

'''simple docstring'''




import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

_lowerCAmelCase    			=						16
_lowerCAmelCase    			=						32




def 						UpperCamelCase       ( a		,						a = 16 )      ->	Dict:






     '''simple docstring'''
     __magic_name__						   =				AutoTokenizer.from_pretrained('''bert-base-cased''' )
     __magic_name__						   =				load_dataset('''glue'''		,						'''mrpc''' )

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

     def collate_fn(a ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __magic_name__						   =				128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __magic_name__						   =				16
          elif accelerator.mixed_precision != "no":
               __magic_name__						   =				8
          else:
               __magic_name__						   =				None

          return tokenizer.pad(
              _UpperCAmelCase		,						padding='''longest'''		,						max_length=_UpperCAmelCase		,						pad_to_multiple_of=_UpperCAmelCase		,						return_tensors='''pt'''		,						)

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

     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

      _lowerCAmelCase    			=						mocked_dataloaders  # noqa: F811




def 						UpperCamelCase       ( a		,						a )      ->	List[Any]:






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

     # New Code #
     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps		,						'''isdigit''' ):
          if args.checkpointing_steps == "epoch":
               __magic_name__						   =				args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __magic_name__						   =				int(args.checkpointing_steps )
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
     else:
          __magic_name__						   =				None

     set_seed(_UpperCAmelCase )

     __magic_name__   ,  __magic_name__						   =				get_dataloaders(_UpperCAmelCase		,						_UpperCAmelCase )
     __magic_name__						   =				evaluate.load('''glue'''		,						'''mrpc''' )

     # If the batch size is too big we use gradient accumulation
     __magic_name__						   =				1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          __magic_name__						   =				batch_size // MAX_GPU_BATCH_SIZE
          __magic_name__						   =				MAX_GPU_BATCH_SIZE

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

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

     # Instantiate optimizer
     __magic_name__						   =				AdamW(params=model.parameters()		,						lr=_UpperCAmelCase )

     # Instantiate scheduler
     __magic_name__						   =				get_linear_schedule_with_warmup(
         optimizer=_UpperCAmelCase		,						num_warmup_steps=100		,						num_training_steps=(len(_UpperCAmelCase ) * num_epochs) // gradient_accumulation_steps		,						)

     # 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.
     __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__						   =				accelerator.prepare(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase )

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

     # We need to load the checkpoint back in before training here with `load_state`
     # The total number of epochs is adjusted based on where the state is being loaded from,
     # as we assume continuation of the same training script
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
               accelerator.load_state(args.resume_from_checkpoint )
               __magic_name__						   =				os.path.basename(args.resume_from_checkpoint )
          else:
               # Get the most recent checkpoint
               __magic_name__						   =				[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
               dirs.sort(key=os.path.getctime )
               __magic_name__						   =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __magic_name__						   =				os.path.splitext(_UpperCAmelCase )[0]

          if "epoch" in training_difference:
               __magic_name__						   =				int(training_difference.replace('''epoch_'''		,						'''''' ) ) + 1
               __magic_name__						   =				None
          else:
               __magic_name__						   =				int(training_difference.replace('''step_'''		,						'''''' ) )
               __magic_name__						   =				resume_step // len(_UpperCAmelCase )
               resume_step -= starting_epoch * len(_UpperCAmelCase )

    # Now we train the model
     for epoch in range(_UpperCAmelCase		,						_UpperCAmelCase ):
          model.train()
          # New Code #
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               __magic_name__						   =				accelerator.skip_first_batches(_UpperCAmelCase		,						_UpperCAmelCase )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __magic_name__						   =				train_dataloader
          for step, batch in enumerate(_UpperCAmelCase ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device )
               __magic_name__						   =				model(**_UpperCAmelCase )
               __magic_name__						   =				outputs.loss
               __magic_name__						   =				loss / gradient_accumulation_steps
               accelerator.backward(_UpperCAmelCase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
               # New Code #
               overall_step += 1

               # New Code #
               # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
               # These are saved to folders named `step_{overall_step}`
               # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
               # If mixed precision was used, will also save a "scalar.bin" file
               if isinstance(_UpperCAmelCase		,						_UpperCAmelCase ):
                    __magic_name__						   =				F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __magic_name__						   =				os.path.join(args.output_dir		,						_UpperCAmelCase )
                         accelerator.save_state(_UpperCAmelCase )

          model.eval()
          for step, batch in enumerate(_UpperCAmelCase ):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device )
               with torch.no_grad():
                    __magic_name__						   =				model(**_UpperCAmelCase )
               __magic_name__						   =				outputs.logits.argmax(dim=-1 )
               __magic_name__   ,  __magic_name__						   =				accelerator.gather_for_metrics((predictions, batch['''labels''']) )
               metric.add_batch(
                   predictions=_UpperCAmelCase		,						references=_UpperCAmelCase		,						)

          __magic_name__						   =				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''		,						_UpperCAmelCase )

          # New Code #
          # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
          # These are saved to folders named `epoch_{epoch}`
          # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
          # If mixed precision was used, will also save a "scalar.bin" file
          if checkpointing_steps == "epoch":
               __magic_name__						   =				F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __magic_name__						   =				os.path.join(args.output_dir		,						_UpperCAmelCase )
               accelerator.save_state(_UpperCAmelCase )




def 						UpperCamelCase       ( )      ->	str:






     '''simple docstring'''
     __magic_name__						   =				argparse.ArgumentParser(description='''Simple example of training script.''' )
     parser.add_argument(
         '''--mixed_precision'''		,						type=_UpperCAmelCase		,						default=_UpperCAmelCase		,						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.''' )
     parser.add_argument(
         '''--checkpointing_steps'''		,						type=_UpperCAmelCase		,						default=_UpperCAmelCase		,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,						)
     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(
         '''--resume_from_checkpoint'''		,						type=_UpperCAmelCase		,						default=_UpperCAmelCase		,						help='''If the training should continue from a checkpoint folder.'''		,						)
     __magic_name__						   =				parser.parse_args()
     __magic_name__						   =				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCAmelCase		,						_UpperCAmelCase )


if __name__ == "__main__":
      main()













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase		       =[self.resize(image=UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





