
'''simple docstring'''





# 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 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  @classmethod
  def 		_lowerCamelCase						(cls	:			List[str]     ) ->							Optional[Any]:
        return cls


  @slow
  @require_torch_gpu
  def 		_lowerCamelCase						(self	:			Any     ) ->							Tuple:
        snake_case_    							=				self.get_auto_remove_tmp_dir()
        snake_case_    							=				f'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

        # we want this test to run even if there is only one GPU, but if there are more we use them all
        snake_case_    							=				get_gpu_count()
        snake_case_    							=				f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
        snake_case_    							=				[sys.executable] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase_						,			env=self.get_env()     )

        snake_case_    							=				os.path.join(lowerCAmelCase_						,			"test_bleu.json"     )
        snake_case_    							=				load_json(lowerCAmelCase_     )
        # print(metrics)
        self.assertGreaterEqual(metrics["bleu"]						,			25     )





"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) 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

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()





