


'''simple docstring'''
import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)






class 		UpperCamelCase_		(       unittest.TestCase ):





						"""simple docstring"""




						def __init__(			self					:       Union[str, Any]						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       Tuple=13						,					_lowerCamelCase					:       List[Any]=7						,					_lowerCamelCase					:       Any=True						,					_lowerCamelCase					:       Any=True						,					_lowerCamelCase					:       int=True						,					_lowerCamelCase					:       Optional[int]=True						,					_lowerCamelCase					:       Optional[int]=99						,					_lowerCamelCase					:       Optional[int]=32						,					_lowerCamelCase					:       Optional[Any]=5						,					_lowerCamelCase					:       List[Any]=4						,					_lowerCamelCase					:       Optional[Any]=37						,					_lowerCamelCase					:       Union[str, Any]="gelu"						,					_lowerCamelCase					:       List[str]=0.1						,					_lowerCamelCase					:       int=0.1						,					_lowerCamelCase					:       Tuple=5_12						,					_lowerCamelCase					:       Dict=16						,					_lowerCamelCase					:       List[Any]=2						,					_lowerCamelCase					:       List[Any]=0.02						,					_lowerCamelCase					:       Any=4						,					)				->       Any:
										__magic_name__							=			parent
										__magic_name__							=			batch_size
										__magic_name__							=			seq_length
										__magic_name__							=			is_training
										__magic_name__							=			use_attention_mask
										__magic_name__							=			use_token_type_ids
										__magic_name__							=			use_labels
										__magic_name__							=			vocab_size
										__magic_name__							=			hidden_size
										__magic_name__							=			num_hidden_layers
										__magic_name__							=			num_attention_heads
										__magic_name__							=			intermediate_size
										__magic_name__							=			hidden_act
										__magic_name__							=			hidden_dropout_prob
										__magic_name__							=			attention_probs_dropout_prob
										__magic_name__							=			max_position_embeddings
										__magic_name__							=			type_vocab_size
										__magic_name__							=			type_sequence_label_size
										__magic_name__							=			initializer_range
										__magic_name__							=			num_choices
						def        __A       (			self					:       List[Any]	)				->       List[Any]:
										__magic_name__							=			ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										__magic_name__							=			None
										if self.use_attention_mask:
														__magic_name__							=			random_attention_mask([self.batch_size, self.seq_length]	)

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

										__magic_name__							=			BertConfig(
										    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						,					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=_lowerCamelCase						,					initializer_range=self.initializer_range						,					)

										return config, input_ids, token_type_ids, attention_mask
						def        __A       (			self					:       Any	)				->       Tuple:
										__magic_name__							=			self.prepare_config_and_inputs()
										__magic_name__							=			config_and_inputs
										__magic_name__							=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
										return config, inputs_dict

						def        __A       (			self					:       Union[str, Any]	)				->       Union[str, Any]:
										__magic_name__							=			self.prepare_config_and_inputs()
										__magic_name__							=			config_and_inputs

										__magic_name__							=			True
										__magic_name__							=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
										__magic_name__							=			ids_tensor([self.batch_size, self.seq_length]						,					vocab_size=2	)

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)






@require_flax
class 		UpperCamelCase_		(       A ,		unittest.TestCase ):





						"""simple docstring"""




						UpperCAmelCase__						:							List[Any]       =  True

						UpperCAmelCase__						:							int       =  (
						    (
						        FlaxBertModel,
						        FlaxBertForPreTraining,
						        FlaxBertForMaskedLM,
						        FlaxBertForMultipleChoice,
						        FlaxBertForQuestionAnswering,
						        FlaxBertForNextSentencePrediction,
						        FlaxBertForSequenceClassification,
						        FlaxBertForTokenClassification,
						        FlaxBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)
						def        __A       (			self					:       str	)				->       Dict:
										__magic_name__							=			FlaxBertModelTester(self	)

						@slow
						def        __A       (			self					:       Tuple	)				->       Optional[Any]:
										# Only check this for base model, not necessary for all model classes.
										# This will also help speed-up tests.
										__magic_name__							=			FlaxBertModel.from_pretrained("bert-base-cased"	)
										__magic_name__							=			model(np.ones((1, 1)	)	)
										self.assertIsNotNone(_lowerCamelCase	)









"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")