




"""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 							snake_case_ (				unittest.TestCase ):







			"""simple docstring"""




			def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=7					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_=9_9					,				lowerCamelCase_=2_4					,				lowerCamelCase_=3_2					,				lowerCamelCase_=5					,				lowerCamelCase_=4					,				lowerCamelCase_=3_7					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=0.1					,				lowerCamelCase_=0.1					,				lowerCamelCase_=5_1_2					,				lowerCamelCase_=1_6					,				lowerCamelCase_=2					,				lowerCamelCase_=0.02					,				lowerCamelCase_=4					,				)							->      Tuple:
							UpperCamelCase										=  parent
							UpperCamelCase										=  batch_size
							UpperCamelCase										=  seq_length
							UpperCamelCase										=  is_training
							UpperCamelCase										=  use_attention_mask
							UpperCamelCase										=  use_token_type_ids
							UpperCamelCase										=  use_labels
							UpperCamelCase										=  vocab_size
							UpperCamelCase										=  hidden_size
							UpperCamelCase										=  embedding_size
							UpperCamelCase										=  num_hidden_layers
							UpperCamelCase										=  num_attention_heads
							UpperCamelCase										=  intermediate_size
							UpperCamelCase										=  hidden_act
							UpperCamelCase										=  hidden_dropout_prob
							UpperCamelCase										=  attention_probs_dropout_prob
							UpperCamelCase										=  max_position_embeddings
							UpperCamelCase										=  type_vocab_size
							UpperCamelCase										=  type_sequence_label_size
							UpperCamelCase										=  initializer_range
							UpperCamelCase										=  num_choices







			def      UpperCAmelCase__ (		self)							->      List[Any]:
							UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size)

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

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

							UpperCamelCase										=  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)							->      int:
							UpperCamelCase										=  self.prepare_config_and_inputs()
							UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
							UpperCamelCase										=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict

@require_flax
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







			"""simple docstring"""




			A_										=					True

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







			def      UpperCAmelCase__ (		self)							->      Any:
							UpperCamelCase										=  FlaxElectraModelTester(self)







			@slow
			def      UpperCAmelCase__ (		self)							->      str:
							for model_class_name in self.all_model_classes:
											if model_class_name == FlaxElectraForMaskedLM:
															UpperCamelCase										=  model_class_name.from_pretrained('''google/electra-small-generator''')
											else:
															UpperCamelCase										=  model_class_name.from_pretrained('''google/electra-small-discriminator''')
											UpperCamelCase										=  model(np.ones((1, 1)))
											self.assertIsNotNone(lowerCamelCase_)





"""simple docstring"""







class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_=None)							->      Tuple:
       UpperCamelCase										=  data
       UpperCamelCase										=  None







   def __repr__(		self)							->      Union[str, Any]:
       UpperCamelCase										=  ''''''
       UpperCamelCase										=  self
       while temp:
           string_rep += F'<{temp.data}> ---> '
           UpperCamelCase										=  temp.next
       string_rep += "<END>"
       return string_rep





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    if not elements_list:
        raise Exception('''The Elements List is empty'''						)

    # Set first element as Head
    UpperCamelCase										=  Node(elements_list[0]						)
    UpperCamelCase										=  head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase										=  Node(_lowercase						)
        UpperCamelCase										=  current.next
    return head


SCREAMING_SNAKE_CASE_      =					[1, 3, 5, 32, 44, 12, 43]
print(f'List: {list_data}')
print('Creating Linked List from List.')
SCREAMING_SNAKE_CASE_      =					make_linked_list(list_data)
print('Linked List:')


print(linked_list)