
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
      import tensorflow as tf


_lowerCamelCase					   =logging.get_logger(__name__)





@dataclass
class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    int           =			[
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]
      def __init__(      self    ,  **__magic_name__      ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         lowerCamelCase		:	List[str]							=      deprecated_arg[3:]
         lowerCamelCase		:	int							=      not kwargs.pop(__magic_name__      )
         logger.warning(
             F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
             F''' {positive_arg}={kwargs[positive_arg]}'''      )
       lowerCamelCase		:	List[str]							=      kwargs.pop("""tpu_name"""    ,  self.tpu_name      )
       lowerCamelCase		:	Optional[int]							=      kwargs.pop("""device_idx"""    ,  self.device_idx      )
       lowerCamelCase		:	Dict							=      kwargs.pop("""eager_mode"""    ,  self.eager_mode      )
       lowerCamelCase		:	str							=      kwargs.pop("""use_xla"""    ,  self.use_xla      )
       super().__init__(**__magic_name__      )

      _UpperCAmelCase				:    str           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Name of TPU"""}  ,   )
      _UpperCAmelCase				:    int           =			field(
          default=0  ,   metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}  ,   )
      _UpperCAmelCase				:    bool           =			field(default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Benchmark models in eager model."""})
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
          }  ,   )
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       lowerCamelCase		:	Union[str, Any]							=      None
       if self.tpu:
        try:
         if self.tpu_name:
          lowerCamelCase		:	Optional[Any]							=      tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
         else:
          lowerCamelCase		:	Any							=      tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
         lowerCamelCase		:	List[Any]							=      None
       return tpu
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       if self.is_tpu:
        tf.config.experimental_connect_to_cluster(self._setup_tpu      )
        tf.tpu.experimental.initialize_tpu_system(self._setup_tpu      )

        lowerCamelCase		:	Tuple							=      tf.distribute.TPUStrategy(self._setup_tpu      )
       else:
        # currently no multi gpu is allowed
        if self.is_gpu:
         # TODO: Currently only single GPU is supported
         tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,  """GPU"""      )
         lowerCamelCase		:	Dict							=      tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''      )
        else:
         tf.config.set_visible_devices([]    ,  """GPU"""      )  # disable GPU
         lowerCamelCase		:	Tuple							=      tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''      )

       return strategy
      @property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       return self._setup_tpu is not None
      @property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       return self._setup_strategy
      @property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       return tf.config.list_physical_devices("""GPU"""      )
      @property
      def 						UpperCamelCase__							(      self      ):
       requires_backends(self    ,  ["""tf"""]      )
       if self.cuda:
        return len(self.gpu_list      )
       return 0






      @property
      def 						UpperCamelCase__							(      self      ):
       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 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  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      )      )




