





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     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     def __init__(							self				:List[Any]	,					snake_case				:Union[str, Any]	,					snake_case				:List[Any]=13	,					snake_case				:Tuple=7	,					snake_case				:List[Any]=True	,					snake_case				:int=True	,					snake_case				:Union[str, Any]=True	,					snake_case				:Optional[Any]=True	,					snake_case				:List[str]=99	,					snake_case				:str=32	,					snake_case				:Union[str, Any]=5	,					snake_case				:int=4	,					snake_case				:Optional[Any]=37	,					snake_case				:Optional[int]="gelu"	,					snake_case				:Any=0.1	,					snake_case				:List[str]=0.1	,					snake_case				:str=512	,					snake_case				:Optional[Any]=16	,					snake_case				:Union[str, Any]=2	,					snake_case				:Any=0.02	,					snake_case				:Union[str, Any]=4	,					):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : Tuple								=	ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size     )

        A_    : Optional[int]								=	None
        if self.use_attention_mask:
           A_    : Union[str, Any]								=	random_attention_mask([self.batch_size, self.seq_length]     )

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

        A_    : Union[str, Any]								=	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=UpperCamelCase_	,					initializer_range=self.initializer_range	,					)

        return config, input_ids, token_type_ids, attention_mask



     def   SCREAMING_SNAKE_CASE						(							self				:int     ):





        '''simple docstring'''




        A_    : List[str]								=	self.prepare_config_and_inputs()
        A_    : Optional[Any]								=	config_and_inputs
        A_    : Optional[Any]								=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict





     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : int								=	self.prepare_config_and_inputs()
        A_    : Tuple								=	config_and_inputs

        A_    : str								=	True
        A_    : Optional[Any]								=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        A_    : str								=	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     __magic_name__     (							a__			, unittest.TestCase							):





     """simple docstring"""



     __UpperCamelCase    			=   True

     __UpperCamelCase    			=   (
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Any								=	FlaxRobertaPreLayerNormModelTester(self     )





     @slow
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        for model_class_name in self.all_model_classes:
           A_    : Optional[int]								=	model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,					from_pt=UpperCamelCase_     )
           A_    : int								=	model(np.ones((1, 1)     )     )
           self.assertIsNotNone(UpperCamelCase_     )




@require_flax
class     __magic_name__     (							unittest.TestCase							):





     """simple docstring"""



     @slow
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : str								=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,					from_pt=UpperCamelCase_     )

        A_    : Any								=	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	,					dtype=jnp.intaa     )
        A_    : Union[str, Any]								=	model(UpperCamelCase_     )[0]
        A_    : str								=	[1, 11, 50_265]
        self.assertEqual(list(output.shape     )	,					UpperCamelCase_     )
        # compare the actual values for a slice.
        A_    : Optional[Any]								=	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]	,					UpperCamelCase_	,					atol=1e-4     )     )





     @slow
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : Dict								=	FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,					from_pt=UpperCamelCase_     )

        A_    : str								=	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	,					dtype=jnp.intaa     )
        A_    : str								=	model(UpperCamelCase_     )[0]
        # compare the actual values for a slice.
        A_    : str								=	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]	,					UpperCamelCase_	,					atol=1e-4     )     )




"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


