'''simple docstring'''

import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def __init__(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Dict=13      ,       SCREAMING_SNAKE_CASE__   :	Tuple=7      ,       SCREAMING_SNAKE_CASE__   :	List[str]=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	int=True      ,       SCREAMING_SNAKE_CASE__   :	Dict=True      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=99      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=32      ,       SCREAMING_SNAKE_CASE__   :	Dict=5      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=4      ,       SCREAMING_SNAKE_CASE__   :	List[str]=37      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Any=0.1      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[str]=5_12      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=16      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=2      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=4      ,       )  ->    Optional[int]:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						seq_length
  __lowerCAmelCase											=						is_training
  __lowerCAmelCase											=						use_attention_mask
  __lowerCAmelCase											=						use_token_type_ids
  __lowerCAmelCase											=						use_labels
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						max_position_embeddings
  __lowerCAmelCase											=						type_vocab_size
  __lowerCAmelCase											=						type_sequence_label_size
  __lowerCAmelCase											=						initializer_range
  __lowerCAmelCase											=						num_choices
 def a					(      self   :	Tuple							)  ->    int:
  __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

  __lowerCAmelCase											=						None
  if self.use_attention_mask:
   __lowerCAmelCase											=						random_attention_mask([self.batch_size, self.seq_length]							)

  __lowerCAmelCase											=						None
  if self.use_token_type_ids:
   __lowerCAmelCase											=						ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size							)

  __lowerCAmelCase											=						AlbertConfig(
      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 a					(      self   :	Any							)  ->    Optional[int]:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						config_and_inputs
  __lowerCAmelCase											=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
  return config, inputs_dict







@require_flax
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 def a					(      self   :	Optional[Any]							)  ->    List[Any]:
  __lowerCAmelCase											=						FlaxAlbertModelTester(self							)





 @slow
 def a					(      self   :	Optional[int]							)  ->    str:
  for model_class_name in self.all_model_classes:
   __lowerCAmelCase											=						model_class_name.from_pretrained("""albert-base-v2"""							)
   __lowerCAmelCase											=						model(np.ones((1, 1)							)							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)







@require_flax
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @slow
 def a					(      self   :	str							)  ->    Optional[int]:
  __lowerCAmelCase											=						FlaxAlbertModel.from_pretrained("""albert-base-v2"""							)
  __lowerCAmelCase											=						np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]							)
  __lowerCAmelCase											=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__      ,       attention_mask=SCREAMING_SNAKE_CASE__							)[0]
  __lowerCAmelCase											=						(1, 11, 7_68)
  self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						np.array(
      [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]							)

  self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-4							)							)







'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






