




import functools
from math import sqrt
from time import time






def 					snake_case					(							A						,   *A						,   **A			)							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							   =							time()
	lowerCamelCase_							:		List[str]							   =							func(*A						,   **A			)
	lowerCamelCase_							:		Any							   =							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 					snake_case					(							A			)							->							list[int]:




	'''simple docstring'''


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






def 					snake_case					(							A			)							->							list[int]:




	'''simple docstring'''


	def fib_recursive_term(A			) -> 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(A			) for i in range(n + 1			)]






def 					snake_case					(							A			)							->							list[int]:




	'''simple docstring'''


	@functools.cache
	def fib_recursive_term(A			) -> 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(A			) for i in range(n + 1			)]






def 					snake_case					(							A			)							->							list[int]:




	'''simple docstring'''


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

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

		lowerCamelCase_							:		str							   =							rec_fn_memoized(num - 1			) + rec_fn_memoized(num - 2			)
		lowerCamelCase_							:		Optional[int]							   =							value
		return value

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






def 					snake_case					(							A			)							->							list[int]:




	'''simple docstring'''


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


if __name__ == "__main__":
							UpperCamelCase__              =						3_0
							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)






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
