






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


lowerCAmelCase_	:    Tuple            =      1_6
lowerCAmelCase_	:    Union[str, Any]            =      3_2






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase = 16	,      lowerCAmelCase = "bert-base-cased"		):







       '''simple docstring'''
       UpperCAmelCase	    =			AutoTokenizer.from_pretrained(snake_case__		)
       UpperCAmelCase	    =			load_dataset("""glue"""	,      """mrpc"""		)

       def tokenize_function(lowerCAmelCase		):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase	    =			tokenizer(examples["""sentence1"""]	,      examples["""sentence2"""]	,      truncation=snake_case__	,      max_length=snake_case__		)
              return outputs

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

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

       def collate_fn(lowerCAmelCase		):
              # 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(snake_case__	,      padding="""max_length"""	,      max_length=128	,      return_tensors="""pt"""		)
              return tokenizer.pad(snake_case__	,      padding="""longest"""	,      return_tensors="""pt"""		)

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

       return train_dataloader, eval_dataloader






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # Initialize accelerator
       UpperCAmelCase	    =			Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase	    =			config["""lr"""]
       UpperCAmelCase	    =			int(config["""num_epochs"""]		)
       UpperCAmelCase	    =			int(config["""seed"""]		)
       UpperCAmelCase	    =			int(config["""batch_size"""]		)
       UpperCAmelCase	    =			args.model_name_or_path

       set_seed(snake_case__		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			get_dataloaders(snake_case__	,      snake_case__	,      snake_case__		)

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

       # Instantiate optimizer
       UpperCAmelCase	    =			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       UpperCAmelCase	    =			optimizer_cls(params=model.parameters()	,      lr=snake_case__		)

       if accelerator.state.deepspeed_plugin is not None:
              UpperCAmelCase	    =			accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              UpperCAmelCase	    =			1
       UpperCAmelCase	    =			(len(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
       ):
              UpperCAmelCase	    =			get_linear_schedule_with_warmup(
                  optimizer=snake_case__	,      num_warmup_steps=0	,      num_training_steps=snake_case__	,      )
       else:
              UpperCAmelCase	    =			DummyScheduler(snake_case__	,      total_num_steps=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.
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			accelerator.prepare(
           snake_case__	,      snake_case__	,      snake_case__	,      snake_case__	,      snake_case__		)

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

       # Now we train the model
       UpperCAmelCase	    =			evaluate.load("""glue"""	,      """mrpc"""		)
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			{}
       for epoch in range(snake_case__	,      snake_case__		):
              model.train()
              for step, batch in enumerate(snake_case__		):
                     UpperCAmelCase	    =			model(**snake_case__		)
                     UpperCAmelCase	    =			outputs.loss
                     UpperCAmelCase	    =			loss / gradient_accumulation_steps
                     accelerator.backward(snake_case__		)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              UpperCAmelCase	    =			0
              for step, batch in enumerate(snake_case__		):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device		)
                     with torch.no_grad():
                            UpperCAmelCase	    =			model(**snake_case__		)
                     UpperCAmelCase	    =			outputs.logits.argmax(dim=-1		)
                     # It is slightly faster to call this once, than multiple times
                     UpperCAmelCase    ,       UpperCAmelCase	    =			accelerator.gather(
                         (predictions, batch["""labels"""])		)  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(snake_case__		) - 1:
                                   UpperCAmelCase	    =			predictions[: len(eval_dataloader.dataset		) - samples_seen]
                                   UpperCAmelCase	    =			references[: len(eval_dataloader.dataset		) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=snake_case__	,      references=snake_case__	,      )

              UpperCAmelCase	    =			metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''	,      snake_case__		)
              UpperCAmelCase	    =			eval_metric["""accuracy"""]

              if best_performance < eval_metric["accuracy"]:
                     UpperCAmelCase	    =			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(snake_case__	,      snake_case__		)






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
       parser.add_argument(
           """--model_name_or_path"""	,      type=snake_case__	,      default="""bert-base-cased"""	,      help="""Path to pretrained model or model identifier from huggingface.co/models."""	,      required=snake_case__	,      )
       parser.add_argument(
           """--output_dir"""	,      type=snake_case__	,      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=snake_case__	,      default=snake_case__	,      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=snake_case__	,      default=3	,      help="""Number of train epochs."""	,      )
       UpperCAmelCase	    =			parser.parse_args()
       UpperCAmelCase	    =			{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__	,      snake_case__		)


if __name__ == "__main__":
 main()








"""simple docstring"""







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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase_	:    Union[str, Any]            =      logging.get_logger(__name__)





class   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	['pixel_values']



  def __init__(      self ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 0.9 ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = None ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





         super().__init__(**snake_case__				)
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size
         UpperCAmelCase	    =			crop_pct
         UpperCAmelCase	    =			resample
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			crop_size
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase	    =			image_std if image_std is not None else IMAGENET_DEFAULT_STD



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
         if crop_pct is not None:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			int(size["""shortest_edge"""] / crop_pct				)
                elif "height" in size and "width" in size:
                       if size["height"] == size["width"]:
                              UpperCAmelCase	    =			int(size["""height"""] / crop_pct				)
                       else:
                              UpperCAmelCase	    =			(int(size["""height"""] / crop_pct				), int(size["""width"""] / crop_pct				))
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

                UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=snake_case__ ,		default_to_square=snake_case__				)
         else:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=size["""shortest_edge"""] ,		default_to_square=snake_case__				)
                elif "height" in size and "width" in size:
                       UpperCAmelCase	    =			(size["""height"""], size["""width"""])
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

         return resize(snake_case__ ,		size=snake_case__ ,		resample=snake_case__ ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__				)
         if "height" not in size or "width" not in size:
                raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
         return center_crop(snake_case__ ,		size=(size["""height"""], size["""width"""]) ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			Tuple:



         """simple docstring"""





         return rescale(snake_case__ ,		scale=snake_case__ ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         return normalize(snake_case__ ,		mean=snake_case__ ,		std=snake_case__ ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = ChannelDimension.FIRST ,		**snake_case__ ,		)			->			PIL.Image.Image:



         """simple docstring"""





         UpperCAmelCase	    =			do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase	    =			crop_pct if crop_pct is not None else self.crop_pct
         UpperCAmelCase	    =			resample if resample is not None else self.resample
         UpperCAmelCase	    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase	    =			do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase	    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase	    =			do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase	    =			image_std if image_std is not None else self.image_std

         UpperCAmelCase	    =			size if size is not None else self.size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			make_list_of_images(snake_case__				)

         if not valid_images(snake_case__				):
                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 or resample is None:
                raise ValueError("""Size and resample must be specified if do_resize is True."""				)

         if do_center_crop and crop_pct is None:
                raise ValueError("""Crop_pct 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."""				)

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

         if do_resize:
                UpperCAmelCase	    =			[self.resize(image=snake_case__ ,		size=snake_case__ ,		crop_pct=snake_case__ ,		resample=snake_case__				) for image in images]

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

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

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

         UpperCAmelCase	    =			[to_channel_dimension_format(snake_case__ ,		snake_case__				) for image in images]

         UpperCAmelCase	    =			{"""pixel_values""": images}
         return BatchFeature(data=snake_case__ ,		tensor_type=snake_case__				)
