

"""simple docstring"""





import os
import sys

import transformers


_a											=							'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)







"""simple docstring"""





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       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						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 !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						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     )     )




