





import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

       if is_vision_available():
              from transformers import OneFormerImageProcessor
              from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
              from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
       from PIL import Image




def    __UpperCAmelCase(      lowercase_ ,    lowercase_="shi-labs/oneformer_demo"      ):
       with open(hf_hub_download(lowercase_ ,    lowercase_ ,    repo_type='''dataset'''      ) ,    '''r'''      ) as f:
              _lowerCamelCase							:     Dict	=		json.load(lowercase_      )
       _lowerCamelCase							:     str	=		{}
       _lowerCamelCase							:     Any	=		[]
       _lowerCamelCase							:     Dict	=		[]
       for key, info in class_info.items():
              _lowerCamelCase							:     Tuple	=		info['''name''']
              class_names.append(info['''name''']      )
              if info["isthing"]:
                     thing_ids.append(int(lowercase_      )      )
       _lowerCamelCase							:     List[Any]	=		thing_ids
       _lowerCamelCase							:     Any	=		class_names
       return metadata







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__=7      ,							a__=3      ,							a__=30      ,							a__=400      ,							a__=None      ,							a__=True      ,							a__=True      ,							a__=[0.5, 0.5, 0.5]      ,							a__=[0.5, 0.5, 0.5]      ,							a__=10      ,							a__=False      ,							a__=255      ,							a__="shi-labs/oneformer_demo"      ,							a__="ade20k_panoptic.json"      ,							a__=10      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		parent
             _lowerCamelCase							:     Optional[Any]	=		batch_size
             _lowerCamelCase							:     Optional[int]	=		num_channels
             _lowerCamelCase							:     Optional[Any]	=		min_resolution
             _lowerCamelCase							:     Union[str, Any]	=		max_resolution
             _lowerCamelCase							:     Tuple	=		do_resize
             _lowerCamelCase							:     Optional[Any]	=		{'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
             _lowerCamelCase							:     Union[str, Any]	=		do_normalize
             _lowerCamelCase							:     Dict	=		image_mean
             _lowerCamelCase							:     Dict	=		image_std
             _lowerCamelCase							:     Any	=		class_info_file
             _lowerCamelCase							:     Union[str, Any]	=		prepare_metadata(a__      ,							a__)
             _lowerCamelCase							:     List[str]	=		num_text
             _lowerCamelCase							:     Dict	=		repo_path

             # for the post_process_functions
             _lowerCamelCase							:     Optional[int]	=		2
             _lowerCamelCase							:     Dict	=		10
             _lowerCamelCase							:     Optional[Any]	=		10
             _lowerCamelCase							:     Dict	=		3
             _lowerCamelCase							:     Optional[Any]	=		4
             _lowerCamelCase							:     Union[str, Any]	=		num_labels
             _lowerCamelCase							:     str	=		do_reduce_labels
             _lowerCamelCase							:     List[Any]	=		ignore_index


      def 				__snake_case			(		self):



             """simple docstring"""





             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "num_labels": self.num_labels,
                 "do_reduce_labels": self.do_reduce_labels,
                 "ignore_index": self.ignore_index,
                 "class_info_file": self.class_info_file,
                 "metadata": self.metadata,
                 "num_text": self.num_text,
             }


      def 				__snake_case			(		self      ,							a__      ,							a__=False):



             """simple docstring"""





             if not batched:
                    _lowerCamelCase							:     Tuple	=		image_inputs[0]
                    if isinstance(a__      ,							Image.Image):
                           _lowerCamelCase,  _lowerCamelCase							:     Any	=		image.size
                    else:
                           _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		image.shape[1], image.shape[2]
                    if w < h:
                           _lowerCamelCase							:     Any	=		int(self.size['''shortest_edge'''] * h / w)
                           _lowerCamelCase							:     Any	=		self.size['''shortest_edge''']
                    elif w > h:
                           _lowerCamelCase							:     Tuple	=		self.size['''shortest_edge''']
                           _lowerCamelCase							:     Union[str, Any]	=		int(self.size['''shortest_edge'''] * w / h)
                    else:
                           _lowerCamelCase							:     Optional[Any]	=		self.size['''shortest_edge''']
                           _lowerCamelCase							:     Any	=		self.size['''shortest_edge''']

             else:
                    _lowerCamelCase							:     List[Any]	=		[]
                    for image in image_inputs:
                           _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self.get_expected_values([image])
                           expected_values.append((expected_height, expected_width))
                    _lowerCamelCase							:     Union[str, Any]	=		max(a__      ,							key=lambda a__: item[0])[0]
                    _lowerCamelCase							:     Dict	=		max(a__      ,							key=lambda a__: item[1])[1]

             return expected_height, expected_width



      def 				__snake_case			(		self):



             """simple docstring"""





             return OneFormerForUniversalSegmentationOutput(
                 # +1 for null class
                 class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1))      ,							masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width))      ,							)







@require_torch
@require_vision
class 			__A       (      lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      UpperCAmelCase__						=      image_processing_class


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		OneFormerImageProcessorTester(self)


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return self.image_processing_tester.prepare_image_processor_dict()


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__      ,							'''image_mean'''))
             self.assertTrue(hasattr(a__      ,							'''image_std'''))
             self.assertTrue(hasattr(a__      ,							'''do_normalize'''))
             self.assertTrue(hasattr(a__      ,							'''do_resize'''))
             self.assertTrue(hasattr(a__      ,							'''size'''))
             self.assertTrue(hasattr(a__      ,							'''ignore_index'''))
             self.assertTrue(hasattr(a__      ,							'''class_info_file'''))
             self.assertTrue(hasattr(a__      ,							'''num_text'''))
             self.assertTrue(hasattr(a__      ,							'''repo_path'''))
             self.assertTrue(hasattr(a__      ,							'''metadata'''))
             self.assertTrue(hasattr(a__      ,							'''do_reduce_labels'''))


      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase							:     List[Any]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__      ,							Image.Image)

             # Test not batched input
             _lowerCamelCase							:     int	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__)

             self.assertEqual(
                 encoded_images.shape      ,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,							)

             # Test batched
             _lowerCamelCase,  _lowerCamelCase							:     Optional[int]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

             _lowerCamelCase							:     List[str]	=		image_processor(
                 a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
             self.assertEqual(
                 encoded_images.shape      ,							(
                     self.image_processing_tester.batch_size,
                     self.image_processing_tester.num_channels,
                     expected_height,
                     expected_width,
                 )      ,							)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase							:     Union[str, Any]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__      ,							numpify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__      ,							np.ndarray)

             # Test not batched input
             _lowerCamelCase							:     str	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

             _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		self.image_processing_tester.get_expected_values(a__)

             self.assertEqual(
                 encoded_images.shape      ,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,							)

             # Test batched
             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

             _lowerCamelCase							:     List[Any]	=		image_processor(
                 a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
             self.assertEqual(
                 encoded_images.shape      ,							(
                     self.image_processing_tester.batch_size,
                     self.image_processing_tester.num_channels,
                     expected_height,
                     expected_width,
                 )      ,							)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase							:     List[str]	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__      ,							torchify=a__)
             for image in image_inputs:
                    self.assertIsInstance(a__      ,							torch.Tensor)

             # Test not batched input
             _lowerCamelCase							:     Dict	=		image_processor(image_inputs[0]      ,							['''semantic''']      ,							return_tensors='''pt''').pixel_values

             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		self.image_processing_tester.get_expected_values(a__)

             self.assertEqual(
                 encoded_images.shape      ,							(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,							)

             # Test batched
             _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		self.image_processing_tester.get_expected_values(a__      ,							batched=a__)

             _lowerCamelCase							:     Union[str, Any]	=		image_processor(
                 a__      ,							['''semantic'''] * len(a__)      ,							return_tensors='''pt''').pixel_values
             self.assertEqual(
                 encoded_images.shape      ,							(
                     self.image_processing_tester.batch_size,
                     self.image_processing_tester.num_channels,
                     expected_height,
                     expected_width,
                 )      ,							)


      def 				__snake_case			(		self      ,							a__=False      ,							a__=False      ,							a__="np"):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		self.image_processing_class(**self.image_processor_dict)
             # prepare image and target
             _lowerCamelCase							:     Any	=		self.image_processing_tester.num_labels
             _lowerCamelCase							:     Optional[Any]	=		None
             _lowerCamelCase							:     Union[str, Any]	=		None
             _lowerCamelCase							:     Tuple	=		prepare_image_inputs(self.image_processing_tester      ,							equal_resolution=a__)
             if with_segmentation_maps:
                    _lowerCamelCase							:     Tuple	=		num_labels
                    if is_instance_map:
                           _lowerCamelCase							:     Optional[Any]	=		list(range(a__)) * 2
                           _lowerCamelCase							:     str	=		dict(enumerate(a__))
                    _lowerCamelCase							:     Union[str, Any]	=		[
                        np.random.randint(0      ,							high * 2      ,							(img.size[1], img.size[0])).astype(np.uinta) for img in image_inputs
                    ]
                    if segmentation_type == "pil":
                           _lowerCamelCase							:     int	=		[Image.fromarray(a__) for annotation in annotations]

             _lowerCamelCase							:     Optional[Any]	=		image_processor(
                 a__      ,							['''semantic'''] * len(a__)      ,							a__      ,							return_tensors='''pt'''      ,							instance_id_to_semantic_id=a__      ,							pad_and_return_pixel_mask=a__      ,							)

             return inputs


      def 				__snake_case			(		self):



             """simple docstring"""





             pass


      def 				__snake_case			(		self):



             """simple docstring"""





             def common(a__=False      ,							a__=None):
                    _lowerCamelCase							:     Dict	=		self.comm_get_image_processor_inputs(
                        with_segmentation_maps=a__      ,							is_instance_map=a__      ,							segmentation_type=a__)

                    _lowerCamelCase							:     str	=		inputs['''mask_labels''']
                    _lowerCamelCase							:     Tuple	=		inputs['''class_labels''']
                    _lowerCamelCase							:     Union[str, Any]	=		inputs['''pixel_values''']
                    _lowerCamelCase							:     Tuple	=		inputs['''text_inputs''']

                    # check the batch_size
                    for mask_label, class_label, text_input in zip(a__      ,							a__      ,							a__):
                           self.assertEqual(mask_label.shape[0]      ,							class_label.shape[0])
                           # this ensure padding has happened
                           self.assertEqual(mask_label.shape[1:]      ,							pixel_values.shape[2:])
                           self.assertEqual(len(a__)      ,							self.image_processing_tester.num_text)

             common()
             common(is_instance_map=a__)
             common(is_instance_map=a__      ,							segmentation_type='''pil''')
             common(is_instance_map=a__      ,							segmentation_type='''pil''')


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		np.zeros((20, 50))
             _lowerCamelCase							:     List[Any]	=		1
             _lowerCamelCase							:     List[Any]	=		1
             _lowerCamelCase							:     str	=		1

             _lowerCamelCase							:     Any	=		binary_mask_to_rle(a__)
             self.assertEqual(len(a__)      ,							4)
             self.assertEqual(rle[0]      ,							21)
             self.assertEqual(rle[1]      ,							45)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.image_processing_class(
                 num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
             _lowerCamelCase							:     Optional[Any]	=		self.image_processing_tester.get_fake_oneformer_outputs()

             _lowerCamelCase							:     Union[str, Any]	=		fature_extractor.post_process_semantic_segmentation(a__)

             self.assertEqual(len(a__)      ,							self.image_processing_tester.batch_size)
             self.assertEqual(
                 segmentation[0].shape      ,							(
                     self.image_processing_tester.height,
                     self.image_processing_tester.width,
                 )      ,							)

             _lowerCamelCase							:     Any	=		[(1, 4) for i in range(self.image_processing_tester.batch_size)]
             _lowerCamelCase							:     Optional[int]	=		fature_extractor.post_process_semantic_segmentation(a__      ,							target_sizes=a__)

             self.assertEqual(segmentation[0].shape      ,							target_sizes[0])


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.image_processing_class(
                 num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
             _lowerCamelCase							:     List[str]	=		self.image_processing_tester.get_fake_oneformer_outputs()
             _lowerCamelCase							:     List[Any]	=		image_processor.post_process_instance_segmentation(a__      ,							threshold=0)

             self.assertTrue(len(a__) == self.image_processing_tester.batch_size)
             for el in segmentation:
                    self.assertTrue('''segmentation''' in el)
                    self.assertTrue('''segments_info''' in el)
                    self.assertEqual(type(el['''segments_info'''])      ,							a__)
                    self.assertEqual(
                        el['''segmentation'''].shape      ,							(self.image_processing_tester.height, self.image_processing_tester.width))



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		self.image_processing_class(
                 num_labels=self.image_processing_tester.num_classes      ,							max_seq_length=77      ,							task_seq_length=77      ,							class_info_file='''ade20k_panoptic.json'''      ,							num_text=self.image_processing_tester.num_text      ,							repo_path='''shi-labs/oneformer_demo'''      ,							)
             _lowerCamelCase							:     Tuple	=		self.image_processing_tester.get_fake_oneformer_outputs()
             _lowerCamelCase							:     Optional[Any]	=		image_processor.post_process_panoptic_segmentation(a__      ,							threshold=0)

             self.assertTrue(len(a__) == self.image_processing_tester.batch_size)
             for el in segmentation:
                    self.assertTrue('''segmentation''' in el)
                    self.assertTrue('''segments_info''' in el)
                    self.assertEqual(type(el['''segments_info'''])      ,							a__)
                    self.assertEqual(
                        el['''segmentation'''].shape      ,							(self.image_processing_tester.height, self.image_processing_tester.width))








import qiskit




def    __UpperCAmelCase(      lowercase_ = 2      ):
       _lowerCamelCase							:     Optional[Any]	=		qubits

       # Using Aer's simulator
       _lowerCamelCase							:     Optional[int]	=		qiskit.Aer.get_backend('''aer_simulator'''      )

       # Creating a Quantum Circuit acting on the q register
       _lowerCamelCase							:     List[str]	=		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							:     str	=		qiskit.execute(lowercase_ ,    lowercase_ ,    shots=10_00      )

       return job.result().get_counts(lowercase_      )


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

