


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 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=5      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=4      ,   )			->	Tuple:




									"""simple docstring"""





									_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      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_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=_SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )

									return config, input_ids, token_type_ids, attention_mask
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_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 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Dict           =     True

		_a     :  List[Any]           =     (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      FlaxRobertaPreLayerNormModelTester(self						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									for model_class_name in self.all_model_classes:
																_UpperCAmelCase					=      model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      model(np.ones((1, 1)						)						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



@require_flax
class 		__a				(    unittest.TestCase      ):
		@slow
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      ,   dtype=jnp.intaa						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      [1, 11, 50265]
									self.assertEqual(list(output.shape						)      ,   _SCREAMING_SNAKE_CASE						)
									# 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]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,   from_pt=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      ,   dtype=jnp.intaa						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)[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]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
