from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
							import torch

if is_torch_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm


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


@dataclass
class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							[
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]




							def __init__(						self   :					Optional[Any]	,							**_UpperCamelCase   :					Optional[Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																						SCREAMING_SNAKE_CASE    			=       deprecated_arg[3:]
																						setattr(self	,							_UpperCamelCase	,							not kwargs.pop(_UpperCamelCase      )      )
																						logger.warning(
																						    F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
																						    F" {positive_arg}={kwargs[positive_arg]}"      )

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("torchscript"	,							self.torchscript      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("torch_xla_tpu_print_metrics"	,							self.torch_xla_tpu_print_metrics      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("fp16_opt_level"	,							self.fpaa_opt_level      )
												super().__init__(**_UpperCamelCase      )

							lowercase__						:       bool								=							field(default=a				,       metadata={"""help""": """Trace the models using torchscript"""}     )
							lowercase__						:       bool								=							field(default=a				,       metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
							lowercase__						:       str								=							field(
							    default="""O1"""				,       metadata={
							        """help""": (
							            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
							            """See details at https://nvidia.github.io/apex/amp.html"""
							        )
							    }				,       )




							@cached_property
							def 	__snake_case(						self   :					Any      )					->				Tuple["torch.device", int]:



												'''simple docstring'''

												requires_backends(self	,							["torch"]      )
												logger.info("PyTorch: setting up devices"      )
												if not self.cuda:
																	SCREAMING_SNAKE_CASE    			=       torch.device("cpu"      )
																	SCREAMING_SNAKE_CASE    			=       0
												elif is_torch_tpu_available():
																	SCREAMING_SNAKE_CASE    			=       xm.xla_device()
																	SCREAMING_SNAKE_CASE    			=       0
												else:
																	SCREAMING_SNAKE_CASE    			=       torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
																	SCREAMING_SNAKE_CASE    			=       torch.cuda.device_count()
												return device, n_gpu




							@property
							def 	__snake_case(						self   :					Any      )					->				List[str]:



												'''simple docstring'''

												return is_torch_tpu_available() and self.tpu




							@property
							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												requires_backends(self	,							["torch"]      )
												# TODO(PVP): currently only single GPU is supported
												return torch.cuda.current_device()




							@property
							def 	__snake_case(						self   :					Optional[Any]      )					->				"torch.device":



												'''simple docstring'''

												requires_backends(self	,							["torch"]      )
												return self._setup_devices[0]




							@property
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												requires_backends(self	,							["torch"]      )
												return self._setup_devices[1]





							@property
							def 	__snake_case(						self   :					int      )					->				List[str]:



												'''simple docstring'''

												return self.n_gpu > 0





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




