





'''simple docstring'''




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 			_lowerCAmelCase		(			A__							,     A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   StableDiffusionInstructPixaPixPipeline
     snake_case_						      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     snake_case_						      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     snake_case_						      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
     snake_case_						      =   IMAGE_TO_IMAGE_IMAGE_PARAMS



     def      lowerCAmelCase			(   self							:			int						)->	str:
        torch.manual_seed(0						)
        snake_case 						=   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   ,       )
        snake_case 						=   PNDMScheduler(skip_prk_steps=__snake_case						)
        torch.manual_seed(0						)
        snake_case 						=   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						)
        snake_case 						=   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=10_00   ,       )
        snake_case 						=   CLIPTextModel(__snake_case						)
        snake_case 						=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

        snake_case 						=   {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			Any   ,       __snake_case							:			Optional[int]=0						)->	Tuple:
        snake_case 						=   floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(__snake_case						)						).to(__snake_case						)
        snake_case 						=   image.cpu().permute(0   ,       2   ,       3   ,       1						)[0]
        snake_case 						=   Image.fromarray(np.uinta(__snake_case						)						).convert("""RGB"""						)
        if str(__snake_case						).startswith("""mps"""						):
           snake_case 						=   torch.manual_seed(__snake_case						)
        else:
           snake_case 						=   torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
        snake_case 						=   {
            """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      lowerCAmelCase			(   self							:			Dict						)->	List[str]:
        snake_case 						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case 						=   self.get_dummy_components()
        snake_case 						=   StableDiffusionInstructPixaPixPipeline(**__snake_case						)
        snake_case 						=   sd_pipe.to(__snake_case						)
        sd_pipe.set_progress_bar_config(disable=__snake_case						)

        snake_case 						=   self.get_dummy_inputs(__snake_case						)
        snake_case 						=   sd_pipe(**__snake_case						).images
        snake_case 						=   image[0, -3:, -3:, -1]
        assert image.shape == (1, 32, 32, 3)
        snake_case 						=   np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]						)

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



     def      lowerCAmelCase			(   self							:			Tuple						)->	Dict:
        snake_case 						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case 						=   self.get_dummy_components()
        snake_case 						=   StableDiffusionInstructPixaPixPipeline(**__snake_case						)
        snake_case 						=   sd_pipe.to(__snake_case						)
        sd_pipe.set_progress_bar_config(disable=__snake_case						)

        snake_case 						=   self.get_dummy_inputs(__snake_case						)
        snake_case 						=   """french fries"""
        snake_case 						=   sd_pipe(**__snake_case   ,       negative_prompt=__snake_case						)
        snake_case 						=   output.images
        snake_case 						=   image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        snake_case 						=   np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]						)

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



     def      lowerCAmelCase			(   self							:			List[Any]						)->	Tuple:
        snake_case 						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case 						=   self.get_dummy_components()
        snake_case 						=   StableDiffusionInstructPixaPixPipeline(**__snake_case						)
        snake_case 						=   sd_pipe.to(__snake_case						)
        sd_pipe.set_progress_bar_config(disable=__snake_case						)

        snake_case 						=   self.get_dummy_inputs(__snake_case						)
        snake_case 						=   [inputs["""prompt"""]] * 2

        snake_case 						=   np.array(inputs["""image"""]						).astype(np.floataa						) / 2_55.0
        snake_case 						=   torch.from_numpy(__snake_case						).unsqueeze(0						).to(__snake_case						)
        snake_case 						=   image / 2 + 0.5
        snake_case 						=   image.permute(0   ,       3   ,       1   ,       2						)
        snake_case 						=   image.repeat(2   ,       1   ,       1   ,       1						)

        snake_case 						=   sd_pipe(**__snake_case						).images
        snake_case 						=   image[-1, -3:, -3:, -1]

        assert image.shape == (2, 32, 32, 3)
        snake_case 						=   np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]						)

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



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[int]:
        snake_case 						=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        snake_case 						=   self.get_dummy_components()
        snake_case 						=   EulerAncestralDiscreteScheduler(
            beta_start=0.0_00_85   ,       beta_end=0.0_12   ,       beta_schedule="""scaled_linear"""						)
        snake_case 						=   StableDiffusionInstructPixaPixPipeline(**__snake_case						)
        snake_case 						=   sd_pipe.to(__snake_case						)
        sd_pipe.set_progress_bar_config(disable=__snake_case						)

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

        snake_case 						=   [round(__snake_case   ,       4						) for x in image_slice.flatten().tolist()]
        print(""",""".join([str(__snake_case						) for x in slice]						)						)

        assert image.shape == (1, 32, 32, 3)
        snake_case 						=   np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]						)

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



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	str:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	Optional[Any]:
        snake_case 						=   self.get_dummy_components()
        snake_case 						=   StableDiffusionInstructPixaPixPipeline(**__snake_case						)
        snake_case 						=   VaeImageProcessor(do_resize=__snake_case   ,       do_normalize=__snake_case						)
        snake_case 						=   pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)

        snake_case 						=   pipe(**self.get_dummy_inputs_by_type(__snake_case   ,       input_image_type="""pt"""						)						)[0]

        snake_case 						=   components["""vae"""]
        snake_case 						=   self.get_dummy_inputs_by_type(__snake_case   ,       input_image_type="""pt"""						)

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

        snake_case 						=   pipe(**__snake_case						)[0]

        snake_case 						=   np.abs(out - out_latents_inputs						).max()
        self.assertLess(__snake_case   ,       1e-4   ,       """passing latents as image input generate different result from passing image"""						)




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






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			Dict						)->	Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			str=0						)->	Union[str, Any]:
        snake_case 						=   torch.manual_seed(__snake_case						)
        snake_case 						=   load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""						)
        snake_case 						=   {
            """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      lowerCAmelCase			(   self							:			List[str]						)->	Optional[int]:
        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""   ,       safety_checker=__snake_case						)
        pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case 						=   np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]						)

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



     def      lowerCAmelCase			(   self							:			Dict						)->	Optional[Any]:
        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""   ,       safety_checker=__snake_case						)
        snake_case 						=   LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
        pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case 						=   np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]						)

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



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""   ,       safety_checker=__snake_case						)
        snake_case 						=   DDIMScheduler.from_config(pipe.scheduler.config						)
        pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case 						=   np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]						)

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



     def      lowerCAmelCase			(   self							:			Dict						)->	int:
        snake_case 						=   0

        def callback_fn(__snake_case							:			int   ,       __snake_case							:			int   ,       __snake_case							:			torch.FloatTensor						) -> None:
           snake_case 						=   True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
              snake_case 						=   latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              snake_case 						=   latents[0, -3:, -3:, -1]
              snake_case 						=   np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]						)

              assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2
           elif step == 2:
              snake_case 						=   latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              snake_case 						=   latents[0, -3:, -3:, -1]
              snake_case 						=   np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]						)

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

        snake_case 						=   False

        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""   ,       safety_checker=__snake_case   ,       torch_dtype=torch.floataa						)
        snake_case 						=   pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing()

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



     def      lowerCAmelCase			(   self							:			Dict						)->	Optional[int]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""   ,       safety_checker=__snake_case   ,       torch_dtype=torch.floataa						)
        snake_case 						=   pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing(1						)
        pipe.enable_sequential_cpu_offload()

        snake_case 						=   self.get_inputs()
        snake_case 						=   pipe(**__snake_case						)

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



     def      lowerCAmelCase			(   self							:			Dict						)->	Dict:
        snake_case 						=   self.get_inputs()
        # resize to resolution that is divisible by 8 but not 16 or 32
        snake_case 						=   inputs["""image"""].resize((5_04, 5_04)						)

        snake_case 						=   """timbrooks/instruct-pix2pix"""
        snake_case 						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
            __snake_case   ,       safety_checker=__snake_case   ,       )
        pipe.to(__snake_case						)
        pipe.set_progress_bar_config(disable=__snake_case						)
        pipe.enable_attention_slicing()

        snake_case 						=   pipe(**__snake_case						)
        snake_case 						=   output.images[0]

        snake_case 						=   image[2_55:2_58, 3_83:3_86, -1]

        assert image.shape == (5_04, 5_04, 3)
        snake_case 						=   np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]						)

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








'''simple docstring'''




from __future__ import annotations





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							int		)		->  Tuple:  # noqa: E741
   while r - l > 1:
      snake_case 						=   (l + r) // 2
      if v[m] >= key:
         snake_case 						=   m
      else:
         snake_case 						=   m  # noqa: E741
   return r





def 	__lowerCamelCase			(      __lowerCAmelCase       :							list[int]		)		->  int:
   if len(__lowerCAmelCase		) == 0:
      return 0

   snake_case 						=   [0] * len(__lowerCAmelCase		)
   snake_case 						=   1

   snake_case 						=   v[0]

   for i in range(1      ,    len(__lowerCAmelCase		)		):
      if v[i] < tail[0]:
         snake_case 						=   v[i]
      elif v[i] > tail[length - 1]:
         snake_case 						=   v[i]
         length += 1
      else:
         snake_case 						=   v[i]

   return length


if __name__ == "__main__":
 import doctest

 doctest.testmod()

