




"""simple docstring"""


import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
     import jax

     from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
     from PIL import Image

     from transformers import BeitImageProcessor






class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def __init__(    self:		str,				a_:		Union[str, Any],				a_:		Dict=100,				a_:		Union[str, Any]=13,				a_:		Any=30,				a_:		Optional[int]=2,				a_:		Optional[int]=3,				a_:		Tuple=True,				a_:		str=True,				a_:		List[str]=32,				a_:		Optional[Any]=5,				a_:		Any=4,				a_:		List[Any]=37,				a_:		Dict="gelu",				a_:		Optional[Any]=0.1,				a_:		Dict=0.1,				a_:		Optional[Any]=10,				a_:		int=0.02,				a_:		str=3,				):




   '''simple docstring'''



   _snake_case							:		List[str]								=  parent
   _snake_case							:		Tuple								=  vocab_size
   _snake_case							:		List[str]								=  batch_size
   _snake_case							:		Any								=  image_size
   _snake_case							:		Tuple								=  patch_size
   _snake_case							:		int								=  num_channels
   _snake_case							:		Optional[int]								=  is_training
   _snake_case							:		int								=  use_labels
   _snake_case							:		Union[str, Any]								=  hidden_size
   _snake_case							:		Any								=  num_hidden_layers
   _snake_case							:		List[Any]								=  num_attention_heads
   _snake_case							:		List[str]								=  intermediate_size
   _snake_case							:		Union[str, Any]								=  hidden_act
   _snake_case							:		List[Any]								=  hidden_dropout_prob
   _snake_case							:		List[Any]								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  type_sequence_label_size
   _snake_case							:		Tuple								=  initializer_range

   # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   _snake_case							:		Union[str, Any]								=  (image_size // patch_size) ** 2
   _snake_case							:		str								=  num_patches + 1


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




   '''simple docstring'''



   _snake_case							:		Any								=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

   _snake_case							:		Union[str, Any]								=  None
   if self.use_labels:
     _snake_case							:		Optional[Any]								=  ids_tensor([self.batch_size],				self.type_sequence_label_size		)

   _snake_case							:		Optional[Any]								=  BeitConfig(
       vocab_size=self.vocab_size,				image_size=self.image_size,				patch_size=self.patch_size,				num_channels=self.num_channels,				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,				is_decoder=a_,				initializer_range=self.initializer_range,				)

   return config, pixel_values, labels


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		List[Any],				a_:		int,				a_:		int		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  FlaxBeitModel(config=a_		)
   _snake_case							:		List[str]								=  model(a_		)
   self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)		)


 def 			UpperCamelCase_       (    self:		Any,				a_:		List[Any],				a_:		Optional[Any],				a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  FlaxBeitForMaskedImageModeling(config=a_		)
   _snake_case							:		int								=  model(a_		)
   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length - 1, self.vocab_size)		)


 def 			UpperCamelCase_       (    self:		int,				a_:		int,				a_:		Union[str, Any],				a_:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.type_sequence_label_size
   _snake_case							:		Optional[int]								=  FlaxBeitForImageClassification(config=a_		)
   _snake_case							:		int								=  model(a_		)
   self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.type_sequence_label_size)		)

   # test greyscale images
   _snake_case							:		int								=  1
   _snake_case							:		Any								=  FlaxBeitForImageClassification(a_		)

   _snake_case							:		Any								=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
   _snake_case							:		str								=  model(a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )		, (
       _snake_case
   )		, (
       _snake_case
   )		, 
   )							:		Dict								=  config_and_inputs
   _snake_case							:		Tuple								=  {"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_flax
class  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = (
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  FlaxBeitModelTester(self		)
   _snake_case							:		Optional[int]								=  ConfigTester(self,				config_class=a_,				has_text_modality=a_,				hidden_size=37		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		Union[str, Any]								=  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:		Optional[int]								=  model_class(a_		)
     _snake_case							:		Optional[int]								=  inspect.signature(model.__call__		)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:		Optional[int]								=  [*signature.parameters.keys()]

     _snake_case							:		Union[str, Any]								=  ["""pixel_values"""]
     self.assertListEqual(arg_names[:1],				a_		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case		, _snake_case							:		Dict								=  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__		):
       _snake_case							:		Tuple								=  self._prepare_for_class(a_,				a_		)
       _snake_case							:		int								=  model_class(a_		)

       @jax.jit
       def model_jitted(a_:		int,				**a_:		Dict		):
         return model(pixel_values=a_,				**a_		)

       with self.subTest("""JIT Enabled"""		):
         _snake_case							:		List[str]								=  model_jitted(**a_		).to_tuple()

       with self.subTest("""JIT Disabled"""		):
         with jax.disable_jit():
           _snake_case							:		Dict								=  model_jitted(**a_		).to_tuple()

       self.assertEqual(len(a_		),				len(a_		)		)
       for jitted_output, output in zip(a_,				a_		):
         self.assertEqual(jitted_output.shape,				output.shape		)


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_		)


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*a_		)


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_		)


 @slow
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   for model_class_name in self.all_model_classes:
     _snake_case							:		int								=  model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""		)
     _snake_case							:		List[str]								=  model(np.ones((1, 3, 224, 224)		)		)
     self.assertIsNotNone(a_		)







def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[Any]								=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image






@require_vision
@require_flax
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 @cached_property
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""		) if is_vision_available() else None


 @slow
 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""		)

   _snake_case							:		Any								=  self.default_image_processor
   _snake_case							:		Any								=  prepare_img()
   _snake_case							:		Dict								=  image_processor(images=a_,				return_tensors="""np"""		).pixel_values

   # prepare bool_masked_pos
   _snake_case							:		int								=  np.ones((1, 196),				dtype=a_		)

   # forward pass
   _snake_case							:		Optional[Any]								=  model(pixel_values=a_,				bool_masked_pos=a_		)
   _snake_case							:		List[Any]								=  outputs.logits

   # verify the logits
   _snake_case							:		Tuple								=  (1, 196, 8_192)
   self.assertEqual(logits.shape,				a_		)

   _snake_case							:		Dict								=  np.array(
       [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]		)

   self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3],				a_,				atol=1E-2		)		)


 @slow
 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""		)

   _snake_case							:		Tuple								=  self.default_image_processor
   _snake_case							:		Any								=  prepare_img()
   _snake_case							:		str								=  image_processor(images=a_,				return_tensors="""np"""		)

   # forward pass
   _snake_case							:		List[str]								=  model(**a_		)
   _snake_case							:		List[Any]								=  outputs.logits

   # verify the logits
   _snake_case							:		List[Any]								=  (1, 1_000)
   self.assertEqual(logits.shape,				a_		)

   _snake_case							:		List[str]								=  np.array([-1.2_385, -1.0_987, -1.0_108]		)

   self.assertTrue(np.allclose(logits[0, :3],				a_,				atol=1E-4		)		)

   _snake_case							:		Tuple								=  281
   self.assertEqual(logits.argmax(-1		).item(),				a_		)


 @slow
 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""		)

   _snake_case							:		Any								=  self.default_image_processor
   _snake_case							:		str								=  prepare_img()
   _snake_case							:		List[Any]								=  image_processor(images=a_,				return_tensors="""np"""		)

   # forward pass
   _snake_case							:		Optional[int]								=  model(**a_		)
   _snake_case							:		List[Any]								=  outputs.logits

   # verify the logits
   _snake_case							:		Optional[int]								=  (1, 21_841)
   self.assertEqual(logits.shape,				a_		)

   _snake_case							:		Tuple								=  np.array([1.6_881, -0.2_787, 0.5_901]		)

   self.assertTrue(np.allclose(logits[0, :3],				a_,				atol=1E-4		)		)

   _snake_case							:		Optional[Any]								=  2_396
   self.assertEqual(logits.argmax(-1		).item(),				a_		)








"""simple docstring"""


import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""number of qubits must be a integer."""   )
  if number_of_qubits <= 0:
    raise ValueError("""number of qubits must be > 0."""   )
  if math.floor(snake_case__   ) != number_of_qubits:
    raise ValueError("""number of qubits must be exact integer."""   )
  if number_of_qubits > 10:
    raise ValueError("""number of qubits too large to simulate(>10)."""   )

  _snake_case							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  number_of_qubits

  for i in range(snake_case__   ):
    quantum_circuit.h(number_of_qubits - i - 1   )
    counter -= 1
    for j in range(snake_case__   ):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)		,       snake_case__		,       snake_case__   )

  for k in range(number_of_qubits // 2   ):
    quantum_circuit.swap(snake_case__		,       number_of_qubits - k - 1   )

  # measure all the qubits
  quantum_circuit.measure(snake_case__		,       snake_case__   )
  # simulate with 10000 shots
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


if __name__ == "__main__":
     print(
         F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
     )


