





"""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


lowerCAmelCase_			=							logging.get_logger(__name__)



@dataclass
class 	lowerCAmelCase					(      snake_case							):
							lowerCAmelCase__						 =  [
							    """no_inference""",
							    """no_cuda""",
							    """no_tpu""",
							    """no_speed""",
							    """no_memory""",
							    """no_env_print""",
							    """no_multi_process""",
							]






							def __init__(   self    ,  **a__     ):
														for deprecated_arg in self.deprecated_args:
																					if deprecated_arg in kwargs:
																												_UpperCAmelCase     =     deprecated_arg[3:]
																												setattr(self    ,  a__    ,  not kwargs.pop(a__     )     )
																												logger.warning(
																												    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																												    f""" {positive_arg}={kwargs[positive_arg]}"""     )

														_UpperCAmelCase     =     kwargs.pop('torchscript'    ,  self.torchscript     )
														_UpperCAmelCase     =     kwargs.pop('torch_xla_tpu_print_metrics'    ,  self.torch_xla_tpu_print_metrics     )
														_UpperCAmelCase     =     kwargs.pop('fp16_opt_level'    ,  self.fpaa_opt_level     )
														super().__init__(**a__     )

							lowerCAmelCase__						 =  field(default=snake_case			,	metadata={"""help""": """Trace the models using torchscript"""}							)
							lowerCAmelCase__						 =  field(default=snake_case			,	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 							__A (   self     ):
														requires_backends(self    ,  ['torch']     )
														logger.info('PyTorch: setting up devices'     )
														if not self.cuda:
																					_UpperCAmelCase     =     torch.device('cpu'     )
																					_UpperCAmelCase     =     0
														elif is_torch_tpu_available():
																					_UpperCAmelCase     =     xm.xla_device()
																					_UpperCAmelCase     =     0
														else:
																					_UpperCAmelCase     =     torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )
																					_UpperCAmelCase     =     torch.cuda.device_count()
														return device, n_gpu






							@property
							def 							__A (   self     ):
														return is_torch_tpu_available() and self.tpu






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






							@property
							def 							__A (   self     ):
														requires_backends(self    ,  ['torch']     )
														return self._setup_devices[0]






							@property
							def 							__A (   self     ):
														requires_backends(self    ,  ['torch']     )
														return self._setup_devices[1]






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













"""simple docstring"""






# 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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_			=							{'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							['''TimmBackbone''']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

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






