





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

from accelerate.utils.dataclasses import DistributedType

class       __A     :


				def __init__(		self   , UpperCAmelCase_=2   , UpperCAmelCase_=3   , UpperCAmelCase_=64   , UpperCAmelCase_=None							):
								lowerCamelCase	 =np.random.default_rng(_UpperCamelCase							)
								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							):
								return self.length


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




class       __A     (						torch.nn.Module    ):


				def __init__(		self   , UpperCAmelCase_=0   , UpperCAmelCase_=0   , UpperCAmelCase_=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   , UpperCAmelCase_=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       __A     (						torch.nn.Module    ):


				def __init__(		self   , UpperCAmelCase_=0   , UpperCAmelCase_=0   , UpperCAmelCase_=False							):
								super().__init__()
								lowerCamelCase	 =torch.nn.Parameter(torch.tensor(_UpperCamelCase							).float()							)
								lowerCamelCase	 =torch.nn.Parameter(torch.tensor(_UpperCamelCase							).float()							)
								lowerCamelCase	 =True


				def _snake_case       (		self   , UpperCAmelCase_=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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase = 16				)						->  Optional[Any]:
				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=__A				)
				lowerCamelCase	 =datasets["""train"""].unique("""label"""				)

				lowerCamelCase	 ={v: i for i, v in enumerate(__A				)}

				def tokenize_function(_UpperCAmelCase				):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase	 =tokenizer(
								    examples["""sentence1"""]			,      examples["""sentence2"""]			,      truncation=__A			,      max_length=__A			,      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(
				    __A			,      batched=__A			,      remove_columns=["""sentence1""", """sentence2""", """label"""]			,      )

				def collate_fn(_UpperCAmelCase				):
								# 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(__A			,      padding="""max_length"""			,      max_length=1_28			,      return_tensors="""pt"""				)
								return tokenizer.pad(__A			,      padding="""longest"""			,      return_tensors="""pt"""				)

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

				return train_dataloader, eval_dataloader












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


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





