






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


snake_case    				=		logging.get_logger(__name__)  # pylint: disable=invalid-name


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	CLIPSegForImageSegmentation					, UpperCAmelCase_       :	CLIPSegProcessor					, UpperCAmelCase_       :	AutoencoderKL					, UpperCAmelCase_       :	CLIPTextModel					, UpperCAmelCase_       :	CLIPTokenizer					, UpperCAmelCase_       :	UNetaDConditionModel					, UpperCAmelCase_       :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					, UpperCAmelCase_       :	StableDiffusionSafetyChecker					, UpperCAmelCase_       :	CLIPImageProcessor					, ):
       super().__init__()

       if hasattr(scheduler.config					, "steps_offset"      ) and scheduler.config.steps_offset != 1:
         SCREAMING_SNAKE_CASE       :      Optional[int]							=    (
             f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
             f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
             "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
             " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
             " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
             " file"
         )
         deprecate("steps_offset!=1"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    dict(scheduler.config      )
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
         SCREAMING_SNAKE_CASE       :      int							=    FrozenDict(UpperCAmelCase_      )

       if hasattr(scheduler.config					, "skip_prk_steps"      ) and scheduler.config.skip_prk_steps is False:
         SCREAMING_SNAKE_CASE       :      Any							=    (
             f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
             " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
             " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
             " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
             " Hub, it would be very nice if you could open a Pull request for the"
             " `scheduler/scheduler_config.json` file"
         )
         deprecate("skip_prk_steps not set"					, "1.0.0"					, UpperCAmelCase_					, standard_warn=UpperCAmelCase_      )
         SCREAMING_SNAKE_CASE       :      int							=    dict(scheduler.config      )
         SCREAMING_SNAKE_CASE       :      List[str]							=    True
         SCREAMING_SNAKE_CASE       :      List[Any]							=    FrozenDict(UpperCAmelCase_      )

       if safety_checker is None:
         logger.warning(
             f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
             " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
             " results in services or applications open to the public. Both the diffusers team and Hugging Face"
             " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
             " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
             " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."      )

       self.register_modules(
           segmentation_model=UpperCAmelCase_					, segmentation_processor=UpperCAmelCase_					, vae=UpperCAmelCase_					, text_encoder=UpperCAmelCase_					, tokenizer=UpperCAmelCase_					, unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_					, safety_checker=UpperCAmelCase_					, feature_extractor=UpperCAmelCase_					, )


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Optional[Union[str, int]] = "auto"      ):
       if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         SCREAMING_SNAKE_CASE       :      Dict							=    self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       self.enable_attention_slicing(UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]      ):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("Please install accelerate via `pip install accelerate`"      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.device("cuda"      )

       for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
         if cpu_offloaded_model is not None:
           cpu_offload(UpperCAmelCase_					, UpperCAmelCase_      )


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				_A       (				self       :	Tuple      ):
       if self.device != torch.device("meta"      ) or not hasattr(self.unet					, "_hf_hook"      ):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(UpperCAmelCase_					, "_hf_hook"      )
             and hasattr(module._hf_hook					, "execution_device"      )
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device      )
       return self.device






     @torch.no_grad()
     def __call__(				self       :	int					, UpperCAmelCase_       :	Union[str, List[str]]					, UpperCAmelCase_       :	Union[torch.FloatTensor, PIL.Image.Image]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 50					, UpperCAmelCase_       :	float = 7.5					, UpperCAmelCase_       :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase_       :	Optional[int] = 1					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	Optional[torch.Generator] = None					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	Optional[str] = "pil"					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase_       :	int = 1					, **UpperCAmelCase_       :	Optional[Any]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.segmentation_processor(
           text=[text]					, images=[image]					, padding="max_length"					, return_tensors="pt"      ).to(self.device      )
       SCREAMING_SNAKE_CASE       :      Dict							=    self.segmentation_model(**UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
       SCREAMING_SNAKE_CASE       :      Any							=    self.numpy_to_pil(UpperCAmelCase_      )[0].resize(image.size      )

       # Run inpainting pipeline with the generated mask
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionInpaintPipeline(
           vae=self.vae					, text_encoder=self.text_encoder					, tokenizer=self.tokenizer					, unet=self.unet					, scheduler=self.scheduler					, safety_checker=self.safety_checker					, feature_extractor=self.feature_extractor					, )
       return inpainting_pipeline(
           prompt=UpperCAmelCase_					, image=UpperCAmelCase_					, mask_image=UpperCAmelCase_					, height=UpperCAmelCase_					, width=UpperCAmelCase_					, num_inference_steps=UpperCAmelCase_					, guidance_scale=UpperCAmelCase_					, negative_prompt=UpperCAmelCase_					, num_images_per_prompt=UpperCAmelCase_					, eta=UpperCAmelCase_					, generator=UpperCAmelCase_					, latents=UpperCAmelCase_					, output_type=UpperCAmelCase_					, return_dict=UpperCAmelCase_					, callback=UpperCAmelCase_					, callback_steps=UpperCAmelCase_					, )














import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import DonutImageProcessor


class  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def __init__(				self       :	int					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple=7					, UpperCAmelCase_       :	Dict=3					, UpperCAmelCase_       :	Any=18					, UpperCAmelCase_       :	Optional[int]=30					, UpperCAmelCase_       :	Any=400					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	int=True					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	List[Any]=True					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase_       :	Dict=[0.5, 0.5, 0.5]					, ):
       SCREAMING_SNAKE_CASE       :      Any							=    parent
       SCREAMING_SNAKE_CASE       :      int							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    num_channels
       SCREAMING_SNAKE_CASE       :      str							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min_resolution
       SCREAMING_SNAKE_CASE       :      Any							=    max_resolution
       SCREAMING_SNAKE_CASE       :      int							=    do_resize
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    size if size is not None else {"height": 18, "width": 20}
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_thumbnail
       SCREAMING_SNAKE_CASE       :      Tuple							=    do_align_axis
       SCREAMING_SNAKE_CASE       :      List[str]							=    do_pad
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    do_normalize
       SCREAMING_SNAKE_CASE       :      Any							=    image_mean
       SCREAMING_SNAKE_CASE       :      Any							=    image_std






     def 				_A       (				self       :	str      ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	DonutImageProcessor if is_vision_available() else None


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    DonutImageProcessingTester(self      )


     @property
     def 				_A       (				self       :	Tuple      ):
       return self.image_processor_tester.prepare_image_processor_dict()


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "size"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_thumbnail"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_align_long_axis"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_pad"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"      )      )
       self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"      )      )


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size					, {"height": 18, "width": 20}      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=42      )
       self.assertEqual(image_processor.size					, {"height": 42, "width": 42}      )

       # Previous config had dimensions in (width, height) order
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class.from_dict(self.image_processor_dict					, size=(42, 84)      )
       self.assertEqual(image_processor.size					, {"height": 84, "width": 42}      )


     def 				_A       (				self       :	Dict      ):
       pass


     @is_flaky()
     def 				_A       (				self       :	Optional[Any]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, Image.Image      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      List[str]							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Dict							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )


     @is_flaky()
     def 				_A       (				self       :	List[str]      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE       :      List[Any]							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, np.ndarray      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      Any							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






     @is_flaky()
     def 				_A       (				self       :	Dict      ):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE       :      Tuple							=    prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_      )
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_					, torch.Tensor      )

       # Test not batched input
       SCREAMING_SNAKE_CASE       :      int							=    image_processing(image_inputs[0]					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )

       # Test batched
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    image_processing(UpperCAmelCase_					, return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape					, (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           )					, )






