


def        UpperCamelCase_(				_A   :str  )->		bool:
			UpperCamelCase__					=       [int(_A  ) for i in ip_va_address.split("."  ) if i.isdigit()]
			return len(_A  ) == 4 and all(0 <= int(_A  ) <= 2_54 for octet in octets  )


if __name__ == "__main__":
						__UpperCamelCase    		=       input().strip()
						__UpperCamelCase    		=       'valid' if is_ip_va_address_valid(ip) else 'invalid'
						print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')






import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    MegatronBertForCausalLM,
						    MegatronBertForMaskedLM,
						    MegatronBertForMultipleChoice,
						    MegatronBertForNextSentencePrediction,
						    MegatronBertForPreTraining,
						    MegatronBertForQuestionAnswering,
						    MegatronBertForSequenceClassification,
						    MegatronBertForTokenClassification,
						    MegatronBertModel,
						)







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=7      ,						snake_case=True      ,						snake_case=True      ,						snake_case=True      ,						snake_case=True      ,						snake_case=99      ,						snake_case=64      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=16      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=3      ,						snake_case=4      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       seq_length
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_input_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_labels
						UpperCamelCase__					=       num_choices
						UpperCamelCase__					=       scope




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size     )

						UpperCamelCase__					=       None
						if self.use_input_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__					=       None
						UpperCamelCase__					=       None
						UpperCamelCase__					=       None
						if self.use_labels:
									UpperCamelCase__					=       ids_tensor([self.batch_size]      ,						self.type_sequence_label_size     )
									UpperCamelCase__					=       ids_tensor([self.batch_size, self.seq_length]      ,						self.num_labels     )
									UpperCamelCase__					=       ids_tensor([self.batch_size]      ,						self.num_choices     )

						UpperCamelCase__					=       self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return MegatronBertConfig(
						    vocab_size=self.vocab_size      ,						hidden_size=self.hidden_size      ,						num_hidden_layers=self.num_hidden_layers      ,						num_attention_heads=self.num_attention_heads      ,						intermediate_size=self.intermediate_size      ,						embedding_size=self.embedding_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      ,						is_decoder=snake_case      ,						initializer_range=self.initializer_range      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case     )
						UpperCamelCase__					=       model(snake_case      ,						token_type_ids=snake_case     )
						UpperCamelCase__					=       model(snake_case     )

						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)     )
						self.parent.assertEqual(result.pooler_output.shape      ,						(self.batch_size, self.hidden_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertForMaskedLM(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertForCausalLM(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertForNextSentencePrediction(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(
						    snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case      ,						)
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, 2)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertForPreTraining(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(
						    snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case      ,						next_sentence_label=snake_case      ,						)
						self.parent.assertEqual(result.prediction_logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)     )
						self.parent.assertEqual(result.seq_relationship_logits.shape      ,						(self.batch_size, 2)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertForQuestionAnswering(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(
						    snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						start_positions=snake_case      ,						end_positions=snake_case      ,						)
						self.parent.assertEqual(result.start_logits.shape      ,						(self.batch_size, self.seq_length)     )
						self.parent.assertEqual(result.end_logits.shape      ,						(self.batch_size, self.seq_length)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.num_labels
						UpperCamelCase__					=       MegatronBertForSequenceClassification(snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.num_labels
						UpperCamelCase__					=       MegatronBertForTokenClassification(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case     )
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.num_choices
						UpperCamelCase__					=       MegatronBertForMultipleChoice(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       input_ids.unsqueeze(1     ).expand(-1      ,						self.num_choices      ,						-1     ).contiguous()
						UpperCamelCase__					=       token_type_ids.unsqueeze(1     ).expand(-1      ,						self.num_choices      ,						-1     ).contiguous()
						UpperCamelCase__					=       input_mask.unsqueeze(1     ).expand(-1      ,						self.num_choices      ,						-1     ).contiguous()
						UpperCamelCase__					=       model(
						    snake_case      ,						attention_mask=snake_case      ,						token_type_ids=snake_case      ,						labels=snake_case      ,						)
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_choices)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						(
						    (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      (
						    UpperCamelCase__
						),      
						)					=       config_and_inputs
						UpperCamelCase__					=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		(
			    (
			        MegatronBertModel,
			        MegatronBertForMaskedLM,
			        MegatronBertForCausalLM,
			        MegatronBertForMultipleChoice,
			        MegatronBertForNextSentencePrediction,
			        MegatronBertForPreTraining,
			        MegatronBertForQuestionAnswering,
			        MegatronBertForSequenceClassification,
			        MegatronBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase		:					int    =		(
			    {
			        'feature-extraction': MegatronBertModel,
			        'fill-mask': MegatronBertForMaskedLM,
			        'question-answering': MegatronBertForQuestionAnswering,
			        'text-classification': MegatronBertForSequenceClassification,
			        'text-generation': MegatronBertForCausalLM,
			        'token-classification': MegatronBertForTokenClassification,
			        'zero-shot': MegatronBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			_UpperCamelCase		:					Optional[Any]    =		True
			# test_resize_embeddings = False
			_UpperCamelCase		:					List[Any]    =		False




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       super()._prepare_for_class(snake_case      ,						snake_case      ,						return_labels=snake_case     )

						if return_labels:
									if model_class in get_values(snake_case     ):
												UpperCamelCase__					=       torch.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length)      ,						dtype=torch.long      ,						device=snake_case     )
												UpperCamelCase__					=       torch.zeros(
												    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=snake_case     )
						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       MegatronBertModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_masked_lm(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_pretraining(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_question_answering(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*snake_case     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_megatron_bert_for_token_classification(*snake_case     )



def        UpperCamelCase_(				_A   :List[Any]  )->		Optional[Any]:
			return torch.tensor(
			    _A				,     dtype=torch.long				,     device=_A				,     )


__UpperCamelCase    		=       1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
class        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			@slow
			@unittest.skip("Model is not available."     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       "nvidia/megatron-bert-uncased-345m"
						if "MYDIR" in os.environ:
									UpperCamelCase__					=       os.path.join(os.environ["MYDIR"]      ,						snake_case     )
						UpperCamelCase__					=       MegatronBertModel.from_pretrained(snake_case     )
						model.to(snake_case     )
						model.half()
						UpperCamelCase__					=       _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]     )
						with torch.no_grad():
									UpperCamelCase__					=       model(snake_case     )[0]
						UpperCamelCase__					=       torch.Size((1, 9, 1024)     )
						self.assertEqual(output.shape      ,						snake_case     )

						UpperCamelCase__					=       [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
						for ii in range(3     ):
									for jj in range(3     ):
												UpperCamelCase__					=       output[0, ii, jj]
												UpperCamelCase__					=       expected[3 * ii + jj]
												UpperCamelCase__					=       "ii={} jj={} a={} b={}".format(snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )
												self.assertTrue(math.isclose(snake_case      ,						snake_case      ,						rel_tol=snake_case      ,						abs_tol=snake_case     )      ,						msg=snake_case     )


