




'''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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCamelCase			     =					16
UpperCamelCase			     =					32
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> Optional[Any]:
     # Initialize accelerator
     if args.with_tracking:
          A:		str          =		Accelerator(
              cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				log_with='''all'''		,				project_dir=args.project_dir	)
     else:
          A:		Any          =		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision	)

     if hasattr(args.checkpointing_steps		,				'''isdigit'''	):
          if args.checkpointing_steps == "epoch":
               A:		Any          =		args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A:		Optional[int]          =		int(args.checkpointing_steps	)
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
     else:
          A:		Any          =		None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A:		str          =		config["""lr"""]
     A:		int          =		int(config['''num_epochs''']	)
     A:		Optional[int]          =		int(config['''seed''']	)
     A:		Dict          =		int(config['''batch_size''']	)

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A:		Union[str, Any]          =		os.path.split(__UpperCamelCase	)[-1].split('''.'''	)[0]
          accelerator.init_trackers(__UpperCamelCase		,				__UpperCamelCase	)

     A:		int          =		AutoTokenizer.from_pretrained('''bert-base-cased'''	)
     A:		Tuple          =		load_dataset('''glue'''		,				'''mrpc'''	)
     A:		Optional[int]          =		evaluate.load('''glue'''		,				'''mrpc'''	)

     def tokenize_function(__lowercase	):
          # max_length=None => use the model max length (it's actually the default)
          A:		List[Any]          =		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():
          A:		int          =		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
     A:		Tuple          =		tokenized_datasets.rename_column('''label'''		,				'''labels'''	)

     # If the batch size is too big we use gradient accumulation
     A:		str          =		1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A:		List[str]          =		batch_size // MAX_GPU_BATCH_SIZE
          A:		int          =		MAX_GPU_BATCH_SIZE

     def collate_fn(__lowercase	):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A:		str          =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A:		Dict          =		1_6
          elif accelerator.mixed_precision != "no":
               A:		Optional[Any]          =		8
          else:
               A:		Dict          =		None

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

     # Instantiate dataloaders.
     A:		List[str]          =		DataLoader(
         tokenized_datasets['''train''']		,				shuffle=__UpperCamelCase		,				collate_fn=__UpperCamelCase		,				batch_size=__UpperCamelCase	)
     A:		Optional[Any]          =		DataLoader(
         tokenized_datasets['''validation''']		,				shuffle=__UpperCamelCase		,				collate_fn=__UpperCamelCase		,				batch_size=__UpperCamelCase	)

     set_seed(__UpperCamelCase	)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A:		List[Any]          =		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).
     A:		Any          =		model.to(accelerator.device	)

     # Instantiate optimizer
     A:		int          =		AdamW(params=model.parameters()		,				lr=__UpperCamelCase	)

     # Instantiate scheduler
     A:		List[Any]          =		get_linear_schedule_with_warmup(
         optimizer=__UpperCamelCase		,				num_warmup_steps=1_0_0		,				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.
     A:		List[str]          =		accelerator.prepare(
         __UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase		,				__UpperCamelCase	)

     # We need to keep track of how many total steps we have iterated over
     A:		Optional[Any]          =		0
     # We also need to keep track of the stating epoch so files are named properly
     A:		Tuple          =		0

     # Potentially load in the weights and states from a previous save
     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	)
               A:		List[str]          =		os.path.basename(args.resume_from_checkpoint	)
          else:
               # Get the most recent checkpoint
               A:		Optional[Any]          =		[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
               dirs.sort(key=os.path.getctime	)
               A:		str          =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A:		Optional[int]          =		os.path.splitext(__UpperCamelCase	)[0]

          if "epoch" in training_difference:
               A:		Tuple          =		int(training_difference.replace('''epoch_'''		,				''''''	)	) + 1
               A:		List[str]          =		None
          else:
               A:		Any          =		int(training_difference.replace('''step_'''		,				''''''	)	)
               A:		Dict          =		resume_step // len(__UpperCamelCase	)
               resume_step -= starting_epoch * len(__UpperCamelCase	)

    # Now we train the model
     for epoch in range(__UpperCamelCase		,				__UpperCamelCase	):
          model.train()
          if args.with_tracking:
               A:		Optional[int]          =		0
          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
               A:		Dict          =		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
               A:		Optional[int]          =		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	)
               A:		Optional[Any]          =		model(**__UpperCamelCase	)
               A:		List[str]          =		outputs.loss
               A:		List[str]          =		loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__UpperCamelCase	)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__UpperCamelCase		,				__UpperCamelCase	):
                    A:		Optional[int]          =		F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A:		Tuple          =		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`.
               batch.to(accelerator.device	)
               with torch.no_grad():
                    A:		Optional[Any]          =		model(**__UpperCamelCase	)
               A:		Optional[Any]          =		outputs.logits.argmax(dim=-1	)
               A:		List[Any]          =		accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
               metric.add_batch(
                   predictions=__UpperCamelCase		,				references=__UpperCamelCase		,				)

          A:		int          =		metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F"""epoch {epoch}:"""		,				__UpperCamelCase	)
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__UpperCamelCase	),
                       '''epoch''': epoch,
                   }		,				step=__UpperCamelCase		,				)

          if checkpointing_steps == "epoch":
               A:		Optional[int]          =		F"""epoch_{epoch}"""
               if args.output_dir is not None:
                    A:		List[str]          =		os.path.join(args.output_dir		,				__UpperCamelCase	)
               accelerator.save_state(__UpperCamelCase	)

     if args.with_tracking:
          accelerator.end_training()
def 					SCREAMING_SNAKE_CASE(   )			-> Any:
     A:		Any          =		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(
         '''--resume_from_checkpoint'''		,				type=__UpperCamelCase		,				default=__UpperCamelCase		,				help='''If the training should continue from a checkpoint folder.'''		,				)
     parser.add_argument(
         '''--with_tracking'''		,				action='''store_true'''		,				help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,				)
     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(
         '''--project_dir'''		,				type=__UpperCamelCase		,				default='''logs'''		,				help='''Location on where to store experiment tracking logs` and relevent project information'''		,				)
     A:		List[Any]          =		parser.parse_args()
     A:		Dict          =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
     training_function(__UpperCamelCase		,				__UpperCamelCase	)


if __name__ == "__main__":
     main()










import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



