





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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
#
########################################################################


_snake_case    :       Optional[int]		       =    16
_snake_case    :       int		       =    32

def     a_       (					lowerCAmelCase_ :     Accelerator,      lowerCAmelCase_ :     DatasetDict,      lowerCAmelCase_ :     List[int],      lowerCAmelCase_ :     List[int],      lowerCAmelCase_ :     int = 16      ):
   __lowerCAmelCase				      =      AutoTokenizer.from_pretrained('bert-base-cased'      )
   __lowerCAmelCase				      =      DatasetDict(
       {
           'train': dataset['train'].select(lowerCAmelCase_      ),
           'validation': dataset['train'].select(lowerCAmelCase_      ),
           'test': dataset['validation'],
       }      )

   def tokenize_function(lowerCAmelCase_ :     Optional[Any]      ):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCAmelCase				      =      tokenizer(examples['sentence1'],      examples['sentence2'],      truncation=lowerCAmelCase_,      max_length=lowerCAmelCase_      )
      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():
      __lowerCAmelCase				      =      datasets.map(
          lowerCAmelCase_,      batched=lowerCAmelCase_,      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
   __lowerCAmelCase				      =      tokenized_datasets.rename_column('label',      'labels'      )

   def collate_fn(lowerCAmelCase_ :     str      ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      __lowerCAmelCase				      =      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":
         __lowerCAmelCase				      =      16
      elif accelerator.mixed_precision != "no":
         __lowerCAmelCase				      =      8
      else:
         __lowerCAmelCase				      =      None

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

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

   __lowerCAmelCase				      =      DataLoader(
       tokenized_datasets['test'],      shuffle=lowerCAmelCase_,      collate_fn=lowerCAmelCase_,      batch_size=lowerCAmelCase_      )

   return train_dataloader, eval_dataloader, test_dataloader

def     a_       (					lowerCAmelCase_ :     Tuple,      lowerCAmelCase_ :     Any      ):
   # New Code #
   __lowerCAmelCase				      =      []
   # Download the dataset
   __lowerCAmelCase				      =      load_dataset('glue',      'mrpc'      )
   # Create our splits
   __lowerCAmelCase				      =      StratifiedKFold(n_splits=int(args.num_folds      )      )
   # Initialize accelerator
   __lowerCAmelCase				      =      Accelerator(cpu=args.cpu,      mixed_precision=args.mixed_precision      )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase				      =      config['lr']
   __lowerCAmelCase				      =      int(config['num_epochs']      )
   __lowerCAmelCase				      =      int(config['seed']      )
   __lowerCAmelCase				      =      int(config['batch_size']      )

   __lowerCAmelCase				      =      evaluate.load('glue',      'mrpc'      )

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

   set_seed(lowerCAmelCase_      )

   # New Code #
   # Create our folds:
   __lowerCAmelCase				      =      kfold.split(np.zeros(datasets['train'].num_rows      ),      datasets['train']['label']      )
   __lowerCAmelCase				      =      []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_      ):
      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      get_fold_dataloaders(
          lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      __lowerCAmelCase				      =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased',      return_dict=lowerCAmelCase_      )

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

      # Instantiate optimizer
      __lowerCAmelCase				      =      AdamW(params=model.parameters(),      lr=lowerCAmelCase_      )

      # Instantiate scheduler
      __lowerCAmelCase				      =      get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_,      num_warmup_steps=100,      num_training_steps=(len(lowerCAmelCase_      ) * 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.
      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.prepare(
          lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_,      lowerCAmelCase_      )

      # Now we train the model
      for epoch in range(lowerCAmelCase_      ):
         model.train()
         for step, batch in enumerate(lowerCAmelCase_      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
            __lowerCAmelCase				      =      outputs.loss
            __lowerCAmelCase				      =      loss / gradient_accumulation_steps
            accelerator.backward(lowerCAmelCase_      )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

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

         __lowerCAmelCase				      =      metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:""",      lowerCAmelCase_      )

      # New Code #
      # We also run predictions on the test set at the very end
      __lowerCAmelCase				      =      []
      for step, batch in enumerate(lowerCAmelCase_      ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device      )
         with torch.no_grad():
            __lowerCAmelCase				      =      model(**lowerCAmelCase_      )
         __lowerCAmelCase				      =      outputs.logits
         __lowerCAmelCase      ,      __lowerCAmelCase				      =      accelerator.gather_for_metrics((predictions, batch['labels'])      )
         fold_predictions.append(predictions.cpu()      )
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu()      )
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(lowerCAmelCase_,      dim=0      )      )
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   __lowerCAmelCase				      =      torch.cat(lowerCAmelCase_,      dim=0      )
   __lowerCAmelCase				      =      torch.stack(lowerCAmelCase_,      dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
   __lowerCAmelCase				      =      metric.compute(predictions=lowerCAmelCase_,      references=lowerCAmelCase_      )
   accelerator.print('Average test metrics from all folds:',      lowerCAmelCase_      )

def     a_       (					):
   __lowerCAmelCase				      =      argparse.ArgumentParser(description='Simple example of training script.'      )
   parser.add_argument(
       '--mixed_precision',      type=lowerCAmelCase_,      default=lowerCAmelCase_,      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.'      )
   # New Code #
   parser.add_argument('--num_folds',      type=lowerCAmelCase_,      default=3,      help='The number of splits to perform across the dataset'      )
   __lowerCAmelCase				      =      parser.parse_args()
   __lowerCAmelCase				      =      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCAmelCase_,      lowerCAmelCase_      )


if __name__ == "__main__":
     main()







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__A								=       logging.get_logger(__name__)  # pylint: disable=invalid-name







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			Tuple:





				"""simple docstring"""


				if not path:
								return "pipe"

				for ext in PipelineDataFormat.SUPPORTED_FORMATS:
								if path.endswith(UpperCamelCase__ ):
												return ext

				raise Exception(
				    F"""Unable to determine file format from file extension {path}. """
				    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: Dict )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       pipeline(
				    task=args.task					,	model=args.model if args.model else None					,	config=args.config					,	tokenizer=args.tokenizer					,	device=args.device					,	)
				__lowerCamelCase									=       try_infer_format_from_ext(args.input ) if args.format == 'infer' else args.format
				__lowerCamelCase									=       PipelineDataFormat.from_str(
				    format=UpperCamelCase__					,	output_path=args.output					,	input_path=args.input					,	column=args.column if args.column else nlp.default_input_names					,	overwrite=args.overwrite					,	)
				return RunCommand(UpperCamelCase__					,	UpperCamelCase__ )







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       nlp
							__lowerCamelCase									=       reader


			@staticmethod
			def  lowercase_       ( lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       parser.add_parser('run'     ,		help='Run a pipeline through the CLI'				)
							run_parser.add_argument('--task'     ,		choices=get_supported_tasks()     ,		help='Task to run'				)
							run_parser.add_argument('--input'     ,		type=lowerCamelCase__     ,		help='Path to the file to use for inference'				)
							run_parser.add_argument('--output'     ,		type=lowerCamelCase__     ,		help='Path to the file that will be used post to write results.'				)
							run_parser.add_argument('--model'     ,		type=lowerCamelCase__     ,		help='Name or path to the model to instantiate.'				)
							run_parser.add_argument('--config'     ,		type=lowerCamelCase__     ,		help='Name or path to the model\'s config to instantiate.'				)
							run_parser.add_argument(
							    '--tokenizer'     ,		type=lowerCamelCase__     ,		help='Name of the tokenizer to use. (default: same as the model name)'				)
							run_parser.add_argument(
							    '--column'     ,		type=lowerCamelCase__     ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'     ,		)
							run_parser.add_argument(
							    '--format'     ,		type=lowerCamelCase__     ,		default='infer'     ,		choices=PipelineDataFormat.SUPPORTED_FORMATS     ,		help='Input format to read from'     ,		)
							run_parser.add_argument(
							    '--device'     ,		type=lowerCamelCase__     ,		default=-1     ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'     ,		)
							run_parser.add_argument('--overwrite'     ,		action='store_true'     ,		help='Allow overwriting the output file.'				)
							run_parser.set_defaults(func=lowerCamelCase__				)






			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self._nlp, []

							for entry in self._reader:
											__lowerCamelCase									=       nlp(**lowerCamelCase__				) if self._reader.is_multi_columns else nlp(lowerCamelCase__				)
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															outputs.append(lowerCamelCase__				)
											else:
															outputs += output

        # Saving data
							if self._nlp.binary_output:
											__lowerCamelCase									=       self._reader.save_binary(lowerCamelCase__				)
											logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""				)
							else:
											self._reader.save(lowerCamelCase__				)


