






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    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				@slow
				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
											snake_case__	  =       AutoTokenizer.from_pretrained("""xlm-roberta-base""")
											snake_case__	  =       """The dog is cute and lives in the garden house"""
											snake_case__	  =       jnp.array([tokenizer.encode(UpperCamelCase__)])

											snake_case__	  =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
											snake_case__	  =       jnp.array(
											    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]])

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
