






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

from accelerate.utils.dataclasses import DistributedType
class  lowerCAmelCase_       :




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   Any=2 ,							SCREAMING_SNAKE_CASE_     :   Dict=3 ,							SCREAMING_SNAKE_CASE_     :   Tuple=64 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=None	):
											lowerCAmelCase__      			=						np.random.default_rng(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						length
											lowerCAmelCase__      			=						rng.normal(size=(length,)	).astype(np.floataa	)
											lowerCAmelCase__      			=						a * self.x + b + rng.normal(scale=0.1 ,							size=(length,)	).astype(np.floataa	)




							def __len__(   self     :   str	):
											return self.length




							def __getitem__(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int	):
											return {"x": self.x[i], "y": self.y[i]}



class  lowerCAmelCase_       (				torch.nn.Module					):




							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0 ,							SCREAMING_SNAKE_CASE_     :   List[str]=False	):
											super().__init__()
											lowerCAmelCase__      			=						torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
											lowerCAmelCase__      			=						torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
											lowerCAmelCase__      			=						True




							def 						__snake_case					(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   str=None	):
											if self.first_batch:
															print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
															lowerCAmelCase__      			=						False
											return x * self.a[0] + self.b[0]



class  lowerCAmelCase_       (				torch.nn.Module					):




							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Dict=0 ,							SCREAMING_SNAKE_CASE_     :   str=0 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=False	):
											super().__init__()
											lowerCAmelCase__      			=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_	).float()	)
											lowerCAmelCase__      			=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_	).float()	)
											lowerCAmelCase__      			=						True




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any=None	):
											if self.first_batch:
															print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
															lowerCAmelCase__      			=						False
											return x * self.a + self.b





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       int = 16       )		->    List[Any]:


				'''simple docstring'''




				from datasets import load_dataset
				from transformers import AutoTokenizer

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

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

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

				def collate_fn(lowercase__						:       Tuple       ):
								# 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(lowercase__     ,					padding='''max_length'''     ,					max_length=1_28     ,					return_tensors='''pt'''       )
								return tokenizer.pad(lowercase__     ,					padding='''longest'''     ,					return_tensors='''pt'''       )

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

				return train_dataloader, eval_dataloader













from pathlib import Path

import fire





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       str     ,					lowercase__						:       int       )		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						Path(lowercase__       )
				lowerCAmelCase__      			=						Path(lowercase__       )
				dest_dir.mkdir(exist_ok=lowercase__       )
				for path in src_dir.iterdir():
								lowerCAmelCase__      			=						[x.rstrip() for x in list(path.open().readlines()       )][:n]
								lowerCAmelCase__      			=						dest_dir.joinpath(path.name       )
								print(lowercase__       )
								dest_path.open('''w'''       ).write('''\n'''.join(lowercase__       )       )


if __name__ == "__main__":
			fire.Fire(minify)





