import os
from string import ascii_letters

lowerCamelCase_				:		Any       						=       ascii_letters + """ \t\n"""
def 							lowerCAmelCase(	):
					__a							=   os.path.split(os.path.realpath(__lowerCamelCase						)						)
					__a							=   {}
					with open(path[0] + '/dictionary.txt'						) as dictionary_file:
										for word in dictionary_file.read().split('\n'						):
															__a							=   None
					return english_words


lowerCamelCase_				:		List[str]       						=       load_dictionary()
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   message.upper()
					__a							=   remove_non_letters(__lowerCamelCase						)
					__a							=   message.split()
					__a							=   len([word for word in possible_words if word in ENGLISH_WORDS]						)
					return float(__lowerCamelCase						) / len(__lowerCamelCase						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase = 20      ,				__lowerCamelCase = 85						):
					__a							=   get_english_count(__lowerCamelCase						) * 100 >= word_percentage
					__a							=   len(remove_non_letters(__lowerCamelCase						)						)
					__a							=   (float(__lowerCamelCase						) / len(__lowerCamelCase						)) * 100
					__a							=   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    )




