





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


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)



@dataclass
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = [
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]






  def __init__(				self :		Tuple					,  **lowerCAmelCase_ :		Dict     )							->						Dict:
     for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
           __lowerCAmelCase								=	deprecated_arg[3:]
           __lowerCAmelCase								=	not kwargs.pop(lowerCAmelCase_     )
           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__(**lowerCAmelCase_     )

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






  @cached_property
  def 				lowercase		(				self :		int     )							->						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 				lowercase		(				self :		str     )							->						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 				lowercase		(				self :		Tuple     )							->						bool:
     requires_backends(self					,  ['tf']     )
     return self._setup_tpu is not None






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






  @property
  def 				lowercase		(				self :		Any     )							->						Dict:
     requires_backends(self					,  ['tf']     )
     return tf.config.list_physical_devices('GPU'     )






  @property
  def 				lowercase		(				self :		Union[str, Any]     )							->						int:
     requires_backends(self					,  ['tf']     )
     if self.cuda:
        return len(self.gpu_list     )
     return 0






  @property
  def 				lowercase		(				self :		List[Any]     )							->						bool:
     return self.n_gpu > 0








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    Dict						    =  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MraForMaskedLM',
         'MraForMultipleChoice',
         'MraForQuestionAnswering',
         'MraForSequenceClassification',
         'MraForTokenClassification',
         'MraLayer',
         'MraModel',
         'MraPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mra import (
              MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
              MraLayer,
              MraModel,
              MraPreTrainedModel,
          )
else:
     import sys

     _snake_case  :    Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure)

