





'''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							:	Dict     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Tuple=13     ,		lowerCamelCase_							:	List[str]=7     ,		lowerCamelCase_							:	Optional[int]=True     ,		lowerCamelCase_							:	Union[str, Any]=True     ,		lowerCamelCase_							:	Tuple=True     ,		lowerCamelCase_							:	Tuple=True     ,		lowerCamelCase_							:	Any=99     ,		lowerCamelCase_							:	List[Any]=24     ,		lowerCamelCase_							:	Optional[int]=32     ,		lowerCamelCase_							:	int=5     ,		lowerCamelCase_							:	str=4     ,		lowerCamelCase_							:	Union[str, Any]=37     ,		lowerCamelCase_							:	Optional[int]="gelu"     ,		lowerCamelCase_							:	Optional[int]=0.1     ,		lowerCamelCase_							:	List[str]=0.1     ,		lowerCamelCase_							:	Union[str, Any]=5_12     ,		lowerCamelCase_							:	Any=16     ,		lowerCamelCase_							:	Optional[int]=2     ,		lowerCamelCase_							:	Optional[Any]=0.02     ,		lowerCamelCase_							:	List[str]=4     ,		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size		)

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

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

							SCREAMING_SNAKE_CASE				:							Optional[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 lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[str]						=	config_and_inputs
							SCREAMING_SNAKE_CASE				:							Tuple						=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict







@require_flax
class       UpperCamelCase__			(			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					True

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


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	FlaxElectraModelTester(self		)



					@slow
					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



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










'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''					)
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(f'''Key {key} not found'''					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		SCREAMING_SNAKE_CASE				:							Dict						=	int(input("""Enter the element to be searched:"""					)					)
		print(lowerCamelCase_					)
		search_in_a_sorted_matrix(lowerCamelCase_	,	len(lowerCamelCase_					)	,	len(mat[0]					)	,	lowerCamelCase_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()



