"""simple docstring"""







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


__SCREAMING_SNAKE_CASE    	=16
__SCREAMING_SNAKE_CASE    	=32



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Accelerator     ,			__SCREAMING_SNAKE_CASE							:     int = 16     ,			__SCREAMING_SNAKE_CASE							:     str = "bert-base-cased"      ):
     lowercase_	:				Dict													=	AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[int]													=	load_dataset('glue'     ,			'mrpc'      )

     def tokenize_function(__SCREAMING_SNAKE_CASE							:     int      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_	:				Optional[Any]													=	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
     lowercase_	:				Union[str, Any]													=	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
     lowercase_	:				Union[str, Any]													=	tokenized_datasets.rename_column('label'     ,			'labels'      )

     def collate_fn(__SCREAMING_SNAKE_CASE							:     int      ):
          # 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=1_28     ,			return_tensors='pt'      )
          return tokenizer.pad(__SCREAMING_SNAKE_CASE     ,			padding='longest'     ,			return_tensors='pt'      )

     # Instantiate dataloaders.
     lowercase_	:				Optional[Any]													=	DataLoader(
         tokenized_datasets['train']     ,			shuffle=__SCREAMING_SNAKE_CASE     ,			collate_fn=__SCREAMING_SNAKE_CASE     ,			batch_size=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Any													=	DataLoader(
         tokenized_datasets['validation']     ,			shuffle=__SCREAMING_SNAKE_CASE     ,			collate_fn=__SCREAMING_SNAKE_CASE     ,			batch_size=__SCREAMING_SNAKE_CASE      )

     return train_dataloader, eval_dataloader



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     model.eval()
     lowercase_	:				List[str]													=	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():
               lowercase_	:				Optional[int]													=	model(**__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[str]													=	outputs.logits.argmax(dim=-1      )
          # It is slightly faster to call this once, than multiple times
          lowercase_							,  lowercase_	:				List[Any]													=	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:
                    lowercase_	:				Union[str, Any]													=	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                    lowercase_	:				Optional[Any]													=	references[: len(eval_dataloader.dataset      ) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=__SCREAMING_SNAKE_CASE     ,			references=__SCREAMING_SNAKE_CASE     ,			)

     lowercase_	:				Optional[Any]													=	metric.compute()
     return eval_metric["accuracy"]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
     # Initialize accelerator
     lowercase_	:				Optional[int]													=	Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_	:				str													=	config['lr']
     lowercase_	:				Optional[Any]													=	int(config['num_epochs']      )
     lowercase_	:				Tuple													=	int(config['seed']      )
     lowercase_	:				Tuple													=	int(config['batch_size']      )
     lowercase_	:				Any													=	args.model_name_or_path

     set_seed(__SCREAMING_SNAKE_CASE      )
     lowercase_							,  lowercase_	:				Optional[Any]													=	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)
     lowercase_	:				Any													=	AutoModelForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE     ,			return_dict=__SCREAMING_SNAKE_CASE      )

     # Instantiate optimizer
     lowercase_	:				Optional[Any]													=	(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_	:				Optional[int]													=	optimizer_cls(params=model.parameters()     ,			lr=__SCREAMING_SNAKE_CASE      )

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_	:				Tuple													=	accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          lowercase_	:				List[Any]													=	1
     lowercase_	:				List[Any]													=	(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
     ):
          lowercase_	:				Dict													=	get_linear_schedule_with_warmup(
              optimizer=__SCREAMING_SNAKE_CASE     ,			num_warmup_steps=0     ,			num_training_steps=__SCREAMING_SNAKE_CASE     ,			)
     else:
          lowercase_	:				int													=	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.
     lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[Any]													=	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
     lowercase_	:				Optional[int]													=	0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_	:				Optional[Any]													=	0
     lowercase_	:				Tuple													=	evaluate.load('glue'     ,			'mrpc'      )
     lowercase_	:				List[str]													=	num_epochs

     if args.partial_train_epoch is not None:
          lowercase_	:				Any													=	args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint      )
          lowercase_	:				int													=	args.resume_from_checkpoint.split('epoch_'      )[1]
          lowercase_	:				List[Any]													=	''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          lowercase_	:				Optional[int]													=	int(__SCREAMING_SNAKE_CASE      ) + 1
          lowercase_	:				Optional[Any]													=	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:
               lowercase_	:				List[str]													=	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
     lowercase_	:				Union[str, Any]													=	{}
     for epoch in range(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
          model.train()
          for step, batch in enumerate(__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				List[str]													=	model(**__SCREAMING_SNAKE_CASE      )
               lowercase_	:				Optional[int]													=	outputs.loss
               lowercase_	:				Any													=	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
          lowercase_	:				Optional[int]													=	F'''epoch_{epoch}'''
          lowercase_	:				Any													=	os.path.join(args.output_dir     ,			__SCREAMING_SNAKE_CASE      )
          accelerator.save_state(__SCREAMING_SNAKE_CASE      )
          lowercase_	:				Union[str, Any]													=	evaluation_loop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				Union[str, Any]													=	accuracy
          lowercase_	:				Union[str, Any]													=	lr_scheduler.get_lr()[0]
          lowercase_	:				str													=	optimizer.param_groups[0]['lr']
          lowercase_	:				Dict													=	epoch
          lowercase_	:				Tuple													=	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 			lowercase__(       ):
     lowercase_	:				Any													=	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.'     ,			)
     lowercase_	:				Dict													=	parser.parse_args()
     lowercase_	:				Dict													=	{'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()


"""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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  ['pixel_values']
   def __init__(			self     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 0.9     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1 / 255     ,__UpperCamelCase = True     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      None:


        '''simple docstring'''


        super().__init__(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	size if size is not None else {'shortest_edge': 224}
        lowercase_	:				Union[str, Any]													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else {'height': 224, 'width': 224}
        lowercase_	:				Optional[int]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				List[str]													=	do_resize
        lowercase_	:				List[Any]													=	size
        lowercase_	:				int													=	crop_pct
        lowercase_	:				Dict													=	resample
        lowercase_	:				List[str]													=	do_center_crop
        lowercase_	:				Union[str, Any]													=	crop_size
        lowercase_	:				List[Any]													=	do_rescale
        lowercase_	:				Tuple													=	rescale_factor
        lowercase_	:				Tuple													=	do_normalize
        lowercase_	:				Union[str, Any]													=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_	:				int													=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = PILImageResampling.BICUBIC     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				Any													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        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:
                  lowercase_	:				Union[str, Any]													=	int(size['shortest_edge'] / crop_pct			)
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_	:				Tuple													=	int(size['height'] / crop_pct			)
                  else:
                       lowercase_	:				Dict													=	(int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

             lowercase_	:				int													=	get_resize_output_image_size(__UpperCamelCase     ,size=__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        else:
             if "shortest_edge" in size:
                  lowercase_	:				Optional[int]													=	get_resize_output_image_size(__UpperCamelCase     ,size=size['shortest_edge']     ,default_to_square=__UpperCamelCase			)
             elif "height" in size and "width" in size:
                  lowercase_	:				Dict													=	(size['height'], size['width'])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase			)			)

        return resize(__UpperCamelCase     ,size=__UpperCamelCase     ,resample=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	get_size_dict(__UpperCamelCase			)
        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(__UpperCamelCase     ,size=(size['height'], size['width'])     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      str:


        '''simple docstring'''


        return rescale(__UpperCamelCase     ,scale=__UpperCamelCase     ,data_format=__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,**__UpperCamelCase     ,)	->      np.ndarray:


        '''simple docstring'''


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







   def 			_UpperCAmelCase   (			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     ,)	->      PIL.Image.Image:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	do_resize if do_resize is not None else self.do_resize
        lowercase_	:				Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
        lowercase_	:				List[str]													=	resample if resample is not None else self.resample
        lowercase_	:				str													=	do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_	:				Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
        lowercase_	:				Union[str, Any]													=	rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_	:				str													=	do_normalize if do_normalize is not None else self.do_normalize
        lowercase_	:				str													=	image_mean if image_mean is not None else self.image_mean
        lowercase_	:				Tuple													=	image_std if image_std is not None else self.image_std

        lowercase_	:				Optional[Any]													=	size if size is not None else self.size
        lowercase_	:				Tuple													=	get_size_dict(__UpperCamelCase     ,default_to_square=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	crop_size if crop_size is not None else self.crop_size
        lowercase_	:				List[str]													=	get_size_dict(__UpperCamelCase     ,param_name='crop_size'			)

        lowercase_	:				str													=	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 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.
        lowercase_	:				Optional[Any]													=	[to_numpy_array(__UpperCamelCase			) for image in images]

        if do_resize:
             lowercase_	:				str													=	[self.resize(image=__UpperCamelCase     ,size=__UpperCamelCase     ,crop_pct=__UpperCamelCase     ,resample=__UpperCamelCase			) for image in images]

        if do_center_crop:
             lowercase_	:				str													=	[self.center_crop(image=__UpperCamelCase     ,size=__UpperCamelCase			) for image in images]

        if do_rescale:
             lowercase_	:				Any													=	[self.rescale(image=__UpperCamelCase     ,scale=__UpperCamelCase			) for image in images]

        if do_normalize:
             lowercase_	:				int													=	[self.normalize(image=__UpperCamelCase     ,mean=__UpperCamelCase     ,std=__UpperCamelCase			) for image in images]

        lowercase_	:				Dict													=	[to_channel_dimension_format(__UpperCamelCase     ,__UpperCamelCase			) for image in images]

        lowercase_	:				Any													=	{'pixel_values': images}
        return BatchFeature(data=__UpperCamelCase     ,tensor_type=__UpperCamelCase			)

