





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  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=7					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=99					,  lowercase=64					,  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=3					,  lowercase=4					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

          a__						:	Dict =				None
          if self.use_input_mask:
                 a__						:	Union[str, Any] =				random_attention_mask([self.batch_size, self.seq_length])

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

          a__						:	Union[str, Any] =				None
          a__						:	Union[str, Any] =				None
          a__						:	str =				None
          if self.use_labels:
                 a__						:	Optional[int] =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)
                 a__						:	Union[str, Any] =				ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels)
                 a__						:	Tuple =				ids_tensor([self.batch_size]					,  self.num_choices)

          a__						:	List[str] =				self.get_config()

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

   def 							__lowercase		(					self)			->						int:







          '''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=lowercase					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				MegatronBertModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase)
          a__						:	Any =				model(lowercase					,  token_type_ids=lowercase)
          a__						:	List[str] =				model(lowercase)

          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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	int =				MegatronBertForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[Any] =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				MegatronBertForCausalLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Dict =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				MegatronBertForNextSentencePrediction(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Any =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  )
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, 2))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Tuple =				MegatronBertForPreTraining(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  next_sentence_label=lowercase					,  )
          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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				MegatronBertForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	str =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  start_positions=lowercase					,  end_positions=lowercase					,  )
          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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	Optional[int] =				MegatronBertForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				self.num_labels
          a__						:	Dict =				MegatronBertForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Dict =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Any =				self.num_choices
          a__						:	Any =				MegatronBertForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Any =				input_ids.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	Any =				token_type_ids.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	List[Any] =				input_mask.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	Dict =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  )
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_choices))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.prepare_config_and_inputs()
          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	Optional[int] =				config_and_inputs
          a__						:	Optional[int] =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				str         =  (
       (
           MegatronBertModel,
           MegatronBertForMaskedLM,
           MegatronBertForCausalLM,
           MegatronBertForMultipleChoice,
           MegatronBertForNextSentencePrediction,
           MegatronBertForPreTraining,
           MegatronBertForQuestionAnswering,
           MegatronBertForSequenceClassification,
           MegatronBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[Any]         =  (
       {
           '''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 {}
   )
   __A   :				List[Any]         =  True
   # test_resize_embeddings = False
   __A   :				Tuple         =  False

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase=False)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[Any] =				super()._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)

          if return_labels:
                 if model_class in get_values(lowercase):
                        a__						:	List[Any] =				torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length)					,  dtype=torch.long					,  device=lowercase)
                        a__						:	Union[str, Any] =				torch.zeros(
                            self.model_tester.batch_size					,  dtype=torch.long					,  device=lowercase)
          return inputs_dict

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	int =				MegatronBertModelTester(self)
          a__						:	List[Any] =				ConfigTester(self					,  config_class=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_model(*lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowercase)

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowercase)



def A_	(		A__				)  ->		Any:
       return torch.tensor(
           A__		,      dtype=torch.long		,      device=A__		,      )


lowercase      :       Any									=  1e-4





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




   """simple docstring"""



   @slow
   @unittest.skip('Model is not available.')
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				'nvidia/megatron-bert-uncased-345m'
          if "MYDIR" in os.environ:
                 a__						:	Optional[int] =				os.path.join(os.environ['MYDIR']					,  lowercase)
          a__						:	str =				MegatronBertModel.from_pretrained(lowercase)
          model.to(lowercase)
          model.half()
          a__						:	List[str] =				_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]])
          with torch.no_grad():
                 a__						:	int =				model(lowercase)[0]
          a__						:	Optional[Any] =				torch.Size((1, 9, 1024))
          self.assertEqual(output.shape					,  lowercase)

          a__						:	Tuple =				[-0.60_40, -0.25_17, -0.10_25, 0.34_20, -0.67_58, -0.00_17, -0.10_89, -0.19_90, 0.57_28]
          for ii in range(3):
                 for jj in range(3):
                        a__						:	Optional[Any] =				output[0, ii, jj]
                        a__						:	Tuple =				expected[3 * ii + jj]
                        a__						:	Union[str, Any] =				'ii={} jj={} a={} b={}'.format(lowercase					,  lowercase					,  lowercase					,  lowercase)
                        self.assertTrue(math.isclose(lowercase					,  lowercase					,  rel_tol=lowercase					,  abs_tol=lowercase)					,  msg=lowercase)









"""simple docstring"""




import qiskit





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  qiskit.Aer.get_backend('aer_simulator'					)

  A_  :       str										=  qiskit.QuantumCircuit(4 ,					2					)
  # encode inputs in qubits 0 and 1
  if bita == 1:
    qc_ha.x(0					)
  if bita == 1:
    qc_ha.x(1					)
  qc_ha.barrier()

  # use cnots to write XOR of the inputs on qubit2
  qc_ha.cx(0 ,					2					)
  qc_ha.cx(1 ,					2					)

  # use ccx / toffoli gate to write AND of the inputs on qubit3
  qc_ha.ccx(0 ,					1 ,					3					)
  qc_ha.barrier()

  # extract outputs
  qc_ha.measure(2 ,					0					)  # extract XOR value
  qc_ha.measure(3 ,					1					)  # extract AND value

  # Execute the circuit on the qasm simulator
  A_  :       Optional[Any]										=  qiskit.execute(_UpperCAmelCase ,					_UpperCAmelCase ,					shots=1000					)

  # Return the histogram data of the results of the experiment
  return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
   lowerCamelCase_     :						List[str]						=					half_adder(1, 1)
   print(F"Half Adder Output Qubit Counts: {counts}")