





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

from accelerate.utils.dataclasses import DistributedType


class  __UpperCamelCase       :






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






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






							def __getitem__(     self   ,      lowerCAmelCase__   )			->				str:
								return {"x": self.x[i], "y": self.y[i]}


class  __UpperCamelCase       (		torch.nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=False   )			->				str:
								super().__init__()
								a   :	int			=       torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
								a   :	Optional[Any]			=       torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
								a   :	List[str]			=       True






							def 					__a     (     self   ,      lowerCAmelCase__=None   )			->				List[str]:
								if self.first_batch:
									print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""   )
									a   :	Tuple			=       False
								return x * self.a[0] + self.b[0]


class  __UpperCamelCase       (		torch.nn.Module		):






							def __init__(     self   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=False   )			->				int:
								super().__init__()
								a   :	Tuple			=       torch.nn.Parameter(torch.tensor(lowerCAmelCase__   ).float()   )
								a   :	Any			=       torch.nn.Parameter(torch.tensor(lowerCAmelCase__   ).float()   )
								a   :	Optional[int]			=       True






							def 					__a     (     self   ,      lowerCAmelCase__=None   )			->				Tuple:
								if self.first_batch:
									print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""   )
									a   :	Optional[Any]			=       False
								return x * self.a + self.b






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int = 16      )				->Dict:






	'''simple docstring'''






	from datasets import load_dataset
	from transformers import AutoTokenizer

	a   :	Optional[int]			=       AutoTokenizer.from_pretrained("bert-base-cased"      )
	a   :	Optional[int]			=       {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
	a   :	str			=       load_dataset("csv"					, data_files=_lowercase      )
	a   :	Dict			=       datasets["train"].unique("label"      )

	a   :	List[str]			=       {v: i for i, v in enumerate(_lowercase      )}

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

	def collate_fn(_lowercase		:					Any      ):
		# 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=128					, return_tensors="pt"      )
		return tokenizer.pad(_lowercase					, padding="longest"					, return_tensors="pt"      )

	# Instantiate dataloaders.
	a   :	List[Any]			=       DataLoader(tokenized_datasets["train"]					, shuffle=_lowercase					, collate_fn=_lowercase					, batch_size=2      )
	a   :	Optional[Any]			=       DataLoader(tokenized_datasets["validation"]					, shuffle=_lowercase					, collate_fn=_lowercase					, batch_size=1      )

	return train_dataloader, eval_dataloader









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


