


"""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


a__       :							Optional[Any]        =  logging.get_logger(__name__)




@dataclass
class 				UpperCamelCase_			(				a__):

	"""simple docstring"""





	snake_case__       :						Dict					      =    [
	    "no_inference",
	    "no_cuda",
	    "no_tpu",
	    "no_speed",
	    "no_memory",
	    "no_env_print",
	    "no_multi_process",
	]




	def __init__(				self    :       List[Any]    ,    **UpperCAmelCase__    :       List[str]     ) ->			Tuple:

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

	snake_case__       :						str					      =    field(
	    default=a__    ,						metadata={"help": "Name of TPU"}    ,						)
	snake_case__       :						Optional[Any]					      =    field(
	    default=0    ,						metadata={"help": "CPU / GPU device index. Defaults to 0."}    ,						)
	snake_case__       :						Any					      =    field(default=a__    ,						metadata={"help": "Benchmark models in eager model."})
	snake_case__       :						str					      =    field(
	    default=a__    ,						metadata={
	        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
	    }    ,						)




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




	@cached_property
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Optional[Any]:
						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     )

											__SCREAMING_SNAKE_CASE		     =       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"     )
																__SCREAMING_SNAKE_CASE		     =       tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
											else:
																tf.config.set_visible_devices([]    ,    "GPU"     )  # disable GPU
																__SCREAMING_SNAKE_CASE		     =       tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

						return strategy




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




	@property
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						requires_backends(self    ,    ["tf"]     )
						return self._setup_strategy




	@property
	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[int]:
						requires_backends(self    ,    ["tf"]     )
						return tf.config.list_physical_devices("GPU"     )




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




	@property
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Union[str, Any]:
						return self.n_gpu > 0










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       :							Optional[Any]        =  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		a__       :							Union[str, Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
