


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class  lowerCAmelCase__(    __lowerCAmelCase    ,    __lowerCAmelCase    ,    __lowerCAmelCase    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Optional[Any]			= AltDiffusionPipeline
    A_  :     Tuple			= TEXT_TO_IMAGE_PARAMS
    A_  :     Optional[int]			= TEXT_TO_IMAGE_BATCH_PARAMS
    A_  :     Dict			= TEXT_TO_IMAGE_IMAGE_PARAMS
    A_  :     Any			= TEXT_TO_IMAGE_IMAGE_PARAMS





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       torch.manual_seed(0	)
       UpperCAmelCase_							:      str						=   UNetaDConditionModel(
           block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,   cross_attention_dim=32       ,   )
       UpperCAmelCase_							:      Tuple						=   DDIMScheduler(
           beta_start=0.00_085       ,   beta_end=0.012       ,   beta_schedule='''scaled_linear'''       ,   clip_sample=lowerCamelCase__       ,   set_alpha_to_one=lowerCamelCase__       ,   )
       torch.manual_seed(0	)
       UpperCAmelCase_							:      List[str]						=   AutoencoderKL(
           block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,   latent_channels=4       ,   )

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0	)
       UpperCAmelCase_							:      Union[str, Any]						=   CLIPTextConfig(
           bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   projection_dim=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=5_002       ,   )
       UpperCAmelCase_							:      List[Any]						=   CLIPTextModel(lowerCamelCase__	)

       UpperCAmelCase_							:      List[Any]						=   XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''	)
       UpperCAmelCase_							:      Tuple						=   77

       UpperCAmelCase_							:      Dict						=   {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components





    def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Tuple=0	):





       '''simple docstring'''






       if str(lowerCamelCase__	).startswith('''mps'''	):
          UpperCAmelCase_							:      Optional[int]						=   torch.manual_seed(lowerCamelCase__	)
       else:
          UpperCAmelCase_							:      Dict						=   torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
       UpperCAmelCase_							:      Dict						=   {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3E-3	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       UpperCAmelCase_							:      Optional[int]						=   self.get_dummy_components()
       torch.manual_seed(0	)
       UpperCAmelCase_							:      str						=   RobertaSeriesConfig(
           hidden_size=32       ,   project_dim=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   vocab_size=5_002       ,   )
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCAmelCase_							:      int						=   RobertaSeriesModelWithTransformation(lowerCamelCase__	)
       UpperCAmelCase_							:      Any						=   text_encoder

       UpperCAmelCase_							:      Dict						=   AltDiffusionPipeline(**lowerCamelCase__	)
       UpperCAmelCase_							:      Any						=   alt_pipe.to(lowerCamelCase__	)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

       UpperCAmelCase_							:      Dict						=   self.get_dummy_inputs(lowerCamelCase__	)
       UpperCAmelCase_							:      Optional[int]						=   '''A photo of an astronaut'''
       UpperCAmelCase_							:      int						=   alt_pipe(**lowerCamelCase__	)
       UpperCAmelCase_							:      Tuple						=   output.images
       UpperCAmelCase_							:      str						=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase_							:      List[Any]						=   np.array(
           [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       UpperCAmelCase_							:      Optional[Any]						=   self.get_dummy_components()
       UpperCAmelCase_							:      Optional[Any]						=   PNDMScheduler(skip_prk_steps=lowerCamelCase__	)
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Optional[int]						=   RobertaSeriesConfig(
           hidden_size=32       ,   project_dim=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   vocab_size=5_002       ,   )
       # TODO: remove after fixing the non-deterministic text encoder
       UpperCAmelCase_							:      int						=   RobertaSeriesModelWithTransformation(lowerCamelCase__	)
       UpperCAmelCase_							:      str						=   text_encoder
       UpperCAmelCase_							:      str						=   AltDiffusionPipeline(**lowerCamelCase__	)
       UpperCAmelCase_							:      Optional[Any]						=   alt_pipe.to(lowerCamelCase__	)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

       UpperCAmelCase_							:      List[Any]						=   self.get_dummy_inputs(lowerCamelCase__	)
       UpperCAmelCase_							:      Tuple						=   alt_pipe(**lowerCamelCase__	)
       UpperCAmelCase_							:      int						=   output.images
       UpperCAmelCase_							:      Tuple						=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase_							:      List[Any]						=   np.array(
           [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





@slow
@require_torch_gpu
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''       ,   safety_checker=lowerCamelCase__	)
       UpperCAmelCase_							:      Union[str, Any]						=   alt_pipe.to(lowerCamelCase__	)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

       UpperCAmelCase_							:      Any						=   '''A painting of a squirrel eating a burger'''
       UpperCAmelCase_							:      Optional[int]						=   torch.manual_seed(0	)
       UpperCAmelCase_							:      Optional[Any]						=   alt_pipe([prompt]       ,   generator=lowerCamelCase__       ,   guidance_scale=6.0       ,   num_inference_steps=20       ,   output_type='''np'''	)

       UpperCAmelCase_							:      Union[str, Any]						=   output.images

       UpperCAmelCase_							:      Tuple						=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase_							:      int						=   np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''       ,   subfolder='''scheduler'''	)

       UpperCAmelCase_							:      Optional[int]						=   AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''       ,   scheduler=lowerCamelCase__       ,   safety_checker=lowerCamelCase__	)
       UpperCAmelCase_							:      Dict						=   alt_pipe.to(lowerCamelCase__	)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

       UpperCAmelCase_							:      Optional[int]						=   '''A painting of a squirrel eating a burger'''
       UpperCAmelCase_							:      Dict						=   torch.manual_seed(0	)

       UpperCAmelCase_							:      List[str]						=   alt_pipe([prompt]       ,   generator=lowerCamelCase__       ,   num_inference_steps=2       ,   output_type='''numpy'''	)
       UpperCAmelCase_							:      Optional[int]						=   output.images

       UpperCAmelCase_							:      Any						=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase_							:      str						=   np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2



import math

import qiskit
def      snake_case_						( __lowercase = 1			,       __lowercase = 1			,       __lowercase = 1       ):
   if (
       isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
   ):
      raise TypeError('''inputs must be integers.'''       )

   if (input_a < 0) or (input_a < 0) or (carry_in < 0):
      raise ValueError('''inputs must be positive.'''       )

   if (
       (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != carry_in)
   ):
      raise ValueError('''inputs must be exact integers.'''       )

   if (input_a > 2) or (input_a > 2) or (carry_in > 2):
      raise ValueError('''inputs must be less or equal to 2.'''       )

   # build registers
   UpperCAmelCase_							:      Any						=   qiskit.QuantumRegister(4			,       '''qr'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.ClassicalRegister(2			,       '''cr'''       )
   # list the entries
   UpperCAmelCase_							:      Any						=   [input_a, input_a, carry_in]

   UpperCAmelCase_							:      Dict						=   qiskit.QuantumCircuit(__lowercase			,       __lowercase       )

   for i in range(0			,       3       ):
      if entry[i] == 2:
         quantum_circuit.h(__lowercase       )  # for hadamard entries
      elif entry[i] == 1:
         quantum_circuit.x(__lowercase       )  # for 1 entries
      elif entry[i] == 0:
         quantum_circuit.i(__lowercase       )  # for 0 entries

    # build the circuit
   quantum_circuit.ccx(0			,       1			,       3       )  # ccx = toffoli gate
   quantum_circuit.cx(0			,       1       )
   quantum_circuit.ccx(1			,       2			,       3       )
   quantum_circuit.cx(1			,       2       )
   quantum_circuit.cx(0			,       1       )

   quantum_circuit.measure([2, 3]			,       __lowercase       )  # measure the last two qbits

   UpperCAmelCase_							:      Optional[int]						=   qiskit.Aer.get_backend('''aer_simulator'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.execute(__lowercase			,       __lowercase			,       shots=1_0_0_0       )

   return job.result().get_counts(__lowercase       )


if __name__ == "__main__":
       print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')