






'''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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      List[Any]:
							a_		:	List[Any]									=   TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''       )

							a_		:	Union[str, Any]									=   {
							    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]	,    dtype=tf.intaa       ),  # "My dog is cute"
							    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]	,    dtype=tf.intaa       ),
							}

							a_		:	List[Any]									=   model(__SCREAMING_SNAKE_CASE       )['''last_hidden_state''']
							a_		:	Dict									=   tf.TensorShape((1, 6, 768)       )
							self.assertEqual(output.shape	,    __SCREAMING_SNAKE_CASE       )
							# compare the actual values for a slice.
							a_		:	str									=   tf.convert_to_tensor(
							    [
							        [
							            [0.068_1762, 0.1089_4451, 0.0677_2504],
							            [-0.0642_3668, 0.0236_6615, 0.0432_9344],
							            [-0.0605_7295, 0.0997_4135, -0.0007_0584],
							        ]
							    ]	,    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


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    '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

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




