import os
from string import ascii_letters

UpperCamelCase			 =	ascii_letters + """ \t\n"""



def     lowerCamelCase_    (  )							->       List[str]:
		__A							:    Tuple         =      os.path.split(os.path.realpath(__lowerCamelCase				)				)
		__A							:    List[Any]         =      {}
		with open(path[0] + "/dictionary.txt"				) as dictionary_file:
				for word in dictionary_file.read().split("\n"				):
						__A							:    int         =      None
		return english_words


UpperCamelCase			 =	load_dictionary()



def     lowerCamelCase_    (  _lowercase				)							->       Any:
		__A							:    int         =      message.upper()
		__A							:    Any         =      remove_non_letters(__lowerCamelCase				)
		__A							:    Dict         =      message.split()
		__A							:    int         =      len([word for word in possible_words if word in ENGLISH_WORDS]				)
		return float(__lowerCamelCase				) / len(__lowerCamelCase				)



def     lowerCamelCase_    (  _lowercase				)							->       Dict:
		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE				)



def     lowerCamelCase_    (  _lowercase					,	_lowercase = 20					,	_lowercase = 85				)							->       Any:
		__A							:    str         =      get_english_count(__lowerCamelCase				) * 100 >= word_percentage
		__A							:    Dict         =      len(remove_non_letters(__lowerCamelCase				)				)
		__A							:    List[Any]         =      (float(__lowerCamelCase				) / len(__lowerCamelCase				)) * 100
		__A							:    Tuple         =      message_letters_percentage >= letter_percentage
		return words_match and letters_match


if __name__ == "__main__":
					import doctest

					doctest.testmod()






# 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 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   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
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




