


'''simple docstring'''







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  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""


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


    def    A    (  self			:							int):
           _A     :	Union[str, Any]      =					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size)

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

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

           _A     :	Dict      =					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    A    (  self			:							Dict):
           _A     :	Dict      =					self.prepare_config_and_inputs()
           _A  , _A  , _A  , _A     :	List[Any]      =					config_and_inputs
           _A     :	List[Any]      =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
           return config, inputs_dict




@require_flax
class  __lowerCamelCase (       a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    True

    a													=    (
        (
            FlaxElectraModel,
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForPreTraining,
            FlaxElectraForTokenClassification,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForMultipleChoice,
            FlaxElectraForSequenceClassification,
        )
        if is_flax_available()
        else ()
    )


    def    A    (  self			:							Any):
           _A     :	str      =					FlaxElectraModelTester(self)


    @slow
    def    A    (  self			:							Dict):
           for model_class_name in self.all_model_classes:
                  if model_class_name == FlaxElectraForMaskedLM:
                         _A     :	Union[str, Any]      =					model_class_name.from_pretrained('google/electra-small-generator')
                  else:
                         _A     :	Dict      =					model_class_name.from_pretrained('google/electra-small-discriminator')
                  _A     :	Tuple      =					model(np.ones((1, 1)))
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)










'''simple docstring'''







import math






def       lowerCAmelCase__       (				lowerCamelCase     :			int   ):
       _A     :	int      =					math.log(2  ,10   ) * number
       _A     :	List[Any]      =					round((log_number - int(lowerCamelCase   ))  ,15   )

       return difference






def       lowerCAmelCase__       (				lowerCamelCase     :			int = 678910   ):
       _A     :	List[str]      =					90
       _A     :	List[Any]      =					0

       _A     :	List[str]      =					math.log(1.23  ,10   )
       _A     :	Any      =					math.log(1.24  ,10   )
       _A     :	Union[str, Any]      =					0

       while position < number:
              _A     :	Dict      =					log_difference(lowerCamelCase   )

              if difference >= upper_limit:
                     power_iterator += 93

              elif difference < lower_limit:
                     power_iterator += 196

              else:
                     _A     :	str      =					power_iterator
                     power_iterator += 196
                     position += 1

       return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






