



'''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


lowerCAmelCase: str							=						logging.get_logger(__name__)

@dataclass
class 							a__( lowerCamelCase__     ):
    lowercase__			=   [
        """no_inference""",
        """no_cuda""",
        """no_tpu""",
        """no_speed""",
        """no_memory""",
        """no_env_print""",
        """no_multi_process""",
    ]







    def __init__(				self			:  int						,      **__snake_case			:  Dict     ):
      for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
          a						: int					       =					deprecated_arg[3:]
          a						: Union[str, Any]					       =					not kwargs.pop(__snake_case     )
          logger.warning(
              F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
              F""" {positive_arg}={kwargs[positive_arg]}"""     )
      a						: Optional[Any]					       =					kwargs.pop('tpu_name'						,      self.tpu_name     )
      a						: Tuple					       =					kwargs.pop('device_idx'						,      self.device_idx     )
      a						: Tuple					       =					kwargs.pop('eager_mode'						,      self.eager_mode     )
      a						: str					       =					kwargs.pop('use_xla'						,      self.use_xla     )
      super().__init__(**__snake_case     )

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







    @cached_property
    def       lowercase_	(				self			:  Dict     ):
      requires_backends(self						,      ['tf']     )
      a						: Optional[Any]					       =					None
      if self.tpu:
        try:
          if self.tpu_name:
            a						: Optional[int]					       =					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
          else:
            a						: Tuple					       =					tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
          a						: Any					       =					None
      return tpu







    @cached_property
    def       lowercase_	(				self			:  Optional[int]     ):
      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     )

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

      return strategy







    @property
    def       lowercase_	(				self			:  Dict     ):
      requires_backends(self						,      ['tf']     )
      return self._setup_tpu is not None







    @property
    def       lowercase_	(				self			:  Tuple     ):
      requires_backends(self						,      ['tf']     )
      return self._setup_strategy







    @property
    def       lowercase_	(				self			:  Union[str, Any]     ):
      requires_backends(self						,      ['tf']     )
      return tf.config.list_physical_devices('GPU'     )







    @property
    def       lowercase_	(				self			:  int     ):
      requires_backends(self						,      ['tf']     )
      if self.cuda:
        return len(self.gpu_list     )
      return 0







    @property
    def       lowercase_	(				self			:  Any     ):
      return self.n_gpu > 0




'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase: Any							=						logging.get_logger(__name__)

class 							a__( lowerCamelCase__     ):
    lowercase__			=   """encoder-decoder"""
    lowercase__			=   True







    def __init__(				self			:  Dict						,      **__snake_case			:  Union[str, Any]     ):
      super().__init__(**__snake_case     )
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      a						: List[str]					       =					kwargs.pop('encoder'     )
      a						: Optional[Any]					       =					encoder_config.pop('model_type'     )
      a						: Tuple					       =					kwargs.pop('decoder'     )
      a						: Optional[int]					       =					decoder_config.pop('model_type'     )

      from ..auto.configuration_auto import AutoConfig

      a						: Any					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Optional[int]					       =					AutoConfig.for_model(__snake_case						,      **__snake_case     )
      a						: Tuple					       =					True







    @classmethod
    def       lowercase_	(				cls			:  int						,      __snake_case			:  PretrainedConfig						,      __snake_case			:  PretrainedConfig						,      **__snake_case			:  Union[str, Any]     ):
      logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'     )
      a						: List[Any]					       =					True
      a						: Tuple					       =					True

      return cls(encoder=encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__snake_case     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: int					       =					copy.deepcopy(self.__dict__     )
      a						: List[str]					       =					self.encoder.to_dict()
      a						: Optional[int]					       =					self.decoder.to_dict()
      a						: Optional[Any]					       =					self.__class__.model_type
      return output