





"""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 TFXLMRobertaModel






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

			"""simple docstring"""







			@slow
			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""    )

									UpperCamelCase				:		Dict				   =			{
									    """input_ids""": tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]						,   dtype=tf.intaa    ),  # "My dog is cute"
									    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]						,   dtype=tf.intaa    ),
									}

									UpperCamelCase				:		Tuple				   =			model(_A    )["""last_hidden_state"""]
									UpperCamelCase				:		Union[str, Any]				   =			tf.TensorShape((1, 6, 7_6_8)    )
									self.assertEqual(output.shape						,   _A    )
									# compare the actual values for a slice.
									UpperCamelCase				:		Any				   =			tf.convert_to_tensor(
									    [
									        [
									            [0.0_68_17_62, 0.10_89_44_51, 0.06_77_25_04],
									            [-0.06_42_36_68, 0.02_36_66_15, 0.04_32_93_44],
									            [-0.06_05_72_95, 0.09_97_41_35, -0.00_07_05_84],
									        ]
									    ]						,   dtype=tf.floataa						,   )

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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

