



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_lowercase:				List[Any]       =	set()
_lowercase:				str       =	get_logger(__name__)






def 					a(   A :					Optional[str] = None )	-> Any:
					"""simple docstring"""





					def decorator(A :					Callable ):
										global _emitted_deprecation_warnings

										if inspect.isclass(A ):
															a        =     deprecated_class_or_function.__init__
															a        =     deprecated_class_or_function.__name__
										else:
															a        =     deprecated_class_or_function
															a        =     deprecated_function.__name__
															# Support deprecating __init__ class method: class name instead
															a        =     name if name != "__init__" else deprecated_function.__qualname__.split("." )[-2]

										a        =     (
										    f'''{name} is deprecated and will be removed in the next major version of datasets.''' + f''' {help_message}'''
										    if help_message
										    else ""
										)

										@wraps(A )
										def wrapper(*A :					Any				,  **A :					List[Any] ):
															a        =     hash(A )
															if func_hash not in _emitted_deprecation_warnings:
																				warnings.warn(A				,  category=A				,  stacklevel=2 )
																				_emitted_deprecation_warnings.add(A )
															return deprecated_function(*A				,  **A )

										a        =     "deprecated"

										if inspect.isclass(A ):
															a        =     wrapper
															return deprecated_class_or_function
										else:
															return wrapper

					return decorator



class 			_lowercase							(     enum.EnumMeta			):





	"""simple docstring"""




	def __getattribute__(cls	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     super().__getattribute__(lowerCamelCase_	)
						if isinstance(lowerCamelCase_	, enum.Enum	) and obj._on_access:
											obj._on_access()
						return obj




	def __getitem__(cls	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     super().__getitem__(lowerCamelCase_	)
						if member._on_access:
											member._on_access()
						return member




	def __call__(cls	, lowerCamelCase_	, lowerCamelCase_=None	, *, lowerCamelCase_=None	, lowerCamelCase_=None	, lowerCamelCase_=None	, lowerCamelCase_=1	):
						"""simple docstring"""

						a        =     super().__call__(lowerCamelCase_	, lowerCamelCase_	, module=lowerCamelCase_	, qualname=lowerCamelCase_	, type=lowerCamelCase_	, start=lowerCamelCase_	)
						if isinstance(lowerCamelCase_	, enum.Enum	) and obj._on_access:
											obj._on_access()
						return obj




class 			_lowercase							(     enum.Enum,   metaclass=lowerCAmelCase			):





	"""simple docstring"""




	def __new__(cls	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     object.__new__(cls	)
						a        =     value
						a        =     member.deprecate
						return member




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return ""




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     F''' {self.help_message}''' if self.help_message else ""
						warnings.warn(
						    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
						    + help_message	, lowerCamelCase_	, stacklevel=3	, )





# 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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						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(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
