





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)

class 							__SCREAMING_SNAKE_CASE       (  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=0.02	,			__lowerCAmelCase=4	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      seq_length
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_attention_mask
														UpperCamelCase__					  =      use_token_type_ids
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      hidden_size
														UpperCamelCase__					  =      num_hidden_layers
														UpperCamelCase__					  =      num_attention_heads
														UpperCamelCase__					  =      intermediate_size
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      type_vocab_size
														UpperCamelCase__					  =      type_sequence_label_size
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      num_choices






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size	)

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

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

														UpperCamelCase__					  =      RobertaPreLayerNormConfig(
														    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=lowercase_	,			initializer_range=self.initializer_range	,			)

														return config, input_ids, token_type_ids, attention_mask






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







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.prepare_config_and_inputs()
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      config_and_inputs

														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
														UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2	)

														return (
														    config,
														    input_ids,
														    token_type_ids,
														    encoder_hidden_states,
														    encoder_attention_mask,
														)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 							__SCREAMING_SNAKE_CASE       (  __a ,		unittest.TestCase							):
							snake_case  :   int      =							True

							snake_case  :   Dict      =							(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      FlaxRobertaPreLayerNormModelTester(self	)







							@slow
							def 					_lowerCamelCase		(    self	):


														for model_class_name in self.all_model_classes:
																					UpperCamelCase__					  =      model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,			from_pt=lowercase_	)
																					UpperCamelCase__					  =      model(np.ones((1, 1)	)	)
																					self.assertIsNotNone(lowercase_	)






@require_flax
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,			from_pt=lowercase_	)

														UpperCamelCase__					  =      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,			dtype=jnp.intaa	)
														UpperCamelCase__					  =      model(lowercase_	)[0]
														UpperCamelCase__					  =      [1, 11, 50265]
														self.assertEqual(list(output.shape	)	,			lowercase_	)
														# compare the actual values for a slice.
														UpperCamelCase__					  =      np.array(
														    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]	,			dtype=np.floataa	)
														self.assertTrue(np.allclose(output[:, :3, :3]	,			lowercase_	,			atol=1E-4	)	)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,			from_pt=lowercase_	)

														UpperCamelCase__					  =      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,			dtype=jnp.intaa	)
														UpperCamelCase__					  =      model(lowercase_	)[0]
														# compare the actual values for a slice.
														UpperCamelCase__					  =      np.array(
														    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]	,			dtype=np.floataa	)
														self.assertTrue(np.allclose(output[:, :3, :3]	,			lowercase_	,			atol=1E-4	)	)





'''simple docstring'''


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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


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

    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

