



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_      :      Dict    =     16
a_      :      Union[str, Any]    =     32






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = 16							,  __UpperCAmelCase = "bert-base-cased"       ):
 a__												= AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
 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=SCREAMING_SNAKE_CASE_							,  max_length=SCREAMING_SNAKE_CASE_       )
  return outputs

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

 # 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(SCREAMING_SNAKE_CASE_							,  padding='''max_length'''							,  max_length=128							,  return_tensors='''pt'''       )
  return tokenizer.pad(SCREAMING_SNAKE_CASE_							,  padding='''longest'''							,  return_tensors='''pt'''       )

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

 return train_dataloader, eval_dataloader






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

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






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 # Initialize accelerator
 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(SCREAMING_SNAKE_CASE_       )
 a__							,			a__												= get_dataloaders(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_       )

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

 # 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=SCREAMING_SNAKE_CASE_       )

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

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

  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(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_       )






def     __a					(		):
 a__												= argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
 parser.add_argument(
     '''--model_name_or_path'''							,  type=SCREAMING_SNAKE_CASE_							,  default='''bert-base-cased'''							,  help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,  required=SCREAMING_SNAKE_CASE_							,  )
 parser.add_argument(
     '''--output_dir'''							,  type=SCREAMING_SNAKE_CASE_							,  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=SCREAMING_SNAKE_CASE_							,  default=SCREAMING_SNAKE_CASE_							,  help='''If the training should continue from a checkpoint folder.'''							,  )
 parser.add_argument(
     '''--partial_train_epoch'''							,  type=SCREAMING_SNAKE_CASE_							,  default=SCREAMING_SNAKE_CASE_							,  help='''If passed, the training will stop after this number of epochs.'''							,  )
 parser.add_argument(
     '''--num_epochs'''							,  type=SCREAMING_SNAKE_CASE_							,  default=2							,  help='''Number of train epochs.'''							,  )
 a__												= parser.parse_args()
 a__												= {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

 training_function(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_       )


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_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=10				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=None				,			):
									_lowercase					       =					size if size is not None else {"""shortest_edge""": 18}
									_lowercase					       =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_frames
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std
									_lowercase					       =					crop_size






			def    UpperCamelCase_				(      self  ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							VivitImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VivitImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_mean"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_std"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_normalize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_resize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_center_crop"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 18}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 18, """width""": 18}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 42}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 84, """width""": 84}  )






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL videos
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)