'''simple docstring'''






import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE_	   =			16
SCREAMING_SNAKE_CASE_	   =			32






def  UpperCamelCase__					(					_lowercase    :				Accelerator   ,    _lowercase    :				int = 1_6     )							->	List[Any]:
  __UpperCAmelCase:					Optional[int]		= AutoTokenizer.from_pretrained("""bert-base-cased"""     )
  __UpperCAmelCase:					Dict		= load_dataset("""glue"""   ,    """mrpc"""     )

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

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    __UpperCAmelCase:					List[Any]		= datasets.map(
        _lowercase   ,    batched=_lowercase   ,    remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,    )

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

  def collate_fn(_lowercase    :				Optional[Any]     ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __UpperCAmelCase:					Union[str, Any]		= 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __UpperCAmelCase:					int		= 1_6
    elif accelerator.mixed_precision != "no":
      __UpperCAmelCase:					Tuple		= 8
    else:
      __UpperCAmelCase:					str		= None

    return tokenizer.pad(
        _lowercase   ,    padding="""longest"""   ,    max_length=_lowercase   ,    pad_to_multiple_of=_lowercase   ,    return_tensors="""pt"""   ,    )

  # Instantiate dataloaders.
  __UpperCAmelCase:					Optional[int]		= DataLoader(
      tokenized_datasets["""train"""]   ,    shuffle=_lowercase   ,    collate_fn=_lowercase   ,    batch_size=_lowercase     )
  __UpperCAmelCase:					Union[str, Any]		= DataLoader(
      tokenized_datasets["""validation"""]   ,    shuffle=_lowercase   ,    collate_fn=_lowercase   ,    batch_size=_lowercase     )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    SCREAMING_SNAKE_CASE_	   =			mocked_dataloaders  # noqa: F811






def  UpperCamelCase__					(					_lowercase    :				Tuple   ,    _lowercase    :				Optional[int]     )							->	List[Any]:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,    _lowercase     ) == "1":
    __UpperCAmelCase:					Dict		= 2
  # Initialize accelerator
  __UpperCAmelCase:					Optional[Any]		= Accelerator(cpu=args.cpu   ,    mixed_precision=args.mixed_precision     )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __UpperCAmelCase:					Any		= config["""lr"""]
  __UpperCAmelCase:					List[Any]		= int(config["""num_epochs"""]     )
  __UpperCAmelCase:					Dict		= int(config["""seed"""]     )
  __UpperCAmelCase:					Any		= int(config["""batch_size"""]     )

  __UpperCAmelCase:					Optional[int]		= evaluate.load("""glue"""   ,    """mrpc"""     )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_lowercase     )
  def inner_training_loop(_lowercase    :				Optional[int]     ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_lowercase     )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCAmelCase:					Any		= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""   ,    return_dict=_lowercase     )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __UpperCAmelCase:					List[str]		= model.to(accelerator.device     )

    # Instantiate optimizer
    __UpperCAmelCase:					List[Any]		= AdamW(params=model.parameters()   ,    lr=_lowercase     )
    __UpperCAmelCase, __UpperCAmelCase:					Any		= get_dataloaders(_lowercase   ,    _lowercase     )

    # Instantiate scheduler
    __UpperCAmelCase:					str		= get_linear_schedule_with_warmup(
        optimizer=_lowercase   ,    num_warmup_steps=1_0_0   ,    num_training_steps=(len(_lowercase     ) * num_epochs)   ,    )

    # 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, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					Optional[int]		= accelerator.prepare(
        _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )

    # Now we train the model
    for epoch in range(_lowercase     ):
      model.train()
      for step, batch in enumerate(_lowercase     ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device     )
        __UpperCAmelCase:					List[str]		= model(**_lowercase     )
        __UpperCAmelCase:					List[Any]		= outputs.loss
        accelerator.backward(_lowercase     )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_lowercase     ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device     )
        with torch.no_grad():
          __UpperCAmelCase:					Optional[int]		= model(**_lowercase     )
        __UpperCAmelCase:					Union[str, Any]		= outputs.logits.argmax(dim=-1     )
        __UpperCAmelCase, __UpperCAmelCase:					int		= accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
        metric.add_batch(
            predictions=_lowercase   ,    references=_lowercase   ,    )

      __UpperCAmelCase:					List[Any]		= metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''   ,    _lowercase     )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()






def  UpperCamelCase__					(					)							->	Tuple:
  __UpperCAmelCase:					Any		= argparse.ArgumentParser(description="""Simple example of training script."""     )
  parser.add_argument(
      """--mixed_precision"""   ,    type=_lowercase   ,    default=_lowercase   ,    choices=["""no""", """fp16""", """bf16""", """fp8"""]   ,    help="""Whether to use mixed precision. Choose"""
      """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
      """and an Nvidia Ampere GPU."""   ,    )
  parser.add_argument("""--cpu"""   ,    action="""store_true"""   ,    help="""If passed, will train on the CPU."""     )
  __UpperCAmelCase:					List[str]		= parser.parse_args()
  __UpperCAmelCase:					int		= {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
  training_function(_lowercase   ,    _lowercase     )


if __name__ == "__main__":
    main()
'''simple docstring'''






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=18   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= size if size is not None else {"""height""": 18, """width""": 18}
    __UpperCAmelCase:					Tuple		= parent
    __UpperCAmelCase:					Any		= batch_size
    __UpperCAmelCase:					str		= num_channels
    __UpperCAmelCase:					Any		= image_size
    __UpperCAmelCase:					Optional[int]		= min_resolution
    __UpperCAmelCase:					Tuple		= max_resolution
    __UpperCAmelCase:					Any		= do_resize
    __UpperCAmelCase:					Any		= size
    __UpperCAmelCase:					Optional[Any]		= do_normalize







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]					),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					ImageGPTImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= ImageGPTImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """clusters"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""height""": 18, """width""": 18}					)

    __UpperCAmelCase:					int		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42					)
    self.assertEqual(image_processor.size   ,    {"""height""": 42, """width""": 42}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.image_processing_class(**self.image_processor_dict					)
    __UpperCAmelCase:					str		= json.loads(image_processor.to_json_string()					)
    for key, value in self.image_processor_dict.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    obj[key]					)					)
      else:
        self.assertEqual(obj[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    """image_processor.json"""					)
      image_processor_first.to_json_file(snake_case_					)
      __UpperCAmelCase:					List[str]		= self.image_processing_class.from_json_file(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)

    with tempfile.TemporaryDirectory() as tmpdirname:
      image_processor_first.save_pretrained(snake_case_					)
      __UpperCAmelCase:					Any		= self.image_processing_class.from_pretrained(snake_case_					).to_dict()

    __UpperCAmelCase:					str		= image_processor_first.to_dict()
    for key, value in image_processor_first.items():
      if key == "clusters":
        self.assertTrue(np.array_equal(snake_case_   ,    image_processor_second[key]					)					)
      else:
        self.assertEqual(image_processor_first[key]   ,    snake_case_					)







  @unittest.skip("""ImageGPT requires clusters at initialization"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	List[str]:
  __UpperCAmelCase:					Any		= load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,    split="""test"""     )

  __UpperCAmelCase:					List[Any]		= Image.open(dataset[4]["""file"""]     )
  __UpperCAmelCase:					Dict		= Image.open(dataset[5]["""file"""]     )

  __UpperCAmelCase:					Dict		= [imagea, imagea]

  return images







@require_vision
@require_torch
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""					)

    __UpperCAmelCase:					Dict		= prepare_images()

    # test non-batched
    __UpperCAmelCase:					Tuple		= image_processing(images[0]   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (1, 1024)					)

    __UpperCAmelCase:					int		= [306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist()   ,    snake_case_					)

    # test batched
    __UpperCAmelCase:					Union[str, Any]		= image_processing(snake_case_   ,    return_tensors="""pt"""					)

    self.assertIsInstance(encoding.input_ids   ,    torch.LongTensor					)
    self.assertEqual(encoding.input_ids.shape   ,    (2, 1024)					)

    __UpperCAmelCase:					Tuple		= [303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()   ,    snake_case_					)