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 		_lowerCamelCase		( 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			,			)							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[Any]=   parent
     SCREAMING_SNAKE_CASE__: List[Any]=   batch_size
     SCREAMING_SNAKE_CASE__: Optional[int]=   seq_length
     SCREAMING_SNAKE_CASE__: Optional[Any]=   is_training
     SCREAMING_SNAKE_CASE__: Optional[int]=   use_attention_mask
     SCREAMING_SNAKE_CASE__: Any=   use_token_type_ids
     SCREAMING_SNAKE_CASE__: Optional[Any]=   use_labels
     SCREAMING_SNAKE_CASE__: List[str]=   vocab_size
     SCREAMING_SNAKE_CASE__: str=   hidden_size
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: Dict=   num_attention_heads
     SCREAMING_SNAKE_CASE__: Optional[int]=   intermediate_size
     SCREAMING_SNAKE_CASE__: Any=   hidden_act
     SCREAMING_SNAKE_CASE__: List[str]=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__: List[Any]=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__: str=   max_position_embeddings
     SCREAMING_SNAKE_CASE__: List[Any]=   type_vocab_size
     SCREAMING_SNAKE_CASE__: List[Any]=   type_sequence_label_size
     SCREAMING_SNAKE_CASE__: Dict=   initializer_range
     SCREAMING_SNAKE_CASE__: Tuple=   num_choices
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Any=   ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size )

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

     SCREAMING_SNAKE_CASE__: Optional[Any]=   None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE__: Dict=   ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size )

     SCREAMING_SNAKE_CASE__: 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=lowerCamelCase_			,			initializer_range=self.initializer_range			,			)

     return config, input_ids, token_type_ids, attention_mask
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: int=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__: Optional[Any]=   config_and_inputs
     SCREAMING_SNAKE_CASE__: List[Any]=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict



    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__: Any=   config_and_inputs

     SCREAMING_SNAKE_CASE__: Optional[Any]=   True
     SCREAMING_SNAKE_CASE__: Optional[int]=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
     SCREAMING_SNAKE_CASE__: Any=   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 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   True

    __a						=   (
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   FlaxRobertaPreLayerNormModelTester(self )



    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:



     for model_class_name in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Optional[int]=   model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,			from_pt=lowerCamelCase_ )
      SCREAMING_SNAKE_CASE__: List[Any]=   model(np.ones((1, 1) ) )
      self.assertIsNotNone(lowerCamelCase_ )






@require_flax
class 		_lowerCamelCase		( unittest.TestCase     ):
    @slow
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Tuple=   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,			from_pt=lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: Optional[int]=   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			,			dtype=jnp.intaa )
     SCREAMING_SNAKE_CASE__: Any=   model(lowerCamelCase_ )[0]
     SCREAMING_SNAKE_CASE__: Optional[Any]=   [1, 11, 50265]
     self.assertEqual(list(output.shape )			,			lowerCamelCase_ )
     # compare the actual values for a slice.
     SCREAMING_SNAKE_CASE__: int=   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]			,			lowerCamelCase_			,			atol=1e-4 ) )



    @slow
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: str=   FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''			,			from_pt=lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: List[str]=   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			,			dtype=jnp.intaa )
     SCREAMING_SNAKE_CASE__: List[str]=   model(lowerCamelCase_ )[0]
     # compare the actual values for a slice.
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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]			,			lowerCamelCase_			,			atol=1e-4 ) )


'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      input_matrix.conj().T     )

   # Set convergence to False. Will define convergence when we exceed max_iterations
   # or when we have small changes from one iteration to next.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

   while not convergence:
      # Multiple matrix by the vector.
      UpperCAmelCase__				:					str 					=	np.dot(_A	,      _A     )
      # Normalize the resulting output vector.
      UpperCAmelCase__				:					int 					=	w / np.linalg.norm(_A     )
      # Find rayleigh quotient
      # (faster than usual b/c we know vector is normalized already)
      UpperCAmelCase__				:					int 					=	vector.conj().T if is_complex else vector.T
      UpperCAmelCase__				:					Any 					=	np.dot(_A	,      np.dot(_A	,      _A     )     )

      # Check convergence.
      UpperCAmelCase__				:					List[Any] 					=	np.abs(lambda_ - lambda_previous     ) / lambda_
      iterations += 1

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

      # Get eigenvalues and eigenvectors using built-in numpy
      # eigh (eigh used for symmetric or hermetian matrices).
      UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	np.linalg.eigh(_A     )
      # Last eigenvalue is the maximum one.
      UpperCAmelCase__				:					Dict 					=	eigen_values[-1]
      # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
      UpperCAmelCase__				:					Union[str, Any] 					=	eigen_vectors[:, -1]

      # Check our implementation and numpy gives close answers.
      assert np.abs(eigen_value - eigen_value_max     ) <= 1e-6
      # Take absolute values element wise of each eigenvector.
      # as they are only unique to a minus sign.
      assert np.linalg.norm(np.abs(_A     ) - np.abs(_A     )     ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




