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      __lowercase					(    _A    ):
						lowercase					 =     [
						    'no_inference',
						    'no_cuda',
						    'no_tpu',
						    'no_speed',
						    'no_memory',
						    'no_env_print',
						    'no_multi_process',
						]

						def __init__(	self				:      Any							,							**__lowerCamelCase				:      int   )							->		Any:


									'''simple docstring'''

									for deprecated_arg in self.deprecated_args:
												if deprecated_arg in kwargs:
															lowercase							=		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							=		kwargs.pop('''torchscript'''							,							self.torchscript   )
									lowercase							=		kwargs.pop('''torch_xla_tpu_print_metrics'''							,							self.torch_xla_tpu_print_metrics   )
									lowercase							=		kwargs.pop('''fp16_opt_level'''							,							self.fpaa_opt_level   )
									super().__init__(**__lowerCamelCase   )

						lowercase					 =     field(default=_A       ,     metadata={'help': 'Trace the models using torchscript'}    )
						lowercase					 =     field(default=_A       ,     metadata={'help': 'Print Xla/PyTorch tpu metrics'}    )
						lowercase					 =     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				:      List[str]   )							->		Tuple["torch.device", int]:


									'''simple docstring'''

									requires_backends(self							,							['''torch''']   )
									logger.info('''PyTorch: setting up devices'''   )
									if not self.cuda:
												lowercase							=		torch.device('''cpu'''   )
												lowercase							=		0
									elif is_torch_tpu_available():
												lowercase							=		xm.xla_device()
												lowercase							=		0
									else:
												lowercase							=		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
												lowercase							=		torch.cuda.device_count()
									return device, n_gpu

						@property
						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									return is_torch_tpu_available() and self.tpu

						@property
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

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

						@property
						def        __a		(	self				:      Any   )							->		"torch.device":


									'''simple docstring'''

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

						@property
						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

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



						@property
						def        __a		(	self				:      Dict   )							->		int:


									'''simple docstring'''

									return self.n_gpu > 0






import logging

from transformers import PretrainedConfig


A_      =      logging.getLogger(__name__)


A_      =      {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}






class      __lowercase					(    _A    ):
						lowercase					 =     'bertabs'

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Tuple=3_05_22							,							__lowerCamelCase				:      Tuple=5_12							,							__lowerCamelCase				:      List[Any]=6							,							__lowerCamelCase				:      Any=5_12							,							__lowerCamelCase				:      Any=8							,							__lowerCamelCase				:      Union[str, Any]=5_12							,							__lowerCamelCase				:      Tuple=0.2							,							__lowerCamelCase				:      str=6							,							__lowerCamelCase				:      int=7_68							,							__lowerCamelCase				:      int=8							,							__lowerCamelCase				:      List[Any]=20_48							,							__lowerCamelCase				:      Union[str, Any]=0.2							,							**__lowerCamelCase				:      Dict							,							)							->		Dict:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )

									lowercase							=		vocab_size
									lowercase							=		max_pos

									lowercase							=		enc_layers
									lowercase							=		enc_hidden_size
									lowercase							=		enc_heads
									lowercase							=		enc_ff_size
									lowercase							=		enc_dropout

									lowercase							=		dec_layers
									lowercase							=		dec_hidden_size
									lowercase							=		dec_heads
									lowercase							=		dec_ff_size
									lowercase							=		dec_dropout





