


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


SCREAMING_SNAKE_CASE       :      Optional[int]												= logging.get_logger(__name__)






@dataclass
class _lowerCamelCase(						_a   ):
       lowercase_ :	int     =			[
           """no_inference""",
           """no_cuda""",
           """no_tpu""",
           """no_speed""",
           """no_memory""",
           """no_env_print""",
           """no_multi_process""",
       ]
       def __init__(					self,      **lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           for deprecated_arg in self.deprecated_args:
               if deprecated_arg in kwargs:
                   _lowercase       :							Tuple      		=		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]}''')

           _lowercase       :							Tuple      		=		kwargs.pop('torchscript',      self.torchscript)
           _lowercase       :							Optional[Any]      		=		kwargs.pop('torch_xla_tpu_print_metrics',      self.torch_xla_tpu_print_metrics)
           _lowercase       :							Dict      		=		kwargs.pop('fp16_opt_level',      self.fpaa_opt_level)
           super().__init__(**lowerCamelCase)

       lowercase_ :	bool     =			field(default=_a, metadata={"""help""": """Trace the models using torchscript"""}   )
       lowercase_ :	bool     =			field(default=_a, metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
       lowercase_ :	str     =			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 			UpperCamelCase     (					self)       ->   Tuple["torch.device", int]:




           """simple docstring"""

           requires_backends(self,      ['torch'])
           logger.info('PyTorch: setting up devices')
           if not self.cuda:
               _lowercase       :							Union[str, Any]      		=		torch.device('cpu')
               _lowercase       :							List[Any]      		=		0
           elif is_torch_tpu_available():
               _lowercase       :							int      		=		xm.xla_device()
               _lowercase       :							Any      		=		0
           else:
               _lowercase       :							Tuple      		=		torch.device('cuda' if torch.cuda.is_available() else 'cpu')
               _lowercase       :							str      		=		torch.cuda.device_count()
           return device, n_gpu
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return is_torch_tpu_available() and self.tpu
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

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




           """simple docstring"""

           requires_backends(self,      ['torch'])
           return self._setup_devices[0]
       @property
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

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




       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return self.n_gpu > 0






def 				UpperCamelCase_(      lowerCamelCase_ )    ->						int:
    if not numbers:
        return 0

    if not isinstance(lowerCamelCase_							, (list, tuple) ) or not all(
        isinstance(lowerCamelCase_							, lowerCamelCase_ ) for number in numbers ):
        raise ValueError('numbers must be an iterable of integers' )

    _lowercase       :							int      		=		numbers[0]

    for i in range(1							, len(lowerCamelCase_ ) ):
        # update the maximum and minimum subarray products
        _lowercase       :							Union[str, Any]      		=		numbers[i]
        if number < 0:
            _lowercase		,				_lowercase       :							Any      		=		min_till_now, max_till_now
        _lowercase       :							Union[str, Any]      		=		max(lowerCamelCase_							, max_till_now * number )
        _lowercase       :							Union[str, Any]      		=		min(lowerCamelCase_							, min_till_now * number )

        # update the maximum product found till now
        _lowercase       :							Optional[Any]      		=		max(lowerCamelCase_							, lowerCamelCase_ )

    return max_prod


