


"""simple docstring"""



import unittest

import numpy as np

from transformers import AlbertConfig, 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.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )







class _lowerCAmelCase    (				unittest.TestCase     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=99						,     UpperCamelCase__=32						,     UpperCamelCase__=5						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=512						,     UpperCamelCase__=16						,     UpperCamelCase__=2						,     UpperCamelCase__=0.02						,     UpperCamelCase__=4						,     )     ->		List[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Dict				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

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

       snake_case   :				Union[str, Any]				 =   None
       if self.use_token_type_ids:
           snake_case   :				Tuple				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

       snake_case   :				Dict				 =   AlbertConfig(
           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=UpperCamelCase__						,     initializer_range=self.initializer_range						,     )

       return config, input_ids, token_type_ids, attention_mask







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				Any				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case ,snake_case   :				str				 =   config_and_inputs
       snake_case   :				int				 =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict







@require_flax
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Optional[int]							  =  (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				int				 =   FlaxAlbertModelTester(self						)







   @slow
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case   :				Tuple				 =   model_class_name.from_pretrained("albert-base-v2"						)
           snake_case   :				Any				 =   model(np.ones((1, 1)						)						)
           self.assertIsNotNone(UpperCamelCase__						)







@require_flax
class _lowerCAmelCase    (				unittest.TestCase     ):







   @slow
   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   FlaxAlbertModel.from_pretrained("albert-base-v2"						)
       snake_case   :				Optional[Any]				 =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
       snake_case   :				Tuple				 =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       snake_case   :				List[str]				 =   model(UpperCamelCase__						,     attention_mask=UpperCamelCase__						)[0]
       snake_case   :				Optional[Any]				 =   (1, 11, 768)
       self.assertEqual(output.shape						,     UpperCamelCase__						)
       snake_case   :				List[str]				 =   np.array(
           [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]						)

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,     UpperCamelCase__						,     atol=1e-4						)						)









"""simple docstring"""



from __future__ import annotations

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  list[float]			)     ->						Any:


    """simple docstring"""





    return np.maximum(0						,							lowercase			)


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]





