

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

from accelerate.utils.dataclasses import DistributedType






class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     Optional[Any]=2					,      __A						:     Union[str, Any]=3					,      __A						:     int=64					,      __A						:     Tuple=None							):
		__A :			Union[str, Any]		    =			np.random.default_rng(__A							)
		__A :			Optional[int]		    =			length
		__A :			Union[str, Any]		    =			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						:     Optional[int]							):
		return self.length




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






class      lowerCamelCase_      (	torch.nn.Module	):




	def __init__(     self						:     Tuple					,      __A						:     List[Any]=0					,      __A						:     Optional[int]=0					,      __A						:     Optional[int]=False							):
		super().__init__()
		__A :			int		    =			torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
		__A :			Any		    =			torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
		__A :			Tuple		    =			True




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Optional[Any]=None							):
		if self.first_batch:
			print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
			__A :			str		    =			False
		return x * self.a[0] + self.b[0]






class      lowerCamelCase_      (	torch.nn.Module	):




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




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Optional[int]=None							):
		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     (   a__    :   Union[str, Any]			,a__    :   int = 16       )					->				List[str]:
	from datasets import load_dataset
	from transformers import AutoTokenizer

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

	__A :			Optional[int]		    =			{v: i for i, v in enumerate(a__       )}

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

	def collate_fn(a__    :   Union[str, 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(a__			,padding="""max_length"""			,max_length=128			,return_tensors="""pt"""       )
		return tokenizer.pad(a__			,padding="""longest"""			,return_tensors="""pt"""       )

	# Instantiate dataloaders.
	__A :			Tuple		    =			DataLoader(tokenized_datasets["""train"""]			,shuffle=a__			,collate_fn=a__			,batch_size=2       )
	__A :			List[str]		    =			DataLoader(tokenized_datasets["""validation"""]			,shuffle=a__			,collate_fn=a__			,batch_size=1       )

	return train_dataloader, eval_dataloader








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





