

"""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


_A							=  logging.get_logger(__name__)

@dataclass
class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







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



  def __init__(self							,       **_lowerCamelCase	):


        """simple docstring"""

        for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                    UpperCAmelCase__			:							List[str]							=    deprecated_arg[3:]
                    setattr(self							,       _lowerCamelCase							,       not kwargs.pop(_lowerCamelCase	)	)
                    logger.warning(
                        F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                        F""" {positive_arg}={kwargs[positive_arg]}"""	)

        UpperCAmelCase__			:							List[Any]							=    kwargs.pop("""torchscript"""							,       self.torchscript	)
        UpperCAmelCase__			:							Dict							=    kwargs.pop("""torch_xla_tpu_print_metrics"""							,       self.torch_xla_tpu_print_metrics	)
        UpperCAmelCase__			:							Union[str, Any]							=    kwargs.pop("""fp16_opt_level"""							,       self.fpaa_opt_level	)
        super().__init__(**_lowerCamelCase	)

  SCREAMING_SNAKE_CASE					      =  field(default=lowerCAmelCase__					,					metadata={'help': 'Trace the models using torchscript'}   )
  SCREAMING_SNAKE_CASE					      =  field(default=lowerCAmelCase__					,					metadata={'help': 'Print Xla/PyTorch tpu metrics'}   )
  SCREAMING_SNAKE_CASE					      =  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	):


        """simple docstring"""

        requires_backends(self							,       ["""torch"""]	)
        logger.info("""PyTorch: setting up devices"""	)
        if not self.cuda:
              UpperCAmelCase__			:							Dict							=    torch.device("""cpu"""	)
              UpperCAmelCase__			:							Optional[Any]							=    0
        elif is_torch_tpu_available():
              UpperCAmelCase__			:							Optional[Any]							=    xm.xla_device()
              UpperCAmelCase__			:							List[str]							=    0
        else:
              UpperCAmelCase__			:							int							=    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)
              UpperCAmelCase__			:							Optional[Any]							=    torch.cuda.device_count()
        return device, n_gpu



  @property
  def  _a      (self	):


        """simple docstring"""

        return is_torch_tpu_available() and self.tpu



  @property
  def  _a      (self	):


        """simple docstring"""

        requires_backends(self							,       ["""torch"""]	)
        # TODO(PVP): currently only single GPU is supported
        return torch.cuda.current_device()



  @property
  def  _a      (self	):


        """simple docstring"""

        requires_backends(self							,       ["""torch"""]	)
        return self._setup_devices[0]



  @property
  def  _a      (self	):


        """simple docstring"""

        requires_backends(self							,       ["""torch"""]	)
        return self._setup_devices[1]




  @property
  def  _a      (self	):


        """simple docstring"""

        return self.n_gpu > 0




"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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


_A							=  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CpmAntForCausalLM""",
        """CpmAntModel""",
        """CpmAntPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

