"""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 					_A		(			unittest.TestCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=7						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=99						,			__lowerCAmelCase=32						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=37						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=16						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=4						,			):







            """simple docstring"""



            lowercase								=      parent
            lowercase								=      batch_size
            lowercase								=      seq_length
            lowercase								=      is_training
            lowercase								=      use_attention_mask
            lowercase								=      use_token_type_ids
            lowercase								=      use_labels
            lowercase								=      vocab_size
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      max_position_embeddings
            lowercase								=      type_vocab_size
            lowercase								=      type_sequence_label_size
            lowercase								=      initializer_range
            lowercase								=      num_choices

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size		)

            lowercase								=      None
            if self.use_attention_mask:
                  lowercase								=      random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase								=      None
            if self.use_token_type_ids:
                  lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size		)

            lowercase								=      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=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			)

            return config, input_ids, token_type_ids, attention_mask







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.prepare_config_and_inputs()
            lowercase				,				lowercase				,				lowercase				,				lowercase								=      config_and_inputs
            lowercase								=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict






@require_flax
class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  List[Any]				    =      (
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      FlaxAlbertModelTester(self		)







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase								=      model_class_name.from_pretrained("""albert-base-v2"""		)
                  lowercase								=      model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)






@require_flax
class 					_A		(			unittest.TestCase      ):

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      FlaxAlbertModel.from_pretrained("""albert-base-v2"""		)
            lowercase								=      np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
            lowercase								=      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase		)[0]
            lowercase								=      (1, 11, 768)
            self.assertEqual(output.shape						,			__lowerCAmelCase		)
            lowercase								=      np.array(
                [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]		)

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




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase       :			int   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Optional[Any]   	={
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'mra'

      def __init__(		self						,			__lowerCAmelCase=5_0265						,			__lowerCAmelCase=768						,			__lowerCAmelCase=12						,			__lowerCAmelCase=12						,			__lowerCAmelCase=3072						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1E-5						,			__lowerCAmelCase="absolute"						,			__lowerCAmelCase=4						,			__lowerCAmelCase="full"						,			__lowerCAmelCase=0						,			__lowerCAmelCase=0						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0						,			__lowerCAmelCase=2						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(pad_token_id=__lowerCAmelCase						,			bos_token_id=__lowerCAmelCase						,			eos_token_id=__lowerCAmelCase						,			**__lowerCAmelCase		)

            lowercase								=      vocab_size
            lowercase								=      max_position_embeddings
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      initializer_range
            lowercase								=      type_vocab_size
            lowercase								=      layer_norm_eps
            lowercase								=      position_embedding_type
            lowercase								=      block_per_row
            lowercase								=      approx_mode
            lowercase								=      initial_prior_first_n_blocks
            lowercase								=      initial_prior_diagonal_n_blocks



