






import unittest

import numpy as np

from transformers import ElectraConfig, 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():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )
class 					a					(				unittest.TestCase     ):



      """simple docstring"""


      def __init__(   self      :    Tuple	,   __lowercase      :    List[Any]	,   __lowercase      :    str=13	,   __lowercase      :    Optional[Any]=7	,   __lowercase      :    int=True	,   __lowercase      :    Union[str, Any]=True	,   __lowercase      :    Union[str, Any]=True	,   __lowercase      :    Any=True	,   __lowercase      :    Optional[Any]=99	,   __lowercase      :    List[Any]=24	,   __lowercase      :    int=32	,   __lowercase      :    int=5	,   __lowercase      :    Optional[int]=4	,   __lowercase      :    Optional[Any]=37	,   __lowercase      :    List[Any]="gelu"	,   __lowercase      :    Union[str, Any]=0.1	,   __lowercase      :    Tuple=0.1	,   __lowercase      :    int=512	,   __lowercase      :    Tuple=16	,   __lowercase      :    Optional[Any]=2	,   __lowercase      :    Dict=0.02	,   __lowercase      :    str=4	,   )							->   int:
        __UpperCAmelCase							:			List[Any]     =						parent
        __UpperCAmelCase							:			Optional[int]     =						batch_size
        __UpperCAmelCase							:			Dict     =						seq_length
        __UpperCAmelCase							:			Tuple     =						is_training
        __UpperCAmelCase							:			Dict     =						use_attention_mask
        __UpperCAmelCase							:			str     =						use_token_type_ids
        __UpperCAmelCase							:			Tuple     =						use_labels
        __UpperCAmelCase							:			str     =						vocab_size
        __UpperCAmelCase							:			Optional[int]     =						hidden_size
        __UpperCAmelCase							:			str     =						embedding_size
        __UpperCAmelCase							:			Tuple     =						num_hidden_layers
        __UpperCAmelCase							:			Optional[Any]     =						num_attention_heads
        __UpperCAmelCase							:			List[Any]     =						intermediate_size
        __UpperCAmelCase							:			str     =						hidden_act
        __UpperCAmelCase							:			Union[str, Any]     =						hidden_dropout_prob
        __UpperCAmelCase							:			Tuple     =						attention_probs_dropout_prob
        __UpperCAmelCase							:			Optional[int]     =						max_position_embeddings
        __UpperCAmelCase							:			Tuple     =						type_vocab_size
        __UpperCAmelCase							:			Dict     =						type_sequence_label_size
        __UpperCAmelCase							:			Tuple     =						initializer_range
        __UpperCAmelCase							:			List[Any]     =						num_choices
      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   List[str]:
        __UpperCAmelCase							:			Tuple     =						ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size      )

        __UpperCAmelCase							:			Optional[int]     =						None
        if self.use_attention_mask:
          __UpperCAmelCase							:			int     =						random_attention_mask([self.batch_size, self.seq_length]      )

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

        __UpperCAmelCase							:			str     =						ElectraConfig(
            vocab_size=self.vocab_size	,   hidden_size=self.hidden_size	,   embedding_size=self.embedding_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	,   initializer_range=self.initializer_range	,   )

        return config, input_ids, token_type_ids, attention_mask




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


@require_flax
class 					a					(				a_							, unittest.TestCase     ):



      """simple docstring"""


      a						:				Optional[int]							 =			True

      a						:				List[str]							 =			(
          (
              FlaxElectraModel,
              FlaxElectraForCausalLM,
              FlaxElectraForMaskedLM,
              FlaxElectraForPreTraining,
              FlaxElectraForTokenClassification,
              FlaxElectraForQuestionAnswering,
              FlaxElectraForMultipleChoice,
              FlaxElectraForSequenceClassification,
          )
          if is_flax_available()
          else ()
      )
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   int:
        __UpperCAmelCase							:			Union[str, Any]     =						FlaxElectraModelTester(self      )




      @slow
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Optional[int]:
        for model_class_name in self.all_model_classes:
          if model_class_name == FlaxElectraForMaskedLM:
            __UpperCAmelCase							:			Optional[int]     =						model_class_name.from_pretrained("""google/electra-small-generator"""      )
          else:
            __UpperCAmelCase							:			Optional[Any]     =						model_class_name.from_pretrained("""google/electra-small-discriminator"""      )
          __UpperCAmelCase							:			str     =						model(np.ones((1, 1)      )      )
          self.assertIsNotNone(_snake_case      )










class 					a					:



      """simple docstring"""


      def __init__(   self      :    int	,   __lowercase      :    int      )							->   None:
        __UpperCAmelCase							:			str     =						data
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None
        __UpperCAmelCase							:			BinaryTreeNode | None     =						None






def        lowerCamelCase__   (					__lowerCamelCase    :  BinaryTreeNode | None						,	__lowerCamelCase    :  int	):
  if node is None:
    __UpperCAmelCase							:			Optional[int]     =						BinaryTreeNode(__lowerCamelCase	)
    return node

  # binary search tree is not empty,
  # so we will insert it into the tree
  # if new_value is less than value of data in node,
  #  add it to left subtree and proceed recursively
  if new_value < node.data:
    __UpperCAmelCase							:			int     =						insert(node.left_child						,	__lowerCamelCase	)
  else:
    # if new_value is greater than value of data in node,
    #  add it to right subtree and proceed recursively
    __UpperCAmelCase							:			Dict     =						insert(node.right_child						,	__lowerCamelCase	)
  return node






def        lowerCamelCase__   (					__lowerCamelCase    :  None | BinaryTreeNode	):  # if node is None,return
  if node:
    __UpperCAmelCase							:			Optional[Any]     =						inorder(node.left_child	)
    __UpperCAmelCase							:			str     =						[*inorder_array, node.data]
    __UpperCAmelCase							:			Optional[Any]     =						inorder_array + inorder(node.right_child	)
  else:
    __UpperCAmelCase							:			Tuple     =						[]
  return inorder_array






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Any     =						insert(__lowerCamelCase						,	15	)
  insert(__lowerCamelCase						,	10	)
  insert(__lowerCamelCase						,	25	)
  insert(__lowerCamelCase						,	6	)
  insert(__lowerCamelCase						,	14	)
  insert(__lowerCamelCase						,	20	)
  insert(__lowerCamelCase						,	60	)
  return root






def        lowerCamelCase__   (					):
  # main function
  __UpperCAmelCase							:			int     =						make_tree()
  print("""Printing values of binary search tree in Inorder Traversal."""	)
  inorder(__lowerCamelCase	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


