code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import floats_tensor, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  class 							lowerCamelCase		( _UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  Any      =				KandinskyVaaPriorEmbaEmbPipeline
     lowercase       :  Union[str, Any]      =				['prompt', 'image']
     lowercase       :  str      =				['prompt', 'image']
     lowercase       :  Optional[int]      =				[
         'num_images_per_prompt',
         'strength',
         'generator',
         'num_inference_steps',
         'latents',
         'negative_prompt',
         'guidance_scale',
         'output_type',
         'return_dict',
     ]
     lowercase       :  List[Any]      =				False
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return 32
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return 32
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self.time_input_dim
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self.time_input_dim * 4
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return 100
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   Union[str, Any]          =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
         return tokenizer
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         torch.manual_seed(0 )
         UpperCamelCase				:   Tuple          =							CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=self.text_embedder_hidden_size    ,      projection_dim=self.text_embedder_hidden_size    ,      intermediate_size=37    ,      layer_norm_eps=1e-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1000    ,      )
         return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         torch.manual_seed(0 )
  
         UpperCamelCase				:   List[str]          =							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }
  
         UpperCamelCase				:   List[Any]          =							PriorTransformer(**SCREAMING_SNAKE_CASE_ )
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         UpperCamelCase				:   Any          =							nn.Parameter(torch.ones(model.clip_std.shape ) )
         return model
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         torch.manual_seed(0 )
         UpperCamelCase				:   Union[str, Any]          =							CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size    ,      image_size=224    ,      projection_dim=self.text_embedder_hidden_size    ,      intermediate_size=37    ,      num_attention_heads=4    ,      num_channels=3    ,      num_hidden_layers=5    ,      patch_size=14    ,      )
  
         UpperCamelCase				:   List[str]          =							CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_ )
         return model
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         UpperCamelCase				:   str          =							CLIPImageProcessor(
             crop_size=224    ,      do_center_crop=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_    ,      do_resize=SCREAMING_SNAKE_CASE_    ,      image_mean=[0.48145466, 0.4578275, 0.40821073]    ,      image_std=[0.26862954, 0.26130258, 0.27577711]    ,      resample=3    ,      size=224    ,      )
  
         return image_processor
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							self.dummy_prior
         UpperCamelCase				:   Optional[Any]          =							self.dummy_image_encoder
         UpperCamelCase				:   Optional[int]          =							self.dummy_text_encoder
         UpperCamelCase				:   Tuple          =							self.dummy_tokenizer
         UpperCamelCase				:   Any          =							self.dummy_image_processor
  
         UpperCamelCase				:   Union[str, Any]          =							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,      prediction_type="""sample"""    ,      num_train_timesteps=1000    ,      clip_sample=SCREAMING_SNAKE_CASE_    ,      clip_sample_range=10.0    ,      )
  
         UpperCamelCase				:   List[str]          =							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }
  
         return components
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=0 ):
         if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
             UpperCamelCase				:   Optional[int]          =							torch.manual_seed(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Optional[int]          =							torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Dict          =							floats_tensor((1, 3, 64, 64)    ,      rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							image.cpu().permute(0    ,      2    ,      3    ,      1 )[0]
         UpperCamelCase				:   Any          =							Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" ).resize((256, 256) )
  
         UpperCamelCase				:   Optional[int]          =							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							"""cpu"""
  
         UpperCamelCase				:   str          =							self.get_dummy_components()
  
         UpperCamelCase				:   List[Any]          =							self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[int]          =							pipe.to(SCREAMING_SNAKE_CASE_ )
  
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   List[Any]          =							pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
         UpperCamelCase				:   Dict          =							output.image_embeds
  
         UpperCamelCase				:   Any          =							pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )    ,      return_dict=SCREAMING_SNAKE_CASE_    ,      )[0]
  
         UpperCamelCase				:   Dict          =							image[0, -10:]
         UpperCamelCase				:   List[str]          =							image_from_tuple[0, -10:]
  
         assert image.shape == (1, 32)
  
         UpperCamelCase				:   Any          =							np.array(
             [
                 0.1071284,
                 1.3330271,
                 0.61260223,
                 -0.6691065,
                 -0.3846852,
                 -1.0303661,
                 0.22716111,
                 0.03348901,
                 0.30040675,
                 -0.24805029,
             ] )
  
         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
  
  
  
  
  
  
  
     @skip_mps
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							torch_device == """cpu"""
         UpperCamelCase				:   Tuple          =							True
         UpperCamelCase				:   Optional[int]          =							False
  
         self._test_inference_batch_single_identical(
             test_max_difference=SCREAMING_SNAKE_CASE_    ,      relax_max_difference=SCREAMING_SNAKE_CASE_    ,      test_mean_pixel_difference=SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
  
  
     @skip_mps
     def       a_							(							self ):
         UpperCamelCase				:   Optional[int]          =							torch_device == """cpu"""
         UpperCamelCase				:   Any          =							False
  
         self._test_attention_slicing_forward_pass(
             test_max_difference=SCREAMING_SNAKE_CASE_    ,      test_mean_pixel_difference=SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import qiskit
  from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
  def 					A_			(		snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2						,snake_case_     :       float = np.pi / 2    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							QuantumRegister(3						,"""qr"""    )  # Define the number of quantum bits
      UpperCamelCase				:   Any          =							ClassicalRegister(1						,"""cr"""    )  # Define the number of classical bits
  
      UpperCamelCase				:   Any          =							QuantumCircuit(snake_case_						,snake_case_    )  # Define the quantum circuit.
  
      # Build the circuit
      quantum_circuit.u(snake_case_						,snake_case_						,snake_case_						,0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1						,2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0						,1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0						,2    )  # add control z gate.
      quantum_circuit.cx(1						,2    )
  
      quantum_circuit.measure([2]						,[0]    )  # measure the qubit.
  
      # Simulate the circuit using qasm simulator
      UpperCamelCase				:   Tuple          =							Aer.get_backend("""aer_simulator"""    )
      UpperCamelCase				:   Any          =							execute(snake_case_						,snake_case_						,shots=1_0_0_0    )
  
      return job.result().get_counts(snake_case_    )
  
  
  if __name__ == "__main__":
    print(
        '''Total count for teleported state is: '''
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )
  
  
  
  
  