



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
#
########################################################################


__snake_case				:		List[Any]										=1_6
__snake_case				:		Any										=3_2






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Accelerator       ,lowerCamelCase_		:		int = 16):





				'''simple docstring'''

				lowerCAmelCase__     :				List[Any]												=     AutoTokenizer.from_pretrained('''bert-base-cased''')
				lowerCAmelCase__     :				Optional[Any]												=     load_dataset('''glue'''       ,'''mrpc''')

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

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

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

				# Instantiate dataloaders.
				lowerCAmelCase__     :				List[str]												=     DataLoader(
				    tokenized_datasets['''train''']       ,shuffle=lowerCAmelCase_       ,collate_fn=lowerCAmelCase_       ,batch_size=lowerCAmelCase_       ,drop_last=lowerCAmelCase_)
				lowerCAmelCase__     :				int												=     DataLoader(
				    tokenized_datasets['''validation''']       ,shuffle=lowerCAmelCase_       ,collate_fn=lowerCAmelCase_       ,batch_size=lowerCAmelCase_       ,drop_last=(accelerator.mixed_precision == '''fp8''')       ,)

				return train_dataloader, eval_dataloader






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Any       ,lowerCamelCase_		:		int):





				'''simple docstring'''

				lowerCAmelCase__     :				str												=     Accelerator(cpu=args.cpu       ,mixed_precision=args.mixed_precision)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase__     :				Union[str, Any]												=     config['''lr''']
				lowerCAmelCase__     :				Tuple												=     int(config['''num_epochs'''])
				lowerCAmelCase__     :				List[Any]												=     int(config['''seed'''])
				lowerCAmelCase__     :				str												=     int(config['''batch_size'''])

				lowerCAmelCase__     :				Dict												=     evaluate.load('''glue'''       ,'''mrpc''')

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

				set_seed(lowerCAmelCase_)
				lowerCAmelCase__						,		lowerCAmelCase__     :				List[str]												=     get_dataloaders(lowerCAmelCase_       ,lowerCAmelCase_)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase__     :				Optional[Any]												=     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__     :				List[str]												=     model.to(accelerator.device)
				# Instantiate optimizer
				lowerCAmelCase__     :				Tuple												=     AdamW(params=model.parameters()       ,lr=lowerCAmelCase_)

				# Instantiate scheduler
				lowerCAmelCase__     :				Any												=     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__     :				str												=     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__     :				Tuple												=     model(**lowerCAmelCase_)
												lowerCAmelCase__     :				Optional[int]												=     outputs.loss
												lowerCAmelCase__     :				Any												=     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__     :				int												=     model(**lowerCAmelCase_)
												lowerCAmelCase__     :				Optional[int]												=     outputs.logits.argmax(dim=-1)
												lowerCAmelCase__						,		lowerCAmelCase__     :				Tuple												=     accelerator.gather_for_metrics((predictions, batch['''labels''']))
												metric.add_batch(
												    predictions=lowerCAmelCase_       ,references=lowerCAmelCase_       ,)

								lowerCAmelCase__     :				Union[str, Any]												=     metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""       ,lowerCAmelCase_)






def 	lowerCAmelCase__		(    ):





				'''simple docstring'''

				lowerCAmelCase__     :				Union[str, Any]												=     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.''')
				lowerCAmelCase__     :				Union[str, Any]												=     parser.parse_args()
				lowerCAmelCase__     :				Dict												=     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCAmelCase_       ,lowerCAmelCase_)


if __name__ == "__main__":
							main()








import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs

@pytest.mark.parametrize(
    'kwargs, expected',	[
        ({'num_shards': 0, 'max_num_jobs': 1}, []),
        ({'num_shards': 10, 'max_num_jobs': 1}, [range(10	)]),
        ({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_,	i + 1	) for i in range(10	)]),
        ({'num_shards': 1, 'max_num_jobs': 10}, [range(1	)]),
        ({'num_shards': 10, 'max_num_jobs': 3}, [range(0,	4	), range(4,	7	), range(7,	10	)]),
        ({'num_shards': 3, 'max_num_jobs': 10}, [range(0,	1	), range(1,	2	), range(2,	3	)]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Any	):
   __lowerCAmelCase								=	_distribute_shards(**lowerCAmelCase_	)
   assert out == expected

@pytest.mark.parametrize(
    'gen_kwargs, max_num_jobs, expected',	[
        ({'foo': 0}, 10, [{'foo': 0}]),
        ({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
        ({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
        ({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
        ({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
    ],	)
def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	_split_gen_kwargs(lowerCAmelCase_,	lowerCAmelCase_	)
   assert out == expected




@pytest.mark.parametrize(
    'gen_kwargs, expected',	[
        ({'foo': 0}, 1),
        ({'shards': [0]}, 1),
        ({'shards': [0, 1, 2, 3]}, 4),
        ({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
        ({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
        ({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
    ],	)
def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Any	):
   if expected is RuntimeError:
      with pytest.raises(lowerCAmelCase_	):
         _number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
   else:
      __lowerCAmelCase								=	_number_of_shards_in_gen_kwargs(lowerCAmelCase_	)
      assert out == expected

