






# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase_					:		Optional[int]           = logging.get_logger(__name__)


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset

	from transformers import Trainer

	class        _SCREAMING_SNAKE_CASE			(     _a ):





		def __init__(						self							:   Optional[Any]					,    __lowerCamelCase							:   int = 101      ):
			UpperCamelCase	:List[Any]		=      length





		def __len__(						self							:   Optional[int]      ):
			return self.length





		def __getitem__(						self							:   Dict					,    __lowerCamelCase							:   Union[str, Any]      ):
			return i

	class        _SCREAMING_SNAKE_CASE			:





		def __call__(						self							:   Dict					,    __lowerCamelCase							:   Tuple      ):
			return {"input_ids": torch.tensor(__lowerCamelCase      ), "labels": torch.tensor(__lowerCamelCase      )}

	class        _SCREAMING_SNAKE_CASE			(     nn.Module ):





		def __init__(						self							:   Optional[int]      ):
			super().__init__()
			# Add some (unused) params otherwise DDP will complain.
			UpperCamelCase	:Union[str, Any]		=      nn.Linear(120					,    80      )





		def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Tuple=None      ):
			if labels is not None:
				return torch.tensor(0.0					,    device=input_ids.device      ), input_ids
			else:
				return input_ids

def   SCREAMING_SNAKE_CASE_				(				)						->     List[Any]:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      HfArgumentParser((TrainingArguments,)   )
	sys.argv += ["--output_dir", "./examples"]
	UpperCamelCase	:int		=      parser.parse_args_into_dataclasses()[0]

	logger.warning(
	    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
	    f"""tpu_num_cores: {training_args.tpu_num_cores}"""      ,     )

	# Essentially, what we want to verify in the distributed case is
	# that we get all samples back, in the right order.
	# (this is crucial for prediction for instance)
	for dataset_length in [1001, 256, 15]:
		UpperCamelCase	:List[str]		=      DummyDataset(__magic_name__   )

		def compute_metrics(__magic_name__   :    EvalPrediction   ) -> Dict:
			UpperCamelCase	:int		=      list(range(len(__magic_name__   )   )   )
			UpperCamelCase	:List[str]		=      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

		UpperCamelCase	:List[Any]		=      Trainer(
		    model=DummyModel()      ,     args=__magic_name__      ,     data_collator=DummyDataCollator()      ,     eval_dataset=__magic_name__      ,     compute_metrics=__magic_name__      ,     )
		UpperCamelCase	:Dict		=      trainer.evaluate()
		logger.info(__magic_name__   )
		if metrics["eval_success"] is not True:
			logger.error(__magic_name__   )
			exit(1   )

		UpperCamelCase	:List[Any]		=      trainer.predict(__magic_name__   )
		logger.info(p.metrics   )
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics   )
			exit(1   )

		UpperCamelCase	:Dict		=      2

		UpperCamelCase	:str		=      trainer.evaluate()
		logger.info(__magic_name__   )
		if metrics["eval_success"] is not True:
			logger.error(__magic_name__   )
			exit(1   )

		UpperCamelCase	:List[Any]		=      trainer.predict(__magic_name__   )
		logger.info(p.metrics   )
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics   )
			exit(1   )

		UpperCamelCase	:str		=      None

	logger.info("""🔥 All distributed tests successful"""   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]   )						->     List[Any]:






	"""simple docstring"""



	main()


if __name__ == "__main__":
	main()








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
