




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class   A(						__UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						StableDiffusionInstructPixaPixPipeline
 UpperCamelCase						       =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
 UpperCamelCase						       =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCamelCase						       =						IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCamelCase						       =						IMAGE_TO_IMAGE_IMAGE_PARAMS

 def     a__			(    self :  int  )       ->     Optional[Any]:

       """simple docstring"""






       torch.manual_seed(0  )
       lowerCamelCase_							=   UNetaDConditionModel(
           block_out_channels=(32, 64)						,							layers_per_block=2						,							sample_size=32						,							in_channels=8						,							out_channels=4						,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,							cross_attention_dim=32						,							)
       lowerCamelCase_							=   PNDMScheduler(skip_prk_steps=A_  )
       torch.manual_seed(0  )
       lowerCamelCase_							=   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						,							)
       torch.manual_seed(0  )
       lowerCamelCase_							=   CLIPTextConfig(
           bos_token_id=0						,							eos_token_id=2						,							hidden_size=32						,							intermediate_size=37						,							layer_norm_eps=1E-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=1000						,							)
       lowerCamelCase_							=   CLIPTextModel(A_  )
       lowerCamelCase_							=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

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

 def     a__			(    self :  Optional[int]						,							A_ :  Tuple						,							A_ :  Dict=0  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   floats_tensor((1, 3, 32, 32)						,							rng=random.Random(A_  )  ).to(A_  )
       lowerCamelCase_							=   image.cpu().permute(0						,							2						,							3						,							1  )[0]
       lowerCamelCase_							=   Image.fromarray(np.uinta(A_  )  ).convert('RGB'  )
       if str(A_  ).startswith('mps'  ):
             lowerCamelCase_							=   torch.manual_seed(A_  )
       else:
             lowerCamelCase_							=   torch.Generator(device=A_  ).manual_seed(A_  )
       lowerCamelCase_							=   {
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'image_guidance_scale': 1,
           'output_type': 'numpy',
       }
       return inputs

 def     a__			(    self :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							=   self.get_dummy_components()
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline(**A_  )
       lowerCamelCase_							=   sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   self.get_dummy_inputs(A_  )
       lowerCamelCase_							=   sd_pipe(**A_  ).images
       lowerCamelCase_							=   image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_							=   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3

 def     a__			(    self :  Optional[int]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							=   self.get_dummy_components()
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline(**A_  )
       lowerCamelCase_							=   sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   self.get_dummy_inputs(A_  )
       lowerCamelCase_							=   'french fries'
       lowerCamelCase_							=   sd_pipe(**A_						,							negative_prompt=A_  )
       lowerCamelCase_							=   output.images
       lowerCamelCase_							=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_							=   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3

 def     a__			(    self :  Optional[int]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							=   self.get_dummy_components()
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline(**A_  )
       lowerCamelCase_							=   sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   self.get_dummy_inputs(A_  )
       lowerCamelCase_							=   [inputs['prompt']] * 2

       lowerCamelCase_							=   np.array(inputs['image']  ).astype(np.floataa  ) / 255.0
       lowerCamelCase_							=   torch.from_numpy(A_  ).unsqueeze(0  ).to(A_  )
       lowerCamelCase_							=   image / 2 + 0.5
       lowerCamelCase_							=   image.permute(0						,							3						,							1						,							2  )
       lowerCamelCase_							=   image.repeat(2						,							1						,							1						,							1  )

       lowerCamelCase_							=   sd_pipe(**A_  ).images
       lowerCamelCase_							=   image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       lowerCamelCase_							=   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3

 def     a__			(    self :  Optional[int]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							=   self.get_dummy_components()
       lowerCamelCase_							=   EulerAncestralDiscreteScheduler(
           beta_start=0.00085						,							beta_end=0.012						,							beta_schedule='scaled_linear'  )
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline(**A_  )
       lowerCamelCase_							=   sd_pipe.to(A_  )
       sd_pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   self.get_dummy_inputs(A_  )
       lowerCamelCase_							=   sd_pipe(**A_  ).images
       lowerCamelCase_							=   image[0, -3:, -3:, -1]

       lowerCamelCase_							=   [round(A_						,							4  ) for x in image_slice.flatten().tolist()]
       print(','.join([str(A_  ) for x in slice]  )  )

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_							=   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3

 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       super().test_inference_batch_single_identical(expected_max_diff=3E-3  )





 def     a__			(    self :  List[str]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self.get_dummy_components()
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline(**A_  )
       lowerCamelCase_							=   VaeImageProcessor(do_resize=A_						,							do_normalize=A_  )
       lowerCamelCase_							=   pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )

       lowerCamelCase_							=   pipe(**self.get_dummy_inputs_by_type(A_						,							input_image_type='pt'  )  )[0]

       lowerCamelCase_							=   components['vae']
       lowerCamelCase_							=   self.get_dummy_inputs_by_type(A_						,							input_image_type='pt'  )

       for image_param in self.image_latents_params:
             if image_param in inputs.keys():
                   lowerCamelCase_							=   vae.encode(inputs[image_param]  ).latent_dist.mode()

       lowerCamelCase_							=   pipe(**A_  )[0]

       lowerCamelCase_							=   np.abs(out - out_latents_inputs  ).max()
       self.assertLess(A_						,							1E-4						,							'passing latents as image input generate different result from passing image'  )







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



 '''simple docstring'''


 def     a__			(    self :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def     a__			(    self :  Optional[Any]						,							A_ :  Any=0  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   torch.manual_seed(A_  )
       lowerCamelCase_							=   load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'  )
       lowerCamelCase_							=   {
           'prompt': 'turn him into a cyborg',
           'image': image,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'image_guidance_scale': 1.0,
           'output_type': 'numpy',
       }
       return inputs

 def     a__			(    self :  Union[str, Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'						,							safety_checker=A_  )
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_							=   self.get_inputs()
       lowerCamelCase_							=   pipe(**A_  ).images
       lowerCamelCase_							=   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]  )

       assert np.abs(expected_slice - image_slice  ).max() < 1E-3

 def     a__			(    self :  Dict  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'						,							safety_checker=A_  )
       lowerCamelCase_							=   LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_							=   self.get_inputs()
       lowerCamelCase_							=   pipe(**A_  ).images
       lowerCamelCase_							=   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]  )

       assert np.abs(expected_slice - image_slice  ).max() < 1E-3

 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'						,							safety_checker=A_  )
       lowerCamelCase_							=   DDIMScheduler.from_config(pipe.scheduler.config  )
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_							=   self.get_inputs()
       lowerCamelCase_							=   pipe(**A_  ).images
       lowerCamelCase_							=   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]  )

       assert np.abs(expected_slice - image_slice  ).max() < 1E-3

 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   0

       def callback_fn(A_ :  int						,							A_ :  int						,							A_ :  torch.FloatTensor  ) -> None:
             lowerCamelCase_							=   True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                   lowerCamelCase_							=   latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   lowerCamelCase_							=   latents[0, -3:, -3:, -1]
                   lowerCamelCase_							=   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]  )

                   assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2
             elif step == 2:
                   lowerCamelCase_							=   latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   lowerCamelCase_							=   latents[0, -3:, -3:, -1]
                   lowerCamelCase_							=   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]  )

                   assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2

       lowerCamelCase_							=   False

       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'						,							safety_checker=A_						,							torch_dtype=torch.floataa  )
       lowerCamelCase_							=   pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_							=   self.get_inputs()
       pipe(**A_						,							callback=A_						,							callback_steps=1  )
       assert callback_fn.has_been_called
       assert number_of_steps == 3

 def     a__			(    self :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix'						,							safety_checker=A_						,							torch_dtype=torch.floataa  )
       lowerCamelCase_							=   pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing(1  )
       pipe.enable_sequential_cpu_offload()

       lowerCamelCase_							=   self.get_inputs()
       lowerCamelCase_							=   pipe(**A_  )

       lowerCamelCase_							=   torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9





 def     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       lowerCamelCase_							=   inputs['image'].resize((504, 504)  )

       lowerCamelCase_							=   'timbrooks/instruct-pix2pix'
       lowerCamelCase_							=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
           A_						,							safety_checker=A_						,							)
       pipe.to(A_  )
       pipe.set_progress_bar_config(disable=A_  )
       pipe.enable_attention_slicing()

       lowerCamelCase_							=   pipe(**A_  )
       lowerCamelCase_							=   output.images[0]

       lowerCamelCase_							=   image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       lowerCamelCase_							=   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))



