



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 	snake_case_			(unittest.TestCase	):




       """simple docstring"""






       def __init__(  self  ,lowercase  ,lowercase=13  ,lowercase=7  ,lowercase=True  ,lowercase=True  ,lowercase=True  ,lowercase=True  ,lowercase=99  ,lowercase=24  ,lowercase=32  ,lowercase=5  ,lowercase=4  ,lowercase=37  ,lowercase="gelu"  ,lowercase=0.1  ,lowercase=0.1  ,lowercase=512  ,lowercase=16  ,lowercase=2  ,lowercase=0.02  ,lowercase=4  ,):

             """simple docstring"""





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



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						int					   =		ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size)

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

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

             UpperCAmelCase_	:						Tuple					   =		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):

             """simple docstring"""





             UpperCAmelCase_	:						List[str]					   =		self.prepare_config_and_inputs()
             UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Tuple					   =		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 	snake_case_			(lowercase__			,			unittest.TestCase	):




       """simple docstring"""






       _lowerCamelCase           =					True

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



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Tuple					   =		FlaxElectraModelTester(self)



       @slow
       def 			A_ (  self):

             """simple docstring"""





             for model_class_name in self.all_model_classes:
                   if model_class_name == FlaxElectraForMaskedLM:
                         UpperCAmelCase_	:						List[Any]					   =		model_class_name.from_pretrained("google/electra-small-generator")
                   else:
                         UpperCAmelCase_	:						Tuple					   =		model_class_name.from_pretrained("google/electra-small-discriminator")
                   UpperCAmelCase_	:						int					   =		model(np.ones((1, 1)))
                   self.assertIsNotNone(lowercase)





import heapq







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  str:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		[(0, start)]  # cost from start node,end node
      UpperCAmelCase_	:						List[Any]					   =		set()
      while heap:
            ((UpperCAmelCase_)   ,   (UpperCAmelCase_))	:						str					   =		heapq.heappop(__snake_case     )
            if u in visited:
                  continue
            visited.add(__snake_case     )
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  UpperCAmelCase_	:						Tuple					   =		cost + c
                  heapq.heappush(__snake_case       ,	(next_item, v)     )
      return -1


__lowerCamelCase								=      {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
__lowerCamelCase								=      {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

__lowerCamelCase								=      dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

__lowerCamelCase								=      dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()
