




'''simple docstring'''



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

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