


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


lowercase__				     =		"true"






def __UpperCamelCase   (			__lowerCamelCase		: Dict      ,  __lowerCamelCase		: Any=82      ,  __lowerCamelCase		: Optional[int]=16				)      ->     Any:







     '''simple docstring'''






     set_seed(42				)
     _a						=		RegressionModel()
     _a						=		deepcopy(snake_case__				)
     _a						=		RegressionDataset(length=snake_case__				)
     _a						=		DataLoader(snake_case__      ,  batch_size=snake_case__				)
     model.to(accelerator.device				)
     _a						=		accelerator.prepare(snake_case__      ,  snake_case__				)
     return model, ddp_model, dataloader






def __UpperCamelCase   (			__lowerCamelCase		: Accelerator      ,  __lowerCamelCase		: Union[str, Any]=False				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"				)
     _a						=		load_dataset("glue"      ,  "mrpc"      ,  split="validation"				)

     def tokenize_function(__lowerCamelCase		: int				):
          _a						=		tokenizer(examples["sentence1"]      ,  examples["sentence2"]      ,  truncation=snake_case__      ,  max_length=snake_case__				)
          return outputs

     with accelerator.main_process_first():
          _a						=		dataset.map(
              snake_case__      ,  batched=snake_case__      ,  remove_columns=["idx", "sentence1", "sentence2"]      ,  )

     _a						=		tokenized_datasets.rename_column("label"      ,  "labels"				)

     def collate_fn(__lowerCamelCase		: int				):
          if use_longest:
               return tokenizer.pad(snake_case__      ,  padding="longest"      ,  return_tensors="pt"				)
          return tokenizer.pad(snake_case__      ,  padding="max_length"      ,  max_length=128      ,  return_tensors="pt"				)

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






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: List[Any]				)      ->     Any:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: List[str]				)      ->     List[str]:







     '''simple docstring'''






     _a						=		[]
     for batch in dataloader:
          _a						=		batch.values()
          with torch.no_grad():
               _a						=		model(snake_case__				)
               _a						=		accelerator.gather_for_metrics((logit, target)				)
               logits_and_targets.append((logit, target)				)
     _a						=		[], []
     for logit, targ in logits_and_targets:
          logits.append(snake_case__				)
          targs.append(snake_case__				)
     _a						=		torch.cat(snake_case__				), torch.cat(snake_case__				)
     return logits, targs






def __UpperCamelCase   (			__lowerCamelCase		: Accelerator      ,  __lowerCamelCase		: Optional[int]=82      ,  __lowerCamelCase		: Optional[Any]=False      ,  __lowerCamelCase		: List[Any]=False      ,  __lowerCamelCase		: List[Any]=16				)      ->     Optional[Any]:







     '''simple docstring'''






     _a						=		get_basic_setup(snake_case__      ,  snake_case__      ,  snake_case__				)
     _a						=		generate_predictions(snake_case__      ,  snake_case__      ,  snake_case__				)
     assert (
         len(snake_case__				) == num_samples
     ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__				)}"






def __UpperCamelCase   (			__lowerCamelCase		: bool = False      ,  __lowerCamelCase		: bool = False				)      ->     Any:







     '''simple docstring'''






     _a						=		evaluate.load("glue"      ,  "mrpc"				)
     _a						=		get_mrpc_setup(snake_case__      ,  snake_case__				)
     # First do baseline
     _a						=		setup["""no"""]
     model.to(snake_case__				)
     model.eval()
     for batch in dataloader:
          batch.to(snake_case__				)
          with torch.inference_mode():
               _a						=		model(**snake_case__				)
          _a						=		outputs.logits.argmax(dim=-1				)
          metric.add_batch(predictions=snake_case__      ,  references=batch["labels"]				)
     _a						=		metric.compute()

     # Then do distributed
     _a						=		setup["""ddp"""]
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               _a						=		model(**snake_case__				)
          _a						=		outputs.logits.argmax(dim=-1				)
          _a						=		batch["""labels"""]
          _a						=		accelerator.gather_for_metrics((preds, references)				)
          metric.add_batch(predictions=snake_case__      ,  references=snake_case__				)
     _a						=		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 __UpperCamelCase   (			)      ->     int:







     '''simple docstring'''






     _a						=		Accelerator(split_batches=snake_case__      ,  dispatch_batches=snake_case__				)
     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(snake_case__      ,  snake_case__				)
                    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]:
               _a						=		Accelerator(split_batches=snake_case__      ,  dispatch_batches=snake_case__				)
               if accelerator.is_local_main_process:
                    print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"				)
               test_torch_metrics(snake_case__      ,  99				)
               accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print("**Test last batch is not dropped when perfectly divisible**"				)
     _a						=		Accelerator()
     test_torch_metrics(snake_case__      ,  512				)
     accelerator.state._reset_state()






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]				)      ->     Union[str, Any]:







     '''simple docstring'''






     main()


if __name__ == "__main__":
   main()





'''simple docstring'''

from __future__ import annotations

import requests






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     dict:







     '''simple docstring'''






     _a						=		F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
     return requests.get(__lowerCamelCase				).json()






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     list[dict]:







     '''simple docstring'''






     _a						=		"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _a						=		requests.get(__lowerCamelCase				).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase				) for story_id in story_ids]






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     str:







     '''simple docstring'''






     _a						=		hackernews_top_stories(__lowerCamelCase				)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase				) for story in stories				)


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())

