
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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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
#
########################################################################


_lowerCamelCase	:    Tuple          =      16
_lowerCamelCase	:    Any          =      32






def 	a_		(	__lowercase       :     Accelerator	, __lowercase       :     int = 16			)			->  Optional[int]:






			"""simple docstring"""




			_snake_case          =      AutoTokenizer.from_pretrained('bert-base-cased'			)
			_snake_case          =      load_dataset('glue'	, 'mrpc'			)

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

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

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

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

			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

			_lowerCamelCase	:    Tuple          =      mocked_dataloaders  # noqa: F811






def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Union[str, Any]			)			->  List[str]:






			"""simple docstring"""




			if os.environ.get('TESTING_MOCKED_DATALOADERS'	, __lowercase			) == "1":
						_snake_case          =      2
			# New Code #
			_snake_case          =      int(args.gradient_accumulation_steps			)
			_snake_case          =      int(args.local_sgd_steps			)
			# Initialize accelerator
			_snake_case          =      Accelerator(
			    cpu=args.cpu	, mixed_precision=args.mixed_precision	, gradient_accumulation_steps=__lowercase			)
			if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
						raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			_snake_case          =      config['lr']
			_snake_case          =      int(config['num_epochs']			)
			_snake_case          =      int(config['seed']			)
			_snake_case          =      int(config['batch_size']			)

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

			set_seed(__lowercase			)
			_snake_case     , _snake_case          =      get_dataloaders(__lowercase	, __lowercase			)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			_snake_case          =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	, return_dict=__lowercase			)

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

			# Instantiate optimizer
			_snake_case          =      AdamW(params=model.parameters()	, lr=__lowercase			)

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

			# Now we train the model
			for epoch in range(__lowercase			):
						model.train()
						with LocalSGD(
						    accelerator=__lowercase	, model=__lowercase	, local_sgd_steps=__lowercase	, enabled=local_sgd_steps is not None			) as local_sgd:
									for step, batch in enumerate(__lowercase			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device			)
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(__lowercase			):
															_snake_case          =      model(**__lowercase			)
															_snake_case          =      output.loss
															accelerator.backward(__lowercase			)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

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

						_snake_case          =      metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''	, __lowercase			)






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






			"""simple docstring"""




			_snake_case          =      argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument(
			    '--mixed_precision'	, type=__lowercase	, default=__lowercase	, 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.'	, )
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'	, type=__lowercase	, default=1	, help='The number of minibatches to be ran before gradients are accumulated.'	, )
			parser.add_argument(
			    '--local_sgd_steps'	, type=__lowercase	, default=8	, help='Number of local SGD steps or None to disable local SGD'			)
			parser.add_argument('--cpu'	, action='store_true'	, help='If passed, will train on the CPU.'			)
			_snake_case          =      parser.parse_args()
			_snake_case          =      {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__lowercase	, __lowercase			)


if __name__ == "__main__":
			main()

from __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_snake_case          =      requests.get(
			    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''	, headers={'User-agent': 'A random string'}	, )
			if response.status_code == 429:
						raise requests.HTTPError

			_snake_case          =      response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_snake_case          =      {
						    item: data['data']['children'][id_]['data'][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
			# If you get Error 429, that means you are rate limited.Try after some time
			print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))