




def 		snake_case_							(_a			:							Any					,     _a			:							Tuple					,     _a			:							List[Any]  ):

    def update_area_of_max_square(_a			:							Union[str, Any]					,     _a			:							Any  ) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        UpperCAmelCase     =	update_area_of_max_square(_a					,     col + 1  )
        UpperCAmelCase     =	update_area_of_max_square(row + 1					,     col + 1  )
        UpperCAmelCase     =	update_area_of_max_square(row + 1					,     _a  )

        if mat[row][col]:
            UpperCAmelCase     =	1 + min([right, diagonal, down]  )
            UpperCAmelCase     =	max(largest_square_area[0]					,     _a  )
            return sub_problem_sol
        else:
            return 0

    UpperCAmelCase     =	[0]
    update_area_of_max_square(0					,     0  )
    return largest_square_area[0]





def 		snake_case_							(_a			:							List[str]					,     _a			:							Any					,     _a			:							List[Any]  ):

    def update_area_of_max_square_using_dp_array(
        _a			:							Optional[Any]					,     _a			:							Any					,     _a			:							str  ) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        UpperCAmelCase     =	update_area_of_max_square_using_dp_array(_a					,     col + 1					,     _a  )
        UpperCAmelCase     =	update_area_of_max_square_using_dp_array(row + 1					,     col + 1					,     _a  )
        UpperCAmelCase     =	update_area_of_max_square_using_dp_array(row + 1					,     _a					,     _a  )

        if mat[row][col]:
            UpperCAmelCase     =	1 + min([right, diagonal, down]  )
            UpperCAmelCase     =	max(largest_square_area[0]					,     _a  )
            UpperCAmelCase     =	sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    UpperCAmelCase     =	[0]
    UpperCAmelCase     =	[[-1] * cols for _ in range(_a  )]
    update_area_of_max_square_using_dp_array(0					,     0					,     _a  )

    return largest_square_area[0]





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							str					,     _a			:							List[str]  ):
    UpperCAmelCase     =	[[0] * (cols + 1) for _ in range(rows + 1  )]
    UpperCAmelCase     =	0
    for row in range(rows - 1					,     -1					,     -1  ):
        for col in range(cols - 1					,     -1					,     -1  ):
            UpperCAmelCase     =	dp_array[row][col + 1]
            UpperCAmelCase     =	dp_array[row + 1][col + 1]
            UpperCAmelCase     =	dp_array[row + 1][col]

            if mat[row][col] == 1:
                UpperCAmelCase     =	1 + min(_a					,     _a					,     _a  )
                UpperCAmelCase     =	max(dp_array[row][col]					,     _a  )
            else:
                UpperCAmelCase     =	0

    return largest_square_area





def 		snake_case_							(_a			:							Tuple					,     _a			:							List[str]					,     _a			:							Optional[Any]  ):
    UpperCAmelCase     =	[0] * (cols + 1)
    UpperCAmelCase     =	[0] * (cols + 1)
    UpperCAmelCase     =	0
    for row in range(rows - 1					,     -1					,     -1  ):
        for col in range(cols - 1					,     -1					,     -1  ):
            UpperCAmelCase     =	current_row[col + 1]
            UpperCAmelCase     =	next_row[col + 1]
            UpperCAmelCase     =	next_row[col]

            if mat[row][col] == 1:
                UpperCAmelCase     =	1 + min(_a					,     _a					,     _a  )
                UpperCAmelCase     =	max(current_row[col]					,     _a  )
            else:
                UpperCAmelCase     =	0
        UpperCAmelCase     =	current_row

    return largest_square_area


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))










'''simple docstring'''







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



           '''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 					A   (			self		:			Any       ):



           '''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=lowercase     ,       initializer_range=self.initializer_range     ,       )

           return config, input_ids, token_type_ids, attention_mask
       def 					A   (			self		:			List[Any]       ):



           '''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 					A   (			self		:			Optional[Any]       ):



           '''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						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      True

       __a       :  str            =      (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModelTester(self       )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           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     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	[1, 11, 50_265]
           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 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 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       )       )




