



# 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      snake_case__       (A__	):







						"""simple docstring"""




						@classmethod
						def 							SCREAMING_SNAKE_CASE__(     cls						)      ->   Optional[Any]:
									"""simple docstring"""




									return cls

						@slow
						@require_torch_gpu
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		Optional[Any]		 =						self.get_auto_remove_tmp_dir()
									a__     :		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
									a__     :		str		 =						get_gpu_count()
									a__     :		Optional[int]		 =						F'''\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        '''.split()
									a__     :		Optional[int]		 =						[sys.executable] + distributed_args + args
									execute_subprocess_async(__lowercase      ,     env=self.get_env()						)

									a__     :		Optional[int]		 =						os.path.join(__lowercase      ,     """test_bleu.json"""						)
									a__     :		Tuple		 =						load_json(__lowercase						)
									# print(metrics)
									self.assertGreaterEqual(metrics["""bleu"""]      ,     2_5						)









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_lowercase	:   Optional[int]    	=None


_lowercase	:   Union[str, Any]    	=logging.get_logger(__name__)


_lowercase	:   Any    	="▁"

_lowercase	:   Union[str, Any]    	={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase	:   List[str]    	={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase	:   List[str]    	={
    "google/reformer-crime-and-punishment": 52_4288,
}

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Union[str, Any]			      =  VOCAB_FILES_NAMES
						__lowerCAmelCase  :int			      =  PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase  :List[Any]			      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase  :Any			      =  ["input_ids", "attention_mask"]
						__lowerCAmelCase  :Tuple			      =  ReformerTokenizer

						def __init__(     self      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase="</s>"      ,     __lowercase="<unk>"      ,     __lowercase=[]      ,     **__lowercase      ,     )      ->   int:
									"""simple docstring"""




									super().__init__(
									    __lowercase      ,     tokenizer_file=__lowercase      ,     eos_token=__lowercase      ,     unk_token=__lowercase      ,     additional_special_tokens=__lowercase      ,     **__lowercase      ,     )

									a__     :		Union[str, Any]		 =						vocab_file
									a__     :		str		 =						False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   Tuple[str]:
									"""simple docstring"""




									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""						)

									if not os.path.isdir(__lowercase						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__     :		Tuple		 =						os.path.join(
									    __lowercase      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
												copyfile(self.vocab_file      ,     __lowercase						)

									return (out_vocab_file,)




