


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase       		=    """true"""




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=82      ,       lowercase_=16      )		->     Dict:




						'''simple docstring'''







						set_seed(42      )
						__UpperCAmelCase			:							List[Any]	    =	RegressionModel()
						__UpperCAmelCase			:							Optional[int]	    =	deepcopy(lowercase_      )
						__UpperCAmelCase			:							Union[str, Any]	    =	RegressionDataset(length=lowercase_      )
						__UpperCAmelCase			:							Optional[Any]	    =	DataLoader(lowercase_      ,       batch_size=lowercase_      )
						model.to(accelerator.device      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	accelerator.prepare(lowercase_      ,       lowercase_      )
						return model, ddp_model, dataloader




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							int	    =	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__UpperCAmelCase			:							List[Any]	    =	load_dataset('''glue'''      ,       '''mrpc'''      ,       split='''validation'''      )

						def tokenize_function(lowercase_      ):
												__UpperCAmelCase			:							Any	    =	tokenizer(examples['''sentence1''']      ,       examples['''sentence2''']      ,       truncation=lowercase_      ,       max_length=lowercase_      )
												return outputs

						with accelerator.main_process_first():
												__UpperCAmelCase			:							Dict	    =	dataset.map(
												    lowercase_      ,       batched=lowercase_      ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']      ,       )

						__UpperCAmelCase			:							List[Any]	    =	tokenized_datasets.rename_column('''label'''      ,       '''labels'''      )

						def collate_fn(lowercase_      ):
												if use_longest:
																		return tokenizer.pad(lowercase_      ,       padding='''longest'''      ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase_      ,       padding='''max_length'''      ,       max_length=128      ,       return_tensors='''pt'''      )

						return DataLoader(lowercase_      ,       shuffle=lowercase_      ,       collate_fn=lowercase_      ,       batch_size=16      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	Accelerator(dispatch_batches=lowercase_      ,       split_batches=lowercase_      )
						__UpperCAmelCase			:							str	    =	get_dataloader(lowercase_      ,       not dispatch_batches      )
						__UpperCAmelCase			:							List[str]	    =	AutoModelForSequenceClassification.from_pretrained(
						    '''hf-internal-testing/mrpc-bert-base-cased'''      ,       return_dict=lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	accelerator.prepare(lowercase_      ,       lowercase_      )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	[]
						for batch in dataloader:
												__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	batch.values()
												with torch.no_grad():
																		__UpperCAmelCase			:							List[Any]	    =	model(lowercase_      )
																		__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase_      )
												targs.append(lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	torch.cat(lowercase_      ), torch.cat(lowercase_      )
						return logits, targs




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=82      ,       lowercase_=False      ,       lowercase_=False      ,       lowercase_=16      )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	get_basic_setup(lowercase_      ,       lowercase_      ,       lowercase_      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	generate_predictions(lowercase_      ,       lowercase_      ,       lowercase_      )
						assert (
						    len(lowercase_      ) == num_samples
						), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase_      )}"




def     __SCREAMING_SNAKE_CASE							(       lowercase_ = False      ,       lowercase_ = False      )		->     str:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	evaluate.load('''glue'''      ,       '''mrpc'''      )
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	get_mrpc_setup(lowercase_      ,       lowercase_      )
						# First do baseline
						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	setup['''no''']
						model.to(lowercase_      )
						model.eval()
						for batch in dataloader:
												batch.to(lowercase_      )
												with torch.inference_mode():
																		__UpperCAmelCase			:							List[str]	    =	model(**lowercase_      )
												__UpperCAmelCase			:							int	    =	outputs.logits.argmax(dim=-1      )
												metric.add_batch(predictions=lowercase_      ,       references=batch['''labels''']      )
						__UpperCAmelCase			:							Union[str, Any]	    =	metric.compute()

						# Then do distributed
						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__UpperCAmelCase			:							List[Any]	    =	model(**lowercase_      )
												__UpperCAmelCase			:							Dict	    =	outputs.logits.argmax(dim=-1      )
												__UpperCAmelCase			:							Optional[int]	    =	batch['''labels''']
												__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase_      ,       references=lowercase_      )
						__UpperCAmelCase			:							Optional[Any]	    =	metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]      ,       distributed[key]      ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"




def     __SCREAMING_SNAKE_CASE							(       )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	Accelerator(split_batches=lowercase_      ,       dispatch_batches=lowercase_      )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('''**Testing gather_for_metrics**'''      )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
																								test_mrpc(lowercase_      ,       lowercase_      )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test torch metrics**'''      )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		__UpperCAmelCase			:							List[Any]	    =	Accelerator(split_batches=lowercase_      ,       dispatch_batches=lowercase_      )
																		if accelerator.is_local_main_process:
																								print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
																		test_torch_metrics(lowercase_      ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__UpperCAmelCase			:							Dict	    =	Accelerator()
						test_torch_metrics(lowercase_      ,       512      )
						accelerator.state._reset_state()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Any:




						'''simple docstring'''







						main()


if __name__ == "__main__":
				main()





from __future__ import annotations

import requests

lowerCAmelCase       		=    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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = 1      ,       lowercase_ = "new"      ,       lowercase_ = None      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Tuple	    =	wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(lowercase_      ) - valid_terms      )      ):
												__UpperCAmelCase			:							List[Any]	    =	f"Invalid search term: {invalid_search_terms}"
												raise ValueError(lowercase_      )
						__UpperCAmelCase			:							Optional[int]	    =	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

						__UpperCAmelCase			:							List[str]	    =	response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(lowercase_      )}

						__UpperCAmelCase			:							List[Any]	    =	{}
						for id_ in range(lowercase_      ):
												__UpperCAmelCase			:							str	    =	{
												    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"""]))

