



'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase=2	,__lowerCamelCase=3	,__lowerCamelCase=64	,__lowerCamelCase=None				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  np.random.default_rng(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  length
						lowerCAmelCase__  :       List[str]				=  rng.normal(size=(length,)				).astype(np.floataa				)
						lowerCAmelCase__  :       Optional[int]				=  a * self.x + b + rng.normal(scale=0.1	,size=(length,)				).astype(np.floataa				)



		def __len__(self				)  ->    int:

						"""simple docstring"""






						return self.length



		def __getitem__(self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						return {"x": self.x[i], "y": self.y[i]}



class  lowerCamelCase__    (							torch.nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase=0	,__lowerCamelCase=0	,__lowerCamelCase=False				)  ->    List[Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       str				=  torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
						lowerCAmelCase__  :       int				=  torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
						lowerCAmelCase__  :       List[str]				=  True



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=None				)  ->    Tuple:

						"""simple docstring"""






						if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""				)
										lowerCAmelCase__  :       Optional[int]				=  False
						return x * self.a[0] + self.b[0]



class  lowerCamelCase__    (							torch.nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase=0	,__lowerCamelCase=0	,__lowerCamelCase=False				)  ->    List[str]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       Dict				=  torch.nn.Parameter(torch.tensor(__lowerCamelCase				).float()				)
						lowerCAmelCase__  :       Any				=  torch.nn.Parameter(torch.tensor(__lowerCamelCase				).float()				)
						lowerCAmelCase__  :       Dict				=  True



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=None				)  ->    List[str]:

						"""simple docstring"""






						if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""				)
										lowerCAmelCase__  :       Dict				=  False
						return x * self.a + self.b






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	int = 16):





				'''simple docstring'''

				from datasets import load_dataset
				from transformers import AutoTokenizer

				lowerCAmelCase__  :       Optional[Any]				=  AutoTokenizer.from_pretrained('''bert-base-cased''')
				lowerCAmelCase__  :       str				=  {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
				lowerCAmelCase__  :       Union[str, Any]				=  load_dataset('''csv''' ,data_files=lowerCamelCase_)
				lowerCAmelCase__  :       Union[str, Any]				=  datasets['''train'''].unique('''label''')

				lowerCAmelCase__  :       int				=  {v: i for i, v in enumerate(lowerCamelCase_)}

				def tokenize_function(lowerCamelCase_	:	Any):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__  :       int				=  tokenizer(
								    examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=lowerCamelCase_ ,max_length=lowerCamelCase_ ,padding='''max_length''')
								if "label" in examples:
												lowerCAmelCase__  :       Optional[int]				=  [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
				lowerCAmelCase__  :       Optional[int]				=  datasets.map(
				    lowerCamelCase_ ,batched=lowerCamelCase_ ,remove_columns=['''sentence1''', '''sentence2''', '''label'''] ,)

				def collate_fn(lowerCamelCase_	:	List[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(lowerCamelCase_ ,padding='''max_length''' ,max_length=128 ,return_tensors='''pt''')
								return tokenizer.pad(lowerCamelCase_ ,padding='''longest''' ,return_tensors='''pt''')

				# Instantiate dataloaders.
				lowerCAmelCase__  :       int				=  DataLoader(tokenized_datasets['''train'''] ,shuffle=lowerCamelCase_ ,collate_fn=lowerCamelCase_ ,batch_size=2)
				lowerCAmelCase__  :       int				=  DataLoader(tokenized_datasets['''validation'''] ,shuffle=lowerCamelCase_ ,collate_fn=lowerCamelCase_ ,batch_size=1)

				return train_dataloader, eval_dataloader






from bisect import bisect
from itertools import accumulate






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  sorted(zip(lowerCamelCase_ ,lowerCamelCase_) ,key=lambda lowerCamelCase_: x[0] / x[1] ,reverse=lowerCamelCase_)
				lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__  :       Tuple				=  list(accumulate(lowerCamelCase_))
				lowerCAmelCase__  :       str				=  bisect(lowerCamelCase_ ,lowerCamelCase_)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k])
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

