

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_						       =	1_6
SCREAMING_SNAKE_CASE_						       =	3_2



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 16     )			->       Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
      SCREAMING_SNAKE_CASE		=						load_dataset("""glue"""			,						"""mrpc"""     )

      def tokenize_function(_SCREAMING_SNAKE_CASE     ):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE		=						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
      # starting with the main process first:
      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE		=						datasets.map(
                _SCREAMING_SNAKE_CASE			,						batched=_SCREAMING_SNAKE_CASE			,						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
      SCREAMING_SNAKE_CASE		=						tokenized_datasets.rename_column("""label"""			,						"""labels"""     )

      def collate_fn(_SCREAMING_SNAKE_CASE     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE		=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE		=						16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE		=						8
            else:
                  SCREAMING_SNAKE_CASE		=						None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE		=						DataLoader(
          tokenized_datasets["""train"""]			,						shuffle=_SCREAMING_SNAKE_CASE			,						collate_fn=_SCREAMING_SNAKE_CASE			,						batch_size=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						DataLoader(
          tokenized_datasets["""validation"""]			,						shuffle=_SCREAMING_SNAKE_CASE			,						collate_fn=_SCREAMING_SNAKE_CASE			,						batch_size=_SCREAMING_SNAKE_CASE     )

      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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,						_SCREAMING_SNAKE_CASE     ) == "1":
            SCREAMING_SNAKE_CASE		=						2
      # Initialize accelerator
      SCREAMING_SNAKE_CASE		=						Accelerator(cpu=args.cpu			,						mixed_precision=args.mixed_precision     )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE		=						config["""lr"""]
      SCREAMING_SNAKE_CASE		=						int(config["""num_epochs"""]     )
      SCREAMING_SNAKE_CASE		=						int(config["""seed"""]     )
      SCREAMING_SNAKE_CASE		=						int(config["""batch_size"""]     )

      SCREAMING_SNAKE_CASE		=						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=_SCREAMING_SNAKE_CASE     )
      def inner_training_loop(_SCREAMING_SNAKE_CASE     ):
            # 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(_SCREAMING_SNAKE_CASE     )

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

            # 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).
            SCREAMING_SNAKE_CASE		=						model.to(accelerator.device     )

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE		=						AdamW(params=model.parameters()			,						lr=_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_dataloaders(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

            # Instantiate scheduler
            SCREAMING_SNAKE_CASE		=						get_linear_schedule_with_warmup(
                optimizer=_SCREAMING_SNAKE_CASE			,						num_warmup_steps=1_00			,						num_training_steps=(len(_SCREAMING_SNAKE_CASE     ) * 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.
            SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.prepare(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

            # Now we train the model
            for epoch in range(_SCREAMING_SNAKE_CASE     ):
                  model.train()
                  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     )
                        SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						outputs.loss
                        accelerator.backward(_SCREAMING_SNAKE_CASE     )
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  model.eval()
                  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():
                              SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						outputs.logits.argmax(dim=-1     )
                        SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                        metric.add_batch(
                            predictions=_SCREAMING_SNAKE_CASE			,						references=_SCREAMING_SNAKE_CASE			,						)

                  SCREAMING_SNAKE_CASE		=						metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(F"""epoch {epoch}:"""			,						_SCREAMING_SNAKE_CASE     )

    # 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        __lowercase       (				)			->       Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						argparse.ArgumentParser(description="""Simple example of training script."""     )
      parser.add_argument(
          """--mixed_precision"""			,						type=_SCREAMING_SNAKE_CASE			,						default=_SCREAMING_SNAKE_CASE			,						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."""     )
      SCREAMING_SNAKE_CASE		=						parser.parse_args()
      SCREAMING_SNAKE_CASE		=						{"""lr""": 2E-5, """num_epochs""": 3, """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.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=7						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   Union[str, Any]=18						,lowerCamelCase__						:   Optional[int]=30						,lowerCamelCase__						:   int=400						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Union[str, Any]=None						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Optional[int]=[0.5, 0.5, 0.5]						,lowerCamelCase__						:   Any=[0.5, 0.5, 0.5]						,)					->		List[str]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Union[str, Any]:




           '''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 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      DPTImageProcessor if is_vision_available() else None






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPTImageProcessingTester(self	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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						:   int	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size						,{"""height""": 18, """width""": 18}	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,Image.Image	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,numpify=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,np.ndarray	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__						,torchify=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,torch.Tensor	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           SCREAMING_SNAKE_CASE		=						image_processing(lowerCamelCase__						,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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)


