


'''simple docstring'''







import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax

     from transformers.models.big_bird.modeling_flax_big_bird import (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
         FlaxBigBirdModel,
     )
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=2      ,	snake_case__=56      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=99      ,	snake_case__=32      ,	snake_case__=2      ,	snake_case__=2      ,	snake_case__=7      ,	snake_case__="gelu_new"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=16      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=4      ,	snake_case__="block_sparse"      ,	snake_case__=True      ,	snake_case__=False      ,	snake_case__=2      ,	snake_case__=3      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       parent
            _lowerCAmelCase    :							Optional[Any]   			=       batch_size
            _lowerCAmelCase    :							Tuple   			=       seq_length
            _lowerCAmelCase    :							Any   			=       is_training
            _lowerCAmelCase    :							List[str]   			=       use_attention_mask
            _lowerCAmelCase    :							Any   			=       use_token_type_ids
            _lowerCAmelCase    :							List[str]   			=       use_labels
            _lowerCAmelCase    :							Dict   			=       vocab_size
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_size
            _lowerCAmelCase    :							List[Any]   			=       num_hidden_layers
            _lowerCAmelCase    :							Optional[int]   			=       num_attention_heads
            _lowerCAmelCase    :							Union[str, Any]   			=       intermediate_size
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[str]   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							Optional[int]   			=       max_position_embeddings
            _lowerCAmelCase    :							Optional[int]   			=       type_vocab_size
            _lowerCAmelCase    :							Optional[int]   			=       type_sequence_label_size
            _lowerCAmelCase    :							str   			=       initializer_range
            _lowerCAmelCase    :							str   			=       num_choices

            _lowerCAmelCase    :							Optional[int]   			=       rescale_embeddings
            _lowerCAmelCase    :							List[str]   			=       attention_type
            _lowerCAmelCase    :							int   			=       use_bias
            _lowerCAmelCase    :							Optional[int]   			=       block_size
            _lowerCAmelCase    :							Optional[Any]   			=       num_random_blocks







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

            _lowerCAmelCase    :							Any   			=       None
            if self.use_attention_mask:
                   _lowerCAmelCase    :							List[str]   			=       random_attention_mask([self.batch_size, self.seq_length]   )

            _lowerCAmelCase    :							List[str]   			=       None
            if self.use_token_type_ids:
                   _lowerCAmelCase    :							Optional[int]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

            _lowerCAmelCase    :							int   			=       BigBirdConfig(
                vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=_lowerCamelCase      ,	initializer_range=self.initializer_range      ,	attention_type=self.attention_type      ,	block_size=self.block_size      ,	num_random_blocks=self.num_random_blocks      ,	use_bias=self.use_bias      ,	rescale_embeddings=self.rescale_embeddings      ,	)

            return config, input_ids, token_type_ids, attention_mask







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       config_and_inputs
            _lowerCAmelCase    :							str   			=       {
                'input_ids': input_ids,
                'token_type_ids': token_type_ids,
                'attention_mask': attention_mask,
            }
            return config, inputs_dict
@require_flax
class 	UpperCamelCase__					( lowerCAmelCase__    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    (
         (
             FlaxBigBirdForCausalLM,
             FlaxBigBirdModel,
             FlaxBigBirdForPreTraining,
             FlaxBigBirdForMaskedLM,
             FlaxBigBirdForMultipleChoice,
             FlaxBigBirdForQuestionAnswering,
             FlaxBigBirdForSequenceClassification,
             FlaxBigBirdForTokenClassification,
         )
         if is_flax_available()
         else ()
     )

     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       FlaxBigBirdModelTester(self   )







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def a	( self   ):

            '''simple docstring'''

            super().test_from_pretrained_save_pretrained()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def a	( self   ):

            '''simple docstring'''

            super().test_from_pretrained_with_no_automatic_init()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def a	( self   ):

            '''simple docstring'''

            super().test_no_automatic_init()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def a	( self   ):

            '''simple docstring'''

            super().test_hidden_states_output()







     @slow
     def a	( self   ):

            '''simple docstring'''

            for model_class_name in self.all_model_classes:
                   _lowerCAmelCase    :							Tuple   			=       model_class_name.from_pretrained('google/bigbird-roberta-base'   )
                   self.assertIsNotNone(_lowerCamelCase   )







     def a	( self   ):

            '''simple docstring'''

            if self.test_attn_probs:
                   super().test_attention_outputs()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__   ):
                          _lowerCAmelCase    :							Optional[Any]   			=       self._prepare_for_class(_lowerCamelCase      ,	_lowerCamelCase   )
                          _lowerCAmelCase    :							int   			=       model_class(_lowerCamelCase   )

                          @jax.jit
                          def model_jitted(snake_case__      ,	snake_case__=None      ,	**snake_case__   ):
                                 return model(input_ids=_lowerCamelCase      ,	attention_mask=_lowerCamelCase      ,	**_lowerCamelCase   )

                          with self.subTest('JIT Enabled'   ):
                                 _lowerCAmelCase    :							List[Any]   			=       model_jitted(**_lowerCamelCase   ).to_tuple()

                          with self.subTest('JIT Disabled'   ):
                                 with jax.disable_jit():
                                        _lowerCAmelCase    :							int   			=       model_jitted(**_lowerCamelCase   ).to_tuple()

                          self.assertEqual(len(_lowerCamelCase   )      ,	len(_lowerCamelCase   )   )
                          for jitted_output, output in zip(_lowerCamelCase      ,	_lowerCamelCase   ):
                                 self.assertEqual(jitted_output.shape      ,	output.shape   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__=1E-5      ,	snake_case__="outputs"      ,	snake_case__=None   ):

            '''simple docstring'''

            if name.startswith('outputs.attentions'   ):
                   return
            else:
                   super().check_pt_flax_outputs(_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase   )





'''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	):



     """simple docstring"""






     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Optional[Any]   			=       AutoTokenizer.from_pretrained('xlm-roberta-base'   )
            _lowerCAmelCase    :							Dict   			=       'The dog is cute and lives in the garden house'
            _lowerCAmelCase    :							List[str]   			=       jnp.array([tokenizer.encode(snake_case__   )]   )

            _lowerCAmelCase    :							Optional[int]   			=       (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
            _lowerCAmelCase    :							Tuple   			=       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]]   )

            _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__   )['last_hidden_state']
            self.assertEqual(output.shape      ,	snake_case__   )
            # compare the actual values for a slice of last dim
            self.assertTrue(jnp.allclose(output[:, :, -1]      ,	snake_case__      ,	atol=1E-3   )   )

