





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 	_snake_case ( self      )				->  Any:
						"""simple docstring"""





						a__	:						Optional[Any]      		=  TFCamembertModel.from_pretrained("jplu/tf-camembert-base"      )

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

						a__	:						Any      		=  model(snake_case      )["last_hidden_state"]
						a__	:						Union[str, Any]      		=  tf.TensorShape((1, 10, 768)      )
						self.assertEqual(output.shape					,			snake_case      )
						# compare the actual values for a slice.
						a__	:						Any      		=  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      )      )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :			Any			      =						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

