
"""simple docstring"""




import functools
from math import sqrt
from time import time






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							*__SCREAMING_SNAKE_CASE			,							**__SCREAMING_SNAKE_CASE							):
	lowercase					  =   time()
	lowercase					  =   func(*_lowerCAmelCase			,							**_lowerCAmelCase							)
	lowercase					  =   time()
	if int(end - start							) > 0:
		print(F'''{func.__name__} runtime: {(end - start):0.4f} s'''							)
	else:
		print(F'''{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'''							)
	return output






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	if n < 0:
		raise Exception('n is negative'							)
	if n == 0:
		return [0]
	lowercase					  =   [0, 1]
	for _ in range(n - 1							):
		fib.append(fib[-1] + fib[-2]							)
	return fib






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	def fib_recursive_term(__SCREAMING_SNAKE_CASE							) -> int:
		if i < 0:
			raise Exception('n is negative'							)
		if i < 2:
			return i
		return fib_recursive_term(i - 1							) + fib_recursive_term(i - 2							)

	if n < 0:
		raise Exception('n is negative'							)
	return [fib_recursive_term(_lowerCAmelCase							) for i in range(n + 1							)]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	@functools.cache
	def fib_recursive_term(__SCREAMING_SNAKE_CASE							) -> int:
		if i < 0:
			raise Exception('n is negative'							)
		if i < 2:
			return i
		return fib_recursive_term(i - 1							) + fib_recursive_term(i - 2							)

	if n < 0:
		raise Exception('n is negative'							)
	return [fib_recursive_term(_lowerCAmelCase							) for i in range(n + 1							)]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	if n < 0:
		raise Exception('n is negative'							)
	# Cache must be outside recursuive function
	# other it will reset every time it calls itself.
	lowercase					  =   {0: 0, 1: 1, 2: 1}  # Prefilled cache

	def rec_fn_memoized(__SCREAMING_SNAKE_CASE							) -> int:
		if num in cache:
			return cache[num]

		lowercase					  =   rec_fn_memoized(num - 1							) + rec_fn_memoized(num - 2							)
		lowercase					  =   value
		return value

	return [rec_fn_memoized(_lowerCAmelCase							) for i in range(n + 1							)]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	if n < 0:
		raise Exception('n is negative'							)
	if n >= 1475:
		raise Exception('n is too large'							)
	lowercase					  =   sqrt(5							)
	lowercase					  =   (1 + sqrt_a) / 2
	return [round(phi**i / sqrt_a							) for i in range(n + 1							)]


if __name__ == "__main__":
	UpperCAmelCase     				=			30
	time_func(fib_iterative, num)
	time_func(fib_recursive, num)  # Around 3s runtime
	time_func(fib_recursive_cached, num)  # Around 0ms runtime
	time_func(fib_memoization, num)
	time_func(fib_binet, num)



# 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     				=			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 	A_       (      __lowerCamelCase       ):




		'''simple docstring'''


		def __init__(	self		,   snake_case = 101	):
			lowercase					  =   length





		def __len__(	self	):
			return self.length





		def __getitem__(	self		,   snake_case	):
			return i







	class 	A_       :




		'''simple docstring'''


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







	class 	A_       (      nn.Module       ):




		'''simple docstring'''


		def __init__(	self	):
			super().__init__()
			# Add some (unused) params otherwise DDP will complain.
			lowercase					  =   nn.Linear(120		,   80	)





		def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=None	):
			if labels is not None:
				return torch.tensor(0.0		,   device=input_ids.device	), input_ids
			else:
				return input_ids






def 							UpperCAmelCase_		(							):
	lowercase					  =   HfArgumentParser((TrainingArguments,)							)
	sys.argv += ["--output_dir", "./examples"]
	lowercase					  =   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]:
		lowercase					  =   DummyDataset(__SCREAMING_SNAKE_CASE							)

		def compute_metrics(__SCREAMING_SNAKE_CASE							) -> Dict:
			lowercase					  =   list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
			lowercase					  =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

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

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   2

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

		lowercase					  =   trainer.predict(__SCREAMING_SNAKE_CASE							)
		logger.info(p.metrics							)
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics							)
			exit(1							)

		lowercase					  =   None

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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()

