


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class       _UpperCamelCase    :

    """simple docstring"""







    def __init__(						self				,   a__=2				,   a__=3				,   a__=64				,   a__=None   )	->				Optional[Any]:
     A								=				np.random.default_rng(a__   )
     A								=				length
     A								=				rng.normal(size=(length,)   ).astype(np.floataa   )
     A								=				a * self.x + b + rng.normal(scale=0.1				,   size=(length,)   ).astype(np.floataa   )

    def __len__(						self   )	->				Optional[Any]:
     return self.length

    def __getitem__(						self				,   a__   )	->				Optional[int]:
     return {"x": self.x[i], "y": self.y[i]}






class       _UpperCamelCase    (					torch.nn.Module				):

    """simple docstring"""







    def __init__(						self				,   a__=0				,   a__=0				,   a__=False   )	->				List[Any]:
     super().__init__()
     A								=				torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
     A								=				torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
     A								=				True

    def      _UpperCAmelCase					(						self				,   a__=None   )	->				List[Any]:
     if self.first_batch:
      print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'   )
      A								=				False
     return x * self.a[0] + self.b[0]






class       _UpperCamelCase    (					torch.nn.Module				):

    """simple docstring"""







    def __init__(						self				,   a__=0				,   a__=0				,   a__=False   )	->				List[str]:
     super().__init__()
     A								=				torch.nn.Parameter(torch.tensor(a__   ).float()   )
     A								=				torch.nn.Parameter(torch.tensor(a__   ).float()   )
     A								=				True

    def      _UpperCAmelCase					(						self				,   a__=None   )	->				int:
     if self.first_batch:
      print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'   )
      A								=				False
     return x * self.a + self.b





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int = 16						) ->   Any:





 """simple docstring"""







 from datasets import load_dataset
 from transformers import AutoTokenizer

 A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
 A								=				{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 A								=				load_dataset("""csv"""							,					data_files=UpperCamelCase__						)
 A								=				datasets["""train"""].unique("""label"""						)

 A								=				{v: i for i, v in enumerate(UpperCamelCase__						)}

 def tokenize_function(UpperCamelCase__:     Optional[Any]						):
  # max_length=None => use the model max length (it's actually the default)
  A								=				tokenizer(
      examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=UpperCamelCase__							,					max_length=UpperCamelCase__							,					padding="""max_length"""						)
  if "label" in examples:
   A								=				[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 A								=				datasets.map(
     UpperCamelCase__							,					batched=UpperCamelCase__							,					remove_columns=["""sentence1""", """sentence2""", """label"""]							,					)

 def collate_fn(UpperCamelCase__:     str						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(UpperCamelCase__							,					padding="""max_length"""							,					max_length=1_28							,					return_tensors="""pt"""						)
  return tokenizer.pad(UpperCamelCase__							,					padding="""longest"""							,					return_tensors="""pt"""						)

 # Instantiate dataloaders.
 A								=				DataLoader(tokenized_datasets["""train"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=2						)
 A								=				DataLoader(tokenized_datasets["""validation"""]							,					shuffle=UpperCamelCase__							,					collate_fn=UpperCamelCase__							,					batch_size=1						)

 return train_dataloader, eval_dataloader







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







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


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



