
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
			import tensorflow as tf


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
@dataclass
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		[
							    '''no_inference''',
							    '''no_cuda''',
							    '''no_tpu''',
							    '''no_speed''',
							    '''no_memory''',
							    '''no_env_print''',
							    '''no_multi_process''',
							]




							def __init__( self    ,				**snake_case_				)       ->  Tuple:
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									__lowerCAmelCase     =  deprecated_arg[3:]
																									__lowerCAmelCase     =  not kwargs.pop(snake_case_				)
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""				)
													__lowerCAmelCase     =  kwargs.pop("""tpu_name"""    ,				self.tpu_name				)
													__lowerCAmelCase     =  kwargs.pop("""device_idx"""    ,				self.device_idx				)
													__lowerCAmelCase     =  kwargs.pop("""eager_mode"""    ,				self.eager_mode				)
													__lowerCAmelCase     =  kwargs.pop("""use_xla"""    ,				self.use_xla				)
													super().__init__(**snake_case_				)

							_snake_case						      =		field(
							    default=A__				,				metadata={'''help''': '''Name of TPU'''}				,				)
							_snake_case						      =		field(
							    default=0				,				metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}				,				)
							_snake_case						      =		field(default=A__				,				metadata={'''help''': '''Benchmark models in eager model.'''} )
							_snake_case						      =		field(
							    default=A__				,				metadata={
							        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
							    }				,				)




							@cached_property
							def    A__						( self				)       ->  Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
													requires_backends(self    ,				["""tf"""]				)
													__lowerCAmelCase     =  None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															__lowerCAmelCase     =  tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
																									else:
																															__lowerCAmelCase     =  tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									__lowerCAmelCase     =  None
													return tpu




							@cached_property
							def    A__						( self				)       ->  Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
													requires_backends(self    ,				["""tf"""]				)
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu				)
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu				)

																			__lowerCAmelCase     =  tf.distribute.TPUStrategy(self._setup_tpu				)
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,				"""GPU"""				)
																									__lowerCAmelCase     =  tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""				)
																			else:
																									tf.config.set_visible_devices([]    ,				"""GPU"""				)  # disable GPU
																									__lowerCAmelCase     =  tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""				)

													return strategy




							@property
							def    A__						( self				)       ->  bool:
													requires_backends(self    ,				["""tf"""]				)
													return self._setup_tpu is not None




							@property
							def    A__						( self				)       ->  "tf.distribute.Strategy":
													requires_backends(self    ,				["""tf"""]				)
													return self._setup_strategy




							@property
							def    A__						( self				)       ->  str:
													requires_backends(self    ,				["""tf"""]				)
													return tf.config.list_physical_devices("""GPU"""				)




							@property
							def    A__						( self				)       ->  int:
													requires_backends(self    ,				["""tf"""]				)
													if self.cuda:
																			return len(self.gpu_list				)
													return 0




							@property
							def    A__						( self				)       ->  bool:
													return self.n_gpu > 0


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_					     =	{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''RagModel''',
			    '''RagPreTrainedModel''',
			    '''RagSequenceForGeneration''',
			    '''RagTokenForGeneration''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE_					     =	[
			    '''TFRagModel''',
			    '''TFRagPreTrainedModel''',
			    '''TFRagSequenceForGeneration''',
			    '''TFRagTokenForGeneration''',
			]


if TYPE_CHECKING:
			from .configuration_rag import RagConfig
			from .retrieval_rag import RagRetriever
			from .tokenization_rag import RagTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_rag import (
						    TFRagModel,
						    TFRagPreTrainedModel,
						    TFRagSequenceForGeneration,
						    TFRagTokenForGeneration,
						)

else:
			import sys

			SCREAMING_SNAKE_CASE_					     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
