

'''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


lowercase_             =     logging.get_logger(__name__)





@dataclass
class 				__lowerCAmelCase  (      lowercase_							):
  _a		    =						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(       self							,     **lowerCAmelCase      )						->				str:
        '''simple docstring'''






        for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                    _lowercase			 =deprecated_arg[3:]
                    setattr(self							,     UpperCamelCase__							,     not kwargs.pop(UpperCamelCase__      )      )
                    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__(**UpperCamelCase__      )

  _a		    =						field(default=lowercase_    ,    metadata={"""help""": """Trace the models using torchscript"""}							)
  _a		    =						field(default=lowercase_    ,    metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}							)
  _a		    =						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      )						->				Union[str, Any]:
        '''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      )						->				Union[str, Any]:
        '''simple docstring'''






        return is_torch_tpu_available() and self.tpu




  @property
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






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




  @property
  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






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




  @property
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






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




  @property
  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        return self.n_gpu > 0





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase  (      unittest.TestCase							):




  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

        _lowercase			 =tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]							,     dtype=tf.intaa							,     )  # J'aime le camembert !"

        _lowercase			 =model(lowerCAmelCase      )['last_hidden_state']
        _lowercase			 =tf.TensorShape((1, 10, 768)      )
        self.assertEqual(output.shape							,     lowerCAmelCase      )
        # compare the actual values for a slice.
        _lowercase			 =tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]							,     dtype=tf.floataa							,     )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,     expected_slice.numpy()							,     atol=1e-4      )      )


