






from typing import Any







class     UpperCAmelCase_		:



						"""simple docstring"""
						def __init__(       self				)       ->   int:
										UpperCamelCase :List[Any]										=     []
										UpperCamelCase :Dict										=     0
						def __str__(       self				)       ->   str:
										UpperCamelCase :str										=     '''<''' + str(self.stack				)[1:-1] + '''>'''
										return printed
						def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
										self.stack.append(SCREAMING_SNAKE_CASE_				)
										UpperCamelCase :str										=     self.length + 1
						def 					UpperCAmelCase		(       self				)       ->   Any:
										self.rotate(1				)
										UpperCamelCase :Tuple										=     self.stack[self.length - 1]
										UpperCamelCase :str										=     self.stack[:-1]
										self.rotate(self.length - 1				)
										UpperCamelCase :Union[str, Any]										=     self.length - 1
										return dequeued
						def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
										for _ in range(SCREAMING_SNAKE_CASE_				):
														UpperCamelCase :Optional[int]										=     self.stack[0]
														UpperCamelCase :Union[str, Any]										=     self.stack[1:]
														self.put(SCREAMING_SNAKE_CASE_				)
														UpperCamelCase :Optional[Any]										=     self.length - 1
						def 					UpperCAmelCase		(       self				)       ->   Any:
										UpperCamelCase :int										=     self.get()
										self.put(SCREAMING_SNAKE_CASE_				)
										self.rotate(self.length - 1				)
										return front




						def 					UpperCAmelCase		(       self				)       ->   int:
										return self.length













# 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     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      @classmethod
      def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
          return cls




      @slow
      @require_torch_gpu
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :int										=     self.get_auto_remove_tmp_dir()
          UpperCamelCase :List[str]										=     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
          UpperCamelCase :List[str]										=     get_gpu_count()
          UpperCamelCase :Optional[Any]										=     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
          UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
          execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)

          UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
          UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
          # print(metrics)
          self.assertGreaterEqual(metrics['''bleu''']					,       25				)





