





'''simple docstring'''




import qiskit







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     qiskit.result.counts.Counts:
 __UpperCAmelCase					=   qiskit.Aer.get_backend('aer_simulator'    )

 # Create a Quantum Circuit acting on the q register
 __UpperCAmelCase					=   qiskit.QuantumCircuit(_lowerCAmelCase       ,    _lowerCAmelCase    )

 # Apply X (NOT) Gate to Qubits 0 & 1
 circuit.x(0    )
 circuit.x(1    )

 # Map the quantum measurement to the classical bits
 circuit.measure([0, 1]       ,    [0, 1]    )

 # Execute the circuit on the qasm simulator
 __UpperCAmelCase					=   qiskit.execute(_lowerCAmelCase       ,    _lowerCAmelCase       ,    shots=10_00    )

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


if __name__ == "__main__":
    _A:					str							=				single_qubit_measure(2, 2)
    print(F"""Total count for various states are: {counts}""")












'''simple docstring'''




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 UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A=13     ,			__A=7     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=True     ,			__A=99     ,			__A=64     ,			__A=32     ,			__A=5     ,			__A=4     ,			__A=37     ,			__A="gelu"     ,			__A=0.1     ,			__A=0.1     ,			__A=512     ,			__A=16     ,			__A=2     ,			__A=0.0_2     ,			__A=3     ,			__A=4     ,			__A=None     ,			):
     __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 					__lowerCamelCase						(  self					):
     __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 					__lowerCamelCase						(  self					):
     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=__A     ,			initializer_range=self.initializer_range     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertModel(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A     ,			token_type_ids=__A					)
     __UpperCAmelCase					=   model(__A					)

     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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForMaskedLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForCausalLM(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForNextSentencePrediction(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, 2)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForPreTraining(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			next_sentence_label=__A     ,			)
     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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   MegatronBertForQuestionAnswering(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			start_positions=__A     ,			end_positions=__A     ,			)
     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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForSequenceClassification(__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_labels
     __UpperCAmelCase					=   MegatronBertForTokenClassification(config=__A					)
     model.to(__A					)
     model.eval()
     __UpperCAmelCase					=   model(__A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A					)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   self.num_choices
     __UpperCAmelCase					=   MegatronBertForMultipleChoice(config=__A					)
     model.to(__A					)
     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(
         __A     ,			attention_mask=__A     ,			token_type_ids=__A     ,			labels=__A     ,			)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_choices)					)




    def 					__lowerCamelCase						(  self					):
     __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 UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_					,			unittest.TestCase							):
    _A :			str						   =       (
        (
            MegatronBertModel,
            MegatronBertForMaskedLM,
            MegatronBertForCausalLM,
            MegatronBertForMultipleChoice,
            MegatronBertForNextSentencePrediction,
            MegatronBertForPreTraining,
            MegatronBertForQuestionAnswering,
            MegatronBertForSequenceClassification,
            MegatronBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _A :			Dict						   =       (
        {
            """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[str]						   =       True
    # test_resize_embeddings = False
    _A :			Tuple						   =       False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A=False					):
     __UpperCAmelCase					=   super()._prepare_for_class(__A     ,			__A     ,			return_labels=__A					)

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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   MegatronBertModelTester(self					)
     __UpperCAmelCase					=   ConfigTester(self     ,			config_class=__A     ,			hidden_size=37					)



    def 					__lowerCamelCase						(  self					):
     self.config_tester.run_common_tests()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_model(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_pretraining(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_question_answering(*__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__A					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_megatron_bert_for_token_classification(*__A					)







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Dict:
 return torch.tensor(
     _lowerCAmelCase       ,    dtype=torch.long       ,    device=_lowerCAmelCase       ,    )


_A:					Any							=				1E-4






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



    @slow
    @unittest.skip('Model is not available.'					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   'nvidia/megatron-bert-uncased-345m'
     if "MYDIR" in os.environ:
      __UpperCAmelCase					=   os.path.join(os.environ['MYDIR']     ,			__A					)
     __UpperCAmelCase					=   MegatronBertModel.from_pretrained(__A					)
     model.to(__A					)
     model.half()
     __UpperCAmelCase					=   _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]					)
     with torch.no_grad():
      __UpperCAmelCase					=   model(__A					)[0]
     __UpperCAmelCase					=   torch.Size((1, 9, 1_024)					)
     self.assertEqual(output.shape     ,			__A					)

     __UpperCAmelCase					=   [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
     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(__A     ,			__A     ,			__A     ,			__A					)
       self.assertTrue(math.isclose(__A     ,			__A     ,			rel_tol=__A     ,			abs_tol=__A					)     ,			msg=__A					)





