



import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
   import torch

if is_tf_available():
   import tensorflow as tf


@require_vision
@require_torchvision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					str      )   ->	Any:
    _A      =       tempfile.mkdtemp()
    _A      =       SamImageProcessor()
    _A      =       SamProcessor(__lowerCAmelCase      )
    processor.save_pretrained(self.tmpdirname      )

 def     snake_case_      (    self					:					Any			,						**__lowerCAmelCase					:					Any      )   ->	Dict:
    return AutoProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      ).image_processor

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					Any      )   ->	Dict:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]

    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]

    return image_inputs

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	int:
    _A      =       SamProcessor(image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _A      =       self.get_image_processor(do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    _A      =       SamProcessor.from_pretrained(self.tmpdirname			,						do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    self.assertEqual(processor.image_processor.to_json_string()			,						image_processor_add_kwargs.to_json_string()      )
    self.assertIsInstance(processor.image_processor			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	Tuple:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''np'''      )

    input_feat_extract.pop('''original_sizes'''      )  # pop original_sizes as it is popped in the processor
    input_feat_extract.pop('''reshaped_input_sizes'''      )  # pop original_sizes as it is popped in the processor

    for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()			,						input_processor[key].sum()			,						delta=1E-2      )

 @require_torch
 def     snake_case_      (    self					:					Dict      )   ->	Any:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )
    _A      =       [torch.ones((1, 3, 5, 5)      )]

    _A      =       [[17_64, 26_46]]

    _A      =       [[6_83, 10_24]]
    _A      =       processor.post_process_masks(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    _A      =       processor.post_process_masks(
        __lowerCAmelCase			,						torch.tensor(__lowerCAmelCase      )			,						torch.tensor(__lowerCAmelCase      )      )
    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    # should also work with np
    _A      =       [np.ones((1, 3, 5, 5)      )]
    _A      =       processor.post_process_masks(__lowerCAmelCase			,						np.array(__lowerCAmelCase      )			,						np.array(__lowerCAmelCase      )      )

    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    _A      =       [[1, 0], [0, 1]]
    with self.assertRaises(__lowerCAmelCase      ):
       _A      =       processor.post_process_masks(__lowerCAmelCase			,						np.array(__lowerCAmelCase      )			,						np.array(__lowerCAmelCase      )      )


@require_vision
@require_tf
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					int      )   ->	Any:
    _A      =       tempfile.mkdtemp()
    _A      =       SamImageProcessor()
    _A      =       SamProcessor(__lowerCAmelCase      )
    processor.save_pretrained(self.tmpdirname      )

 def     snake_case_      (    self					:					int			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	int:
    return AutoProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      ).image_processor

 def     snake_case_      (    self					:					List[str]      )   ->	Tuple:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]

    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]

    return image_inputs

 def     snake_case_      (    self					:					str      )   ->	Tuple:
    _A      =       SamProcessor(image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _A      =       self.get_image_processor(do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    _A      =       SamProcessor.from_pretrained(self.tmpdirname			,						do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    self.assertEqual(processor.image_processor.to_json_string()			,						image_processor_add_kwargs.to_json_string()      )
    self.assertIsInstance(processor.image_processor			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Any      )   ->	Optional[int]:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''np'''      )

    input_feat_extract.pop('''original_sizes'''      )  # pop original_sizes as it is popped in the processor
    input_feat_extract.pop('''reshaped_input_sizes'''      )  # pop reshaped_input_sizes as it is popped in the processor

    for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()			,						input_processor[key].sum()			,						delta=1E-2      )

 @require_tf
 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )
    _A      =       [tf.ones((1, 3, 5, 5)      )]

    _A      =       [[17_64, 26_46]]

    _A      =       [[6_83, 10_24]]
    _A      =       processor.post_process_masks(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						return_tensors='''tf'''      )
    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    _A      =       processor.post_process_masks(
        __lowerCAmelCase			,						tf.convert_to_tensor(__lowerCAmelCase      )			,						tf.convert_to_tensor(__lowerCAmelCase      )			,						return_tensors='''tf'''			,						)
    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    # should also work with np
    _A      =       [np.ones((1, 3, 5, 5)      )]
    _A      =       processor.post_process_masks(
        __lowerCAmelCase			,						np.array(__lowerCAmelCase      )			,						np.array(__lowerCAmelCase      )			,						return_tensors='''tf'''      )

    self.assertEqual(masks[0].shape			,						(1, 3, 17_64, 26_46)      )

    _A      =       [[1, 0], [0, 1]]
    with self.assertRaises(tf.errors.InvalidArgumentError      ):
       _A      =       processor.post_process_masks(
           __lowerCAmelCase			,						np.array(__lowerCAmelCase      )			,						np.array(__lowerCAmelCase      )			,						return_tensors='''tf'''      )


@require_vision
@require_torchvision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Optional[int]      )   ->	str:
    _A      =       tempfile.mkdtemp()
    _A      =       SamImageProcessor()
    _A      =       SamProcessor(__lowerCAmelCase      )
    processor.save_pretrained(self.tmpdirname      )

 def     snake_case_      (    self					:					List[str]			,						**__lowerCAmelCase					:					Union[str, Any]      )   ->	Any:
    return AutoProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      ).image_processor

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[Any]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					str      )   ->	Union[str, Any]:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]

    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]

    return image_inputs

 @is_pt_tf_cross_test
 def     snake_case_      (    self					:					Any      )   ->	str:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )
    _A      =       np.random.randint(0			,						2			,						size=(1, 3, 5, 5)      ).astype(np.floataa      )
    _A      =       [tf.convert_to_tensor(__lowerCAmelCase      )]
    _A      =       [torch.tensor(__lowerCAmelCase      )]

    _A      =       [[17_64, 26_46]]

    _A      =       [[6_83, 10_24]]
    _A      =       processor.post_process_masks(
        __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						return_tensors='''tf'''      )
    _A      =       processor.post_process_masks(
        __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						return_tensors='''pt'''      )

    self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()      )      )

 @is_pt_tf_cross_test
 def     snake_case_      (    self					:					Optional[int]      )   ->	List[Any]:
    _A      =       self.get_image_processor()

    _A      =       SamProcessor(image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''pt'''      )['''pixel_values'''].numpy()
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''pt'''      )['''pixel_values'''].numpy()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''tf'''      )['''pixel_values'''].numpy()
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''tf'''      )['''pixel_values'''].numpy()

    self.assertTrue(np.allclose(__lowerCAmelCase			,						__lowerCAmelCase      )      )
    self.assertTrue(np.allclose(__lowerCAmelCase			,						__lowerCAmelCase      )      )
    self.assertTrue(np.allclose(__lowerCAmelCase			,						__lowerCAmelCase      )      )








import math

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 3				)  ->     qiskit.result.counts.Counts:
   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).'''				)

   _A      =       QuantumRegister(_snake_case	,   '''qr'''				)
   _A      =       ClassicalRegister(_snake_case	,   '''cr'''				)

   _A      =       QuantumCircuit(_snake_case	,   _snake_case				)

   _A      =       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
   _A      =       Aer.get_backend('''qasm_simulator'''				)
   _A      =       execute(_snake_case	,   _snake_case	,   shots=10_000				)

   return job.result().get_counts(_snake_case				)


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



