


"""simple docstring"""







import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[Any]   =StableDiffusionDiffEditPipeline
       a  :				Dict   =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       a  :				Dict   =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       a  :				Optional[Any]   =frozenset(
           []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       a  :				Union[str, Any]   =frozenset([]				)

       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     Dict								=							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		,     attention_head_dim=(2, 4)		,     use_linear_projection=snake_case__		,     )
         lowerCAmelCase     :     List[str]								=							DDIMScheduler(
             beta_start=0.00085		,     beta_end=0.012		,     beta_schedule="scaled_linear"		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     )
         lowerCAmelCase     :     int								=							DDIMInverseScheduler(
             beta_start=0.00085		,     beta_end=0.012		,     beta_schedule="scaled_linear"		,     clip_sample=snake_case__		,     set_alpha_to_zero=snake_case__		,     )
         torch.manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							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		,     sample_size=128		,     )
         torch.manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							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=1_000		,     hidden_act="gelu"		,     projection_dim=512		,     )
         lowerCAmelCase     :     Optional[int]								=							CLIPTextModel(snake_case__						)
         lowerCAmelCase     :     int								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         lowerCAmelCase     :     Tuple								=							{
             "unet": unet,
             "scheduler": scheduler,
             "inverse_scheduler": inverse_scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }

         return components

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							floats_tensor((1, 16, 16)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     Any								=							floats_tensor((1, 2, 4, 16, 16)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Any								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     Tuple								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase     :     int								=							{
             "prompt": "a dog and a newt",
             "mask_image": mask,
             "image_latents": latents,
             "generator": generator,
             "num_inference_steps": 2,
             "inpaint_strength": 1.0,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }

         return inputs

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							floats_tensor((1, 3, 32, 32)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							image.cpu().permute(0		,     2		,     3		,     1						)[0]
         lowerCAmelCase     :     List[Any]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						)
         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Optional[int]								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     Optional[Any]								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase     :     int								=							{
             "image": image,
             "source_prompt": "a cat and a frog",
             "target_prompt": "a dog and a newt",
             "generator": generator,
             "num_inference_steps": 2,
             "num_maps_per_mask": 2,
             "mask_encode_strength": 1.0,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }

         return inputs

       def        lowercase__							(						self		,     snake_case__		,     snake_case__=0						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							floats_tensor((1, 3, 32, 32)		,     rng=random.Random(snake_case__						)						).to(snake_case__						)
         lowerCAmelCase     :     Dict								=							image.cpu().permute(0		,     2		,     3		,     1						)[0]
         lowerCAmelCase     :     Optional[Any]								=							Image.fromarray(np.uinta(snake_case__						)						).convert("RGB"						)
         if str(snake_case__						).startswith("mps"						):
           lowerCAmelCase     :     Dict								=							torch.manual_seed(snake_case__						)
         else:
           lowerCAmelCase     :     int								=							torch.Generator(device=snake_case__						).manual_seed(snake_case__						)
         lowerCAmelCase     :     Dict								=							{
             "image": image,
             "prompt": "a cat and a frog",
             "generator": generator,
             "num_inference_steps": 2,
             "inpaint_strength": 1.0,
             "guidance_scale": 6.0,
             "decode_latents": True,
             "output_type": "numpy",
         }
         return inputs

       def        lowercase__							(						self						):






         """simple docstring"""





         if not hasattr(self.pipeline_class		,     "_optional_components"						):
           return

         lowerCAmelCase     :     Dict								=							self.get_dummy_components()
         lowerCAmelCase     :     Union[str, Any]								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)
         pipe.set_progress_bar_config(disable=snake_case__						)

         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
           setattr(snake_case__		,     snake_case__		,     snake_case__						)
         pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}						)

         lowerCAmelCase     :     Dict								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							pipe(**snake_case__						)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(snake_case__						)
           lowerCAmelCase     :     List[str]								=							self.pipeline_class.from_pretrained(snake_case__						)
           pipe_loaded.to(snake_case__						)
           pipe_loaded.set_progress_bar_config(disable=snake_case__						)

         for optional_component in pipe._optional_components:
           self.assertTrue(
               getattr(snake_case__		,     snake_case__						) is None		,     f"""`{optional_component}` did not stay set to None after loading."""		,     )

         lowerCAmelCase     :     int								=							self.get_dummy_inputs(snake_case__						)
         lowerCAmelCase     :     Tuple								=							pipe_loaded(**snake_case__						)[0]

         lowerCAmelCase     :     List[Any]								=							np.abs(output - output_loaded						).max()
         self.assertLess(snake_case__		,     1e-4						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							"cpu"

         lowerCAmelCase     :     int								=							self.get_dummy_components()
         lowerCAmelCase     :     List[Any]								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     int								=							self.get_dummy_mask_inputs(snake_case__						)
         lowerCAmelCase     :     List[str]								=							pipe.generate_mask(**snake_case__						)
         lowerCAmelCase     :     Dict								=							mask[0, -3:, -3:]

         self.assertEqual(mask.shape		,     (1, 16, 16)						)
         lowerCAmelCase     :     Optional[int]								=							np.array([0] * 9						)
         lowerCAmelCase     :     Any								=							np.abs(mask_slice.flatten() - expected_slice						).max()
         self.assertLessEqual(snake_case__		,     1e-3						)
         self.assertEqual(mask[0, -3, -4]		,     0						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							"cpu"

         lowerCAmelCase     :     List[str]								=							self.get_dummy_components()
         lowerCAmelCase     :     str								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Any								=							self.get_dummy_inversion_inputs(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							pipe.invert(**snake_case__						).images
         lowerCAmelCase     :     List[str]								=							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape		,     (2, 32, 32, 3)						)
         lowerCAmelCase     :     List[Any]								=							np.array(
             [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,     )
         lowerCAmelCase     :     str								=							np.abs(image_slice.flatten() - expected_slice						).max()
         self.assertLessEqual(snake_case__		,     1e-3						)

       def        lowercase__							(						self						):






         """simple docstring"""





         super().test_inference_batch_single_identical(expected_max_diff=5e-3						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							"cpu"

         lowerCAmelCase     :     Tuple								=							self.get_dummy_components()

         lowerCAmelCase     :     Optional[int]								=							{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
         lowerCAmelCase     :     Dict								=							DPMSolverMultistepScheduler(**snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							DPMSolverMultistepInverseScheduler(**snake_case__						)

         lowerCAmelCase     :     Optional[int]								=							self.pipeline_class(**snake_case__						)
         pipe.to(snake_case__						)
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     int								=							self.get_dummy_inversion_inputs(snake_case__						)
         lowerCAmelCase     :     str								=							pipe.invert(**snake_case__						).images
         lowerCAmelCase     :     str								=							image[0, -1, -3:, -3:]

         self.assertEqual(image.shape		,     (2, 32, 32, 3)						)
         lowerCAmelCase     :     int								=							np.array(
             [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,     )
         lowerCAmelCase     :     Optional[int]								=							np.abs(image_slice.flatten() - expected_slice						).max()
         self.assertLessEqual(snake_case__		,     1e-3						)


@require_torch_gpu
@slow
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





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

       @classmethod
       def        lowercase__							(						cls						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"						)

         lowerCAmelCase     :     Union[str, Any]								=							raw_image.convert("RGB"						).resize((768, 768)						)

         lowerCAmelCase     :     List[Any]								=							raw_image

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							torch.manual_seed(0						)

         lowerCAmelCase     :     List[str]								=							StableDiffusionDiffEditPipeline.from_pretrained(
             "stabilityai/stable-diffusion-2-1"		,     safety_checker=snake_case__		,     torch_dtype=torch.floataa						)
         lowerCAmelCase     :     Dict								=							DDIMScheduler.from_config(pipe.scheduler.config						)
         lowerCAmelCase     :     Optional[Any]								=							DDIMInverseScheduler.from_config(pipe.scheduler.config						)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Tuple								=							"a bowl of fruit"
         lowerCAmelCase     :     Tuple								=							"a bowl of pears"

         lowerCAmelCase     :     List[Any]								=							pipe.generate_mask(
             image=self.raw_image		,     source_prompt=snake_case__		,     target_prompt=snake_case__		,     generator=snake_case__		,     )

         lowerCAmelCase     :     Dict								=							pipe.invert(
             prompt=snake_case__		,     image=self.raw_image		,     inpaint_strength=0.7		,     generator=snake_case__						).latents

         lowerCAmelCase     :     str								=							pipe(
             prompt=snake_case__		,     mask_image=snake_case__		,     image_latents=snake_case__		,     generator=snake_case__		,     negative_prompt=snake_case__		,     inpaint_strength=0.7		,     output_type="numpy"		,     ).images[0]

         lowerCAmelCase     :     Dict								=							(
             np.array(
                 load_image(
                     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                     "/diffedit/pears.png"						).resize((768, 768)						)						)
             / 255
         )
         assert np.abs((expected_image - image).max()						) < 5e-1







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							torch.manual_seed(0						)

         lowerCAmelCase     :     int								=							StableDiffusionDiffEditPipeline.from_pretrained(
             "stabilityai/stable-diffusion-2-1"		,     safety_checker=snake_case__		,     torch_dtype=torch.floataa						)
         lowerCAmelCase     :     Tuple								=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
         lowerCAmelCase     :     Any								=							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config						)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Tuple								=							"a bowl of fruit"
         lowerCAmelCase     :     Optional[Any]								=							"a bowl of pears"

         lowerCAmelCase     :     int								=							pipe.generate_mask(
             image=self.raw_image		,     source_prompt=snake_case__		,     target_prompt=snake_case__		,     generator=snake_case__		,     )

         lowerCAmelCase     :     List[Any]								=							pipe.invert(
             prompt=snake_case__		,     image=self.raw_image		,     inpaint_strength=0.7		,     generator=snake_case__		,     num_inference_steps=25		,     ).latents

         lowerCAmelCase     :     int								=							pipe(
             prompt=snake_case__		,     mask_image=snake_case__		,     image_latents=snake_case__		,     generator=snake_case__		,     negative_prompt=snake_case__		,     inpaint_strength=0.7		,     num_inference_steps=25		,     output_type="numpy"		,     ).images[0]

         lowerCAmelCase     :     Union[str, Any]								=							(
             np.array(
                 load_image(
                     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                     "/diffedit/pears.png"						).resize((768, 768)						)						)
             / 255
         )
         assert np.abs((expected_image - image).max()						) < 5e-1









"""simple docstring"""







lowerCAmelCase__      				=   [
    '''Audio''',
    '''Array2D''',
    '''Array3D''',
    '''Array4D''',
    '''Array5D''',
    '''ClassLabel''',
    '''Features''',
    '''Sequence''',
    '''Value''',
    '''Image''',
    '''Translation''',
    '''TranslationVariableLanguages''',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages





