

'''simple docstring'''

import argparse

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)
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE							:				Dict     	=			16
SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			32




def 							_UpperCamelCase				(   lowerCAmelCase__:					Accelerator		,lowerCAmelCase__:					int = 16   )      -> str:
							SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained('bert-base-cased'   )
							SCREAMING_SNAKE_CASE_				=				load_dataset('glue'		,'mrpc'   )

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

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

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

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_				=				DataLoader(
							    tokenized_datasets['train']		,shuffle=__lowerCAmelCase		,collate_fn=__lowerCAmelCase		,batch_size=__lowerCAmelCase		,drop_last=__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_				=				DataLoader(
							    tokenized_datasets['validation']		,shuffle=__lowerCAmelCase		,collate_fn=__lowerCAmelCase		,batch_size=__lowerCAmelCase		,drop_last=(accelerator.mixed_precision == 'fp8')		,)

							return train_dataloader, eval_dataloader




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Optional[int]   )      -> int:
							# Initialize accelerator
							SCREAMING_SNAKE_CASE_				=				Accelerator(cpu=args.cpu		,mixed_precision=args.mixed_precision   )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE_				=				config["""lr"""]
							SCREAMING_SNAKE_CASE_				=				int(config['num_epochs']   )
							SCREAMING_SNAKE_CASE_				=				int(config['seed']   )
							SCREAMING_SNAKE_CASE_				=				int(config['batch_size']   )

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

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

							set_seed(__lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_				=				get_dataloaders(__lowerCAmelCase		,__lowerCAmelCase   )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_				=				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).
							SCREAMING_SNAKE_CASE_				=				model.to(accelerator.device   )
							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_				=				AdamW(params=model.parameters()		,lr=__lowerCAmelCase   )

							# Instantiate scheduler
							SCREAMING_SNAKE_CASE_				=				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.

							SCREAMING_SNAKE_CASE_				=				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   )
																					SCREAMING_SNAKE_CASE_				=				model(**__lowerCAmelCase   )
																					SCREAMING_SNAKE_CASE_				=				outputs.loss
																					SCREAMING_SNAKE_CASE_				=				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():
																												SCREAMING_SNAKE_CASE_				=				model(**__lowerCAmelCase   )
																					SCREAMING_SNAKE_CASE_				=				outputs.logits.argmax(dim=-1   )
																					SCREAMING_SNAKE_CASE_				=				accelerator.gather_for_metrics((predictions, batch['labels'])   )
																					metric.add_batch(
																					    predictions=__lowerCAmelCase		,references=__lowerCAmelCase		,)

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




def 							_UpperCamelCase				(   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				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.'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(__lowerCAmelCase		,__lowerCAmelCase   )


if __name__ == "__main__":
							main()













import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



