



'''simple docstring'''




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 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			@classmethod
			def UpperCamelCase__ (							cls					:			Tuple						):





					"""simple docstring"""






					return cls



			@slow
			@require_torch_gpu
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.get_auto_remove_tmp_dir()
					__SCREAMING_SNAKE_CASE	:		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
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		get_gpu_count()
					__SCREAMING_SNAKE_CASE	:		Dict   	=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[sys.executable] + distributed_args + args
					execute_subprocess_async(lowerCAmelCase__							,					env=self.get_env()						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		os.path.join(lowerCAmelCase__							,					"""test_bleu.json"""						)
					__SCREAMING_SNAKE_CASE	:		int   	=		load_json(lowerCAmelCase__						)
					# print(metrics)
					self.assertGreaterEqual(metrics["""bleu"""]							,					2_5						)




'''simple docstring'''




UpperCamelCase__	:     List[Any]    =			10_00


def     lowerCAmelCase_       (     _lowerCamelCase:       float ):
		return x * x


def     lowerCAmelCase_       (     _lowerCamelCase:       Any	,				_lowerCamelCase:       float	,				_lowerCamelCase:       float	,				_lowerCamelCase:       int = 4 ):
		assert callable(
		    _lowerCamelCase ), F"the function(object) passed should be callable your input : {function}"
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"a should be float or integer your input : {a}"
		assert isinstance(function(_lowerCamelCase )	,				(float, int) ), (
		    "the function should return integer or float return type of your function, "
		    F"{type(_lowerCamelCase )}"
		)
		assert isinstance(_lowerCamelCase	,				(float, int) ), F"b should be float or integer your input : {b}"
		assert (
		    isinstance(_lowerCamelCase	,				_lowerCamelCase ) and precision > 0
		), F"precision should be positive integer your input : {precision}"

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		(b - a) / N_STEPS
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		function(_lowerCamelCase ) + function(_lowerCamelCase )

		for i in range(1	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		a + h * i
				result += function(_lowerCamelCase ) * (4 if i % 2 else 2)

		result *= h / 3
		return round(_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()