

"""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        _UpperCamelCase	(						unittest.TestCase	):


  """simple docstring"""





  def __init__( self					: str       ,       snake_case					: Optional[Any]       ,       snake_case					: Any=13       ,       snake_case					: Tuple=7       ,       snake_case					: int=True       ,       snake_case					: Union[str, Any]=True       ,       snake_case					: Optional[Any]=True       ,       snake_case					: List[str]=True       ,       snake_case					: Optional[Any]=99       ,       snake_case					: Dict=24       ,       snake_case					: Union[str, Any]=32       ,       snake_case					: List[str]=5       ,       snake_case					: List[Any]=4       ,       snake_case					: Any=37       ,       snake_case					: Union[str, Any]="gelu"       ,       snake_case					: Optional[Any]=0.1       ,       snake_case					: str=0.1       ,       snake_case					: str=512       ,       snake_case					: List[str]=16       ,       snake_case					: Optional[Any]=2       ,       snake_case					: Optional[int]=0.02       ,       snake_case					: Dict=4       ,       )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		Dict        =     parent
         __magic_name__			:		List[str]        =     batch_size
         __magic_name__			:		Tuple        =     seq_length
         __magic_name__			:		List[str]        =     is_training
         __magic_name__			:		Optional[Any]        =     use_attention_mask
         __magic_name__			:		Optional[int]        =     use_token_type_ids
         __magic_name__			:		Tuple        =     use_labels
         __magic_name__			:		Optional[Any]        =     vocab_size
         __magic_name__			:		List[Any]        =     hidden_size
         __magic_name__			:		int        =     embedding_size
         __magic_name__			:		Union[str, Any]        =     num_hidden_layers
         __magic_name__			:		str        =     num_attention_heads
         __magic_name__			:		Optional[Any]        =     intermediate_size
         __magic_name__			:		Any        =     hidden_act
         __magic_name__			:		Union[str, Any]        =     hidden_dropout_prob
         __magic_name__			:		Dict        =     attention_probs_dropout_prob
         __magic_name__			:		int        =     max_position_embeddings
         __magic_name__			:		List[str]        =     type_vocab_size
         __magic_name__			:		Union[str, Any]        =     type_sequence_label_size
         __magic_name__			:		Dict        =     initializer_range
         __magic_name__			:		List[str]        =     num_choices



  def 							_UpperCAmelCase      ( self					: int      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size      )

         __magic_name__			:		List[Any]        =     None
         if self.use_attention_mask:
                __magic_name__			:		Optional[Any]        =     random_attention_mask([self.batch_size, self.seq_length]      )

         __magic_name__			:		List[Any]        =     None
         if self.use_token_type_ids:
                __magic_name__			:		Union[str, Any]        =     ids_tensor([self.batch_size, self.seq_length]       ,       self.type_vocab_size      )

         __magic_name__			:		Any        =     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					: Optional[int]      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Any        =     self.prepare_config_and_inputs()
         __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							__magic_name__			:		int        =     config_and_inputs
         __magic_name__			:		Optional[Any]        =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict

@require_flax
class        _UpperCamelCase	(						lowerCamelCase__   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    True

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



  def 							_UpperCAmelCase      ( self					: List[str]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     FlaxElectraModelTester(self      )




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


         '''simple docstring'''





         for model_class_name in self.all_model_classes:
                if model_class_name == FlaxElectraForMaskedLM:
                       __magic_name__			:		Optional[Any]        =     model_class_name.from_pretrained('''google/electra-small-generator'''      )
                else:
                       __magic_name__			:		int        =     model_class_name.from_pretrained('''google/electra-small-discriminator'''      )
                __magic_name__			:		Dict        =     model(np.ones((1, 1)      )      )
                self.assertIsNotNone(snake_case      )



"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
