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				):







							@slow
							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:int						  =     FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''xlm-roberta-base'''		)
										lowerCamelCase						:Dict						  =     '''The dog is cute and lives in the garden house'''
										lowerCamelCase						:Tuple						  =     jnp.array([tokenizer.encode(UpperCamelCase__		)]		)

										lowerCamelCase						:int						  =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
										lowerCamelCase						:Union[str, Any]						  =     jnp.array(
										    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)

										lowerCamelCase						:Union[str, Any]						  =     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_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """NllbMoeForConditionalGeneration""",
			    """NllbMoeModel""",
			    """NllbMoePreTrainedModel""",
			    """NllbMoeTop2Router""",
			    """NllbMoeSparseMLP""",
			]


if TYPE_CHECKING:
			from .configuration_nllb_moe import (
			    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    NllbMoeConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nllb_moe import (
						    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NllbMoeForConditionalGeneration,
						    NllbMoeModel,
						    NllbMoePreTrainedModel,
						    NllbMoeSparseMLP,
						    NllbMoeTopaRouter,
						)


else:
			import sys

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



