






"""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   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




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

		A_		:    str						     = (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
		A_		:    Union[str, 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]]				)

		A_		:    int						     = 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_torch_available


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TimesformerModel",
							    "TimesformerForVideoClassification",
							    "TimesformerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

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

