

'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)





@dataclass
class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    [
				    'no_inference',
				    'no_cuda',
				    'no_tpu',
				    'no_speed',
				    'no_memory',
				    'no_env_print',
				    'no_multi_process',
				]







				def __init__(       self     :		Tuple		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																						lowercase :							Optional[int]    		=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]}'''   )

										lowercase :							int    		=kwargs.pop('''torchscript'''		,					self.torchscript   )
										lowercase :							List[str]    		=kwargs.pop('''torch_xla_tpu_print_metrics'''		,					self.torch_xla_tpu_print_metrics   )
										lowercase :							int    		=kwargs.pop('''fp16_opt_level'''		,					self.fpaa_opt_level   )
										super().__init__(**UpperCAmelCase__   )

				lowerCamelCase_		       =    field(default=lowercase__						,							metadata={'help': 'Trace the models using torchscript'}      )
				lowerCamelCase_		       =    field(default=lowercase__						,							metadata={'help': 'Print Xla/PyTorch tpu metrics'}      )
				lowerCamelCase_		       =    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  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										requires_backends(self		,					['''torch''']   )
										logger.info('''PyTorch: setting up devices'''   )
										if not self.cuda:
																lowercase :							List[Any]    		=torch.device('''cpu'''   )
																lowercase :							int    		=0
										elif is_torch_tpu_available():
																lowercase :							Optional[Any]    		=xm.xla_device()
																lowercase :							int    		=0
										else:
																lowercase :							Optional[Any]    		=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
																lowercase :							str    		=torch.cuda.device_count()
										return device, n_gpu







				@property
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										return is_torch_tpu_available() and self.tpu







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										requires_backends(self		,					['''torch''']   )
										# TODO(PVP): currently only single GPU is supported
										return torch.cuda.current_device()







				@property
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										requires_backends(self		,					['''torch''']   )
										return self._setup_devices[0]







				@property
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										requires_backends(self		,					['''torch''']   )
										return self._setup_devices[1]







				@property
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										return self.n_gpu > 0








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





