




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							 =     16
_lowerCamelCase			:	Optional[int]							 =     32







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_ = 16				,       lowercase_ = "bert-base-cased"		)     -> List[str]:



       """simple docstring"""

       A__   				=					AutoTokenizer.from_pretrained(lowercase_		)
       A__   				=					load_dataset('''glue'''				,       '''mrpc'''		)

       def tokenize_function(lowercase_		):
              # max_length=None => use the model max length (it's actually the default)
              A__   				=					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
       A__   				=					datasets.map(
           lowercase_				,       batched=lowercase_				,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,       load_from_cache_file=lowercase_		)

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

       def collate_fn(lowercase_		):
              # 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(lowercase_				,       padding='''max_length'''				,       max_length=128				,       return_tensors='''pt'''		)
              return tokenizer.pad(lowercase_				,       padding='''longest'''				,       return_tensors='''pt'''		)

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

       return train_dataloader, eval_dataloader







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       A__   				=					Accelerator()

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

       set_seed(lowercase_		)
       A__				,							A__   				=					get_dataloaders(lowercase_				,       lowercase_				,       lowercase_		)

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

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

       if accelerator.state.deepspeed_plugin is not None:
              A__   				=					accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              A__   				=					1
       A__   				=					(len(lowercase_		) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A__   				=					get_linear_schedule_with_warmup(
                  optimizer=lowercase_				,       num_warmup_steps=0				,       num_training_steps=lowercase_				,       )
       else:
              A__   				=					DummyScheduler(lowercase_				,       total_num_steps=lowercase_				,       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.
       A__				,							A__				,							A__				,							A__				,							A__   				=					accelerator.prepare(
           lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)

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

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

                     overall_step += 1

              model.eval()
              A__   				=					0
              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():
                            A__   				=					model(**lowercase_		)
                     A__   				=					outputs.logits.argmax(dim=-1		)
                     # It is slightly faster to call this once, than multiple times
                     A__				,							A__   				=					accelerator.gather(
                         (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(lowercase_		) - 1:
                                   A__   				=					predictions[: len(eval_dataloader.dataset		) - samples_seen]
                                   A__   				=					references[: len(eval_dataloader.dataset		) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=lowercase_				,       references=lowercase_				,       )

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

              if best_performance < eval_metric["accuracy"]:
                     A__   				=					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(lowercase_				,       lowercase_		)







def SCREAMING_SNAKE_CASE       ( )     -> List[str]:



       """simple docstring"""

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


if __name__ == "__main__":
  main()








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)


