamelCase		,       __UpperCamelCase ):
    model.train()
    for step, batch in enumerate(__UpperCamelCase ):
      __A									= model(**__UpperCamelCase )
      __A									= outputs.loss
      __A									= 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()
    __A									= 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():
        __A									= model(**__UpperCamelCase )
      __A									= outputs.logits.argmax(dim=-1 )
      # It is slightly faster to call this once, than multiple times
      __A			,    __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(__UpperCamelCase ) - 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=__UpperCamelCase		,       references=__UpperCamelCase		,       )

    __A									= metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'		,       __UpperCamelCase )
    __A									= eval_metric['''accuracy''']

    if best_performance < eval_metric["accuracy"]:
      __A									= 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 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= 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.'''		,       )
  __A									= parser.parse_args()
  __A									= {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
  training_function(__UpperCamelCase		,       __UpperCamelCase )


if __name__ == "__main__":
       main()












"""simple docstring"""


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 ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




