


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 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """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 lowercase (		self: int		)       -> 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=lowerCamelCase__					,   initializer_range=self.initializer_range					,   )

              return config, input_ids, token_type_ids, attention_mask

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


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



       def lowercase (		self: int		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  self.prepare_config_and_inputs()
              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 							_UpperCamelCase					(	UpperCAmelCase__  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Tuple         =   True

       _UpperCamelCase				:					Tuple         =   (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )

       def lowercase (		self: Tuple		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  FlaxRobertaPreLayerNormModelTester(self		)



       @slow
       def lowercase (		self: Optional[int]		)       -> 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=lowerCamelCase__		)
                     UpperCamelCase_          =  model(np.ones((1, 1)		)		)
                     self.assertIsNotNone(lowerCamelCase__		)



@require_flax
class 							_UpperCamelCase					(	unittest.TestCase     ):

       @slow
       def lowercase (		self: Tuple		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,   from_pt=lowerCamelCase__		)

              UpperCamelCase_          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					,   dtype=jnp.intaa		)
              UpperCamelCase_          =  model(lowerCamelCase__		)[0]
              UpperCamelCase_          =  [1, 11, 50265]
              self.assertEqual(list(output.shape		)					,   lowerCamelCase__		)
              # compare the actual values for a slice.
              UpperCamelCase_          =  np.array(
                  [[[40.4880, 18.0199, -5.23_67], [-1.88_77, -4.08_85, 10.7085], [-2.26_13, -5.61_10, 7.26_65]]]					,   dtype=np.floataa		)
              self.assertTrue(np.allclose(output[:, :3, :3]					,   lowerCamelCase__					,   atol=1e-4		)		)



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




              """simple docstring"""


              UpperCamelCase_          =  FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,   from_pt=lowerCamelCase__		)

              UpperCamelCase_          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					,   dtype=jnp.intaa		)
              UpperCamelCase_          =  model(lowerCamelCase__		)[0]
              # compare the actual values for a slice.
              UpperCamelCase_          =  np.array(
                  [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]					,   dtype=np.floataa		)
              self.assertTrue(np.allclose(output[:, :3, :3]					,   lowerCamelCase__					,   atol=1e-4		)		)








import sys




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  len(UpperCamelCase_  )
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]

       for chain_length in range(2					,		UpperCamelCase_  ):
              for a in range(1					,		n - chain_length + 1  ):
                     UpperCamelCase_          =  a + chain_length - 1

                     UpperCamelCase_          =  sys.maxsize
                     for c in range(UpperCamelCase_					,		UpperCamelCase_  ):
                            UpperCamelCase_          =  (
                                matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                            )
                            if cost < matrix[a][b]:
                                   UpperCamelCase_          =  cost
                                   UpperCamelCase_          =  c
       return matrix, sol




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       if i == j:
              print("A" + str(UpperCamelCase_  )					,		end=" "  )
       else:
              print("("					,		end=" "  )
              print_optiomal_solution(UpperCamelCase_					,		UpperCamelCase_					,		optimal_solution[i][j]  )
              print_optiomal_solution(UpperCamelCase_					,		optimal_solution[i][j] + 1					,		UpperCamelCase_  )
              print(")"					,		end=" "  )




def  lowerCAmelCase_ (							)     ->		Any:
       UpperCamelCase_          =  [30, 35, 15, 5, 10, 20, 25]
       UpperCamelCase_          =  len(UpperCamelCase_  )
       # Size of matrix created from above array will be
       # 30*35 35*15 15*5 5*10 10*20 20*25
       UpperCamelCase_       , UpperCamelCase_          =  matrix_chain_order(UpperCamelCase_  )

       print("No. of Operation required: " + str(matrix[1][n - 1]  )  )
       print_optiomal_solution(UpperCamelCase_					,		1					,		n - 1  )


if __name__ == "__main__":
     main()

