



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


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


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__()

											if hasattr(scheduler.config ,					"steps_offset"				) and scheduler.config.steps_offset != 1:
															a__	:							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															    f'The configuration file of this scheduler: {scheduler} is outdated.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				)
															a__	:							List[Any]      						= dict(scheduler.config				)
															a__	:							int      						= 1
															a__	:							List[Any]      						= FrozenDict(__UpperCAmelCase				)

											if hasattr(scheduler.config ,					"skip_prk_steps"				) and scheduler.config.skip_prk_steps is False:
															a__	:							Tuple      						= (
															    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				)
															a__	:							Optional[Any]      						= dict(scheduler.config				)
															a__	:							int      						= True
															a__	:							Union[str, 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 ,					__UpperCAmelCase = "auto"				):







											"""simple docstring"""



											if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															a__	:							Optional[int]      						= self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.enable_attention_slicing(__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError("Please install accelerate via `pip install accelerate`"				)

											a__	:							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				):







											"""simple docstring"""



											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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = 512 ,					__UpperCAmelCase = 512 ,					__UpperCAmelCase = 50 ,					__UpperCAmelCase = 7.5 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 1 ,					__UpperCAmelCase = 0.0 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = "pil" ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 1 ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							str      						= self.segmentation_processor(
											    text=[text] ,					images=[image] ,					padding="max_length" ,					return_tensors="pt"				).to(self.device				)
											a__	:							Dict      						= self.segmentation_model(**__UpperCAmelCase				)
											a__	:							Optional[Any]      						= torch.sigmoid(outputs.logits				).cpu().detach().unsqueeze(-1				).numpy()
											a__	:							List[Any]      						= self.numpy_to_pil(__UpperCAmelCase				)[0].resize(image.size				)

											# Run inpainting pipeline with the generated mask
											a__	:							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
import requests

from transformers.testing_utils import 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

						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCamelCase		       =      False

if is_vision_available():
						from PIL import Image

						from transformers import PixaStructImageProcessor


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=18 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							str      						= size if size is not None else {"height": 20, "width": 20}
											a__	:							Optional[int]      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							List[Any]      						= num_channels
											a__	:							Union[str, Any]      						= image_size
											a__	:							List[str]      						= min_resolution
											a__	:							int      						= max_resolution
											a__	:							int      						= size
											a__	:							List[str]      						= do_normalize
											a__	:							Any      						= do_convert_rgb
											a__	:							Optional[Any]      						= [512, 1024, 2048, 4096]
											a__	:							Any      						= patch_size if patch_size is not None else {"height": 16, "width": 16}



							def 		_A	(							self				):







											"""simple docstring"""



											return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
											a__	:							Union[str, Any]      						= Image.open(requests.get(__UpperCAmelCase ,					stream=__UpperCAmelCase				).raw				).convert("RGB"				)
											return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,  )
@require_torch
@require_vision
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= PixaStructImageProcessingTester(self				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.image_processor_tester.prepare_dummy_image()

											a__	:							Any      						= self.image_processing_class(**self.image_processor_dict				)
											a__	:							List[str]      						= 2048

											a__	:							Union[str, Any]      						= image_processor(__UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				)
											self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,					torch.tensor(0.0_6_0_6				) ,					atol=1E-3 ,					rtol=1E-3				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							str      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							List[Any]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							Optional[Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[Any]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Any      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							Optional[int]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											a__	:							int      						= True

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															with self.assertRaises(__UpperCAmelCase				):
																			a__	:							List[str]      						= image_processor(
																			    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches

															a__	:							Tuple      						= "Hello"

															a__	:							Union[str, Any]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase ,					header_text=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											a__	:							Tuple      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase ,					numpify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					np.ndarray				)

											a__	:							List[str]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							Any      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							str      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							Any      						= 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
											a__	:							Optional[Any]      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * self.image_processor_tester.num_channels
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							str      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							Optional[int]      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."       ,  )
@require_torch
@require_vision
class 				_a						(       SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[str] =       PixaStructImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= PixaStructImageProcessingTester(self ,					num_channels=4				)
											a__	:							Tuple      						= 3



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Union[str, Any]      						= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							str      						= (
											    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
											    * (self.image_processor_tester.num_channels - 1)
											) + 2

											for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															a__	:							List[str]      						= image_processor(
															    image_inputs[0] ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(1, max_patch, expected_hidden_dim) ,					)

															# Test batched
															a__	:							int      						= image_processor(
															    __UpperCAmelCase ,					return_tensors="pt" ,					max_patches=__UpperCAmelCase				).flattened_patches
															self.assertEqual(
															    encoded_images.shape ,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,					)


