



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   ):


						def __init__( self						:  Optional[int]      ,       _a						:  Union[str, Any]      ,       _a						:  List[Any]=1_3      ,       _a						:  List[Any]=7      ,       _a						:  int=True      ,       _a						:  str=True      ,       _a						:  Dict=True      ,       _a						:  str=True      ,       _a						:  str=9_9      ,       _a						:  int=2_4      ,       _a						:  Optional[Any]=3_2      ,       _a						:  List[str]=5      ,       _a						:  Optional[int]=4      ,       _a						:  str=3_7      ,       _a						:  str="gelu"      ,       _a						:  str=0.1      ,       _a						:  Tuple=0.1      ,       _a						:  int=5_1_2      ,       _a						:  str=1_6      ,       _a						:  Optional[Any]=2      ,       _a						:  Optional[Any]=0.0_2      ,       _a						:  Optional[Any]=4      ,       ):
													a__:       Optional[Any]											=parent
													a__:       str											=batch_size
													a__:       Any											=seq_length
													a__:       List[Any]											=is_training
													a__:       Optional[Any]											=use_attention_mask
													a__:       Any											=use_token_type_ids
													a__:       List[Any]											=use_labels
													a__:       List[str]											=vocab_size
													a__:       int											=hidden_size
													a__:       str											=embedding_size
													a__:       Optional[int]											=num_hidden_layers
													a__:       List[Any]											=num_attention_heads
													a__:       Any											=intermediate_size
													a__:       Optional[int]											=hidden_act
													a__:       Optional[int]											=hidden_dropout_prob
													a__:       Dict											=attention_probs_dropout_prob
													a__:       int											=max_position_embeddings
													a__:       Optional[Any]											=type_vocab_size
													a__:       Union[str, Any]											=type_sequence_label_size
													a__:       int											=initializer_range
													a__:       Union[str, Any]											=num_choices


						def   _lowerCamelCase			( self						:  str			):
													a__:       int											=ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

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

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

													a__:       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   _lowerCamelCase			( self						:  str			):
													a__:       Union[str, Any]											=self.prepare_config_and_inputs()
													a__:       Tuple											=config_and_inputs
													a__:       int											={"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   ):
						_lowerCAmelCase								= True

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


						def   _lowerCamelCase			( self						:  str			):
													a__:       List[str]											=FlaxElectraModelTester(self			)







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











def 						__lowerCamelCase       (      __magic_name__	:  list     , __magic_name__	:  int						):
							for index, item in enumerate(__magic_name__						):
														if item == target:
																					return index
							return -1





def 						__lowerCamelCase       (      __magic_name__	:  list     , __magic_name__	:  int     , __magic_name__	:  int     , __magic_name__	:  int						):
							if not (0 <= high < len(__magic_name__						) and 0 <= low < len(__magic_name__						)):
														raise Exception("Invalid upper or lower bound!"						)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(__magic_name__     , low + 1     , high - 1     , __magic_name__						)


if __name__ == "__main__":
		__UpperCAmelCase						  =     input('''Enter numbers separated by comma:\n''').strip()
		__UpperCAmelCase						  =     [int(item.strip()) for item in user_input.split(''',''')]

		__UpperCAmelCase						  =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__UpperCAmelCase						  =     linear_search(sequence, target)
		if result != -1:
				print(f"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(f"""{target} was not found in {sequence}""")





