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    :				Any							     =	logging.get_logger(__name__)







@dataclass
class __snake_case       (		snake_case__	):
     lowerCAmelCase_														=							[
         "no_inference",
         "no_cuda",
         "no_tpu",
         "no_speed",
         "no_memory",
         "no_env_print",
         "no_multi_process",
     ]




     def __init__(					self    :       List[str] ,			**_lowercase    :       Any   ):



       """simple docstring"""






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

       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""torchscript""" ,			self.torchscript   )
       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""torch_xla_tpu_print_metrics""" ,			self.torch_xla_tpu_print_metrics   )
       SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""fp16_opt_level""" ,			self.fpaa_opt_level   )
       super().__init__(**lowercase_   )

     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    :       List[str]   ):



       """simple docstring"""






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




     @property
     def 			__a		(					self    :       Optional[Any]   ):



       """simple docstring"""






       return is_torch_tpu_available() and self.tpu




     @property
     def 			__a		(					self    :       int   ):



       """simple docstring"""






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




     @property
     def 			__a		(					self    :       Optional[Any]   ):



       """simple docstring"""






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




     @property
     def 			__a		(					self    :       Any   ):



       """simple docstring"""






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




     @property
     def 			__a		(					self    :       Tuple   ):



       """simple docstring"""






       return self.n_gpu > 0

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


__lowerCamelCase    :				Tuple							     =	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
   print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
   print('''Torch version:''', None)

try:
   import deepspeed

   print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
   print('''DeepSpeed version:''', None)

try:
   import tensorflow as tf

   print('''TensorFlow version:''', tf.__version__)
   print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
   print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
   print('''TensorFlow version:''', None)
