



"""simple docstring"""






from typing import TYPE_CHECKING

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


snake_case   					=				{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RemBertForCausalLM''',
         '''RemBertForMaskedLM''',
         '''RemBertForMultipleChoice''',
         '''RemBertForQuestionAnswering''',
         '''RemBertForSequenceClassification''',
         '''RemBertForTokenClassification''',
         '''RemBertLayer''',
         '''RemBertModel''',
         '''RemBertPreTrainedModel''',
         '''load_tf_weights_in_rembert''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFRemBertForCausalLM''',
         '''TFRemBertForMaskedLM''',
         '''TFRemBertForMultipleChoice''',
         '''TFRemBertForQuestionAnswering''',
         '''TFRemBertForSequenceClassification''',
         '''TFRemBertForTokenClassification''',
         '''TFRemBertLayer''',
         '''TFRemBertModel''',
         '''TFRemBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert import RemBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert_fast import RemBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rembert import (
              REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RemBertForCausalLM,
              RemBertForMaskedLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
              RemBertLayer,
              RemBertModel,
              RemBertPreTrainedModel,
              load_tf_weights_in_rembert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rembert import (
              TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRemBertForCausalLM,
              TFRemBertForMaskedLM,
              TFRemBertForMultipleChoice,
              TFRemBertForQuestionAnswering,
              TFRemBertForSequenceClassification,
              TFRemBertForTokenClassification,
              TFRemBertLayer,
              TFRemBertModel,
              TFRemBertPreTrainedModel,
          )


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				)				)





