



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case   					=				{
    '''configuration_blenderbot_small''': [
        '''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotSmallConfig''',
        '''BlenderbotSmallOnnxConfig''',
    ],
    '''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				['''BlenderbotSmallTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BlenderbotSmallForCausalLM''',
         '''BlenderbotSmallForConditionalGeneration''',
         '''BlenderbotSmallModel''',
         '''BlenderbotSmallPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''TFBlenderbotSmallForConditionalGeneration''',
         '''TFBlenderbotSmallModel''',
         '''TFBlenderbotSmallPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''FlaxBlenderbotSmallForConditionalGeneration''',
         '''FlaxBlenderbotSmallModel''',
         '''FlaxBlenderbotSmallPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_blenderbot_small import (
         BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotSmallConfig,
         BlenderbotSmallOnnxConfig,
     )
     from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot_small import (
              BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotSmallForCausalLM,
              BlenderbotSmallForConditionalGeneration,
              BlenderbotSmallModel,
              BlenderbotSmallPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot_small import (
              TFBlenderbotSmallForConditionalGeneration,
              TFBlenderbotSmallModel,
              TFBlenderbotSmallPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot_small import (
              FlaxBlenderbotSmallForConditionalGeneration,
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallPreTrainedModel,
          )

else:
     import sys

     snake_case   					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""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     UpperCAmelCase    (	unittest.TestCase  ):



   @slow
   def    __UpperCAmelCase    (       self    :	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(__lowerCamelCase				)]				)

     _snake_case   	=      (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
     _snake_case   	=      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]]				)

     _snake_case   	=      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				)				)





