

import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


a		=16
a		=32






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__ = 1_6			)  ->     Optional[int]:
	__lowerCamelCase				:				Optional[Any]					       = AutoTokenizer.from_pretrained('bert-base-cased'			)
	__lowerCamelCase				:				int					       = load_dataset('glue'	, 'mrpc'			)

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

	def collate_fn(lowerCamelCase__			):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowerCamelCase				:				List[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":
			__lowerCamelCase				:				Optional[int]					       = 1_6
		elif accelerator.mixed_precision != "no":
			__lowerCamelCase				:				List[Any]					       = 8
		else:
			__lowerCamelCase				:				Any					       = None

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

	# Instantiate dataloaders.
	__lowerCamelCase				:				Any					       = DataLoader(
	    tokenized_datasets['train']	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__			)
	__lowerCamelCase				:				str					       = DataLoader(
	    tokenized_datasets['validation']	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__			)

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					a		=mocked_dataloaders  # noqa: F811






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'	, lowerCamelCase__			) == "1":
		__lowerCamelCase				:				Union[str, Any]					       = 2
	# Initialize accelerator
	__lowerCamelCase				:				List[Any]					       = Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision			)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowerCamelCase				:				Tuple					       = config['lr']
	__lowerCamelCase				:				List[str]					       = int(config['num_epochs']			)
	__lowerCamelCase				:				List[Any]					       = int(config['seed']			)
	__lowerCamelCase				:				int					       = int(config['batch_size']			)

	__lowerCamelCase				:				List[str]					       = evaluate.load('glue'	, 'mrpc'			)

	# New Code #
	# We now can define an inner training loop function. It should take a batch size as the only parameter,
	# and build the dataloaders in there.
	# It also gets our decorator
	@find_executable_batch_size(starting_batch_size=lowerCamelCase__			)
	def inner_training_loop(lowerCamelCase__			):
		# And now just move everything below under this function
		# We need to bring in the Accelerator object from earlier
		nonlocal accelerator
		# And reset all of its attributes that could hold onto any memory:
		accelerator.free_memory()

		# Then we can declare the model, optimizer, and everything else:
		set_seed(lowerCamelCase__			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCamelCase				:				Optional[int]					       = 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				:				Union[str, Any]					       = model.to(accelerator.device			)

		# Instantiate optimizer
		__lowerCamelCase				:				Any					       = AdamW(params=model.parameters()	, lr=lowerCamelCase__			)
		__lowerCamelCase       , __lowerCamelCase				:				str					       = get_dataloaders(lowerCamelCase__	, lowerCamelCase__			)

		# Instantiate scheduler
		__lowerCamelCase				:				int					       = get_linear_schedule_with_warmup(
		    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__			) * num_epochs)	, )

		# 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				:				Any					       = 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				:				Optional[Any]					       = model(**lowerCamelCase__			)
				__lowerCamelCase				:				Tuple					       = outputs.loss
				accelerator.backward(lowerCamelCase__			)
				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				:				List[str]					       = model(**lowerCamelCase__			)
				__lowerCamelCase				:				str					       = outputs.logits.argmax(dim=-1			)
				__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = accelerator.gather_for_metrics((predictions, batch['labels'])			)
				metric.add_batch(
				    predictions=lowerCamelCase__	, references=lowerCamelCase__	, )

			__lowerCamelCase				:				Optional[Any]					       = metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F"epoch {epoch}:"	, lowerCamelCase__			)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
	inner_training_loop()






def    SCREAMING_SNAKE_CASE__       (  )  ->     Union[str, Any]:
	__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				:				Optional[int]					       = parser.parse_args()
	__lowerCamelCase				:				Optional[int]					       = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(lowerCamelCase__	, lowerCamelCase__			)


if __name__ == "__main__":
					main()






import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






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







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
		if compression_fs_class.protocol == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_fs_class.protocol == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

	# Overwrite protocol and reload
	register_implementation(_snake_case      ,   _snake_case      ,   clobber=_snake_case   )
	with pytest.warns(_snake_case   ) as warning_info:
		importlib.reload(datasets.filesystems   )

	assert len(_snake_case   ) == 1
	assert (
	    str(warning_info[0].message   )
	    == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
	)
