
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 		_lowercase		(       unittest.TestCase	):

   @slow
   def 		A							(      self			:       str							)       ->				str:


    """simple docstring"""






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

    a						=		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
    a						=		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]]							)

    a						=		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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
     from .pipeline_unclip import UnCLIPPipeline
     from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
     from .text_proj import UnCLIPTextProjModel

