






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 				UpperCAmelCase_  (		unittest.TestCase		):




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

          UpperCAmelCase__      :  str				      =			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
          UpperCAmelCase__      :  int				      =			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]]		)

          UpperCAmelCase__      :  Optional[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		)		)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

