
'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ = 100 ,       ):
     _UpperCAmelCase					:  Dict         =    x_start
     _UpperCAmelCase					:  Dict         =    fnc(lowerCAmelCase_       )
     _UpperCAmelCase					:  Optional[Any]         =    0.0

     for _ in range(lowerCAmelCase_       ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _UpperCAmelCase					:  List[Any]         =    (x_end - x_start) / steps + xa
          _UpperCAmelCase					:  List[Any]         =    fnc(lowerCAmelCase_       )
          area += abs(fxa + fxa       ) * (xa - xa) / 2

          # Increment step
          _UpperCAmelCase					:  Optional[Any]         =    xa
          _UpperCAmelCase					:  Optional[int]         =    fxa
     return area


if __name__ == "__main__":







   def __A  (    lowerCAmelCase_       ):
        return x**3

   print('''f(x) = x^3''')
   print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
   lowerCAmelCase_			:			Tuple			   =     10
   while i <= 100000:
      lowerCAmelCase_			:			List[str]			   =     trapezoidal_area(f, -5, 5, i)
      print(F"with {i} steps: {area}")
      i *= 10








# 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					)
