





'''simple docstring'''

import unittest

import numpy as np

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

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor


class      SCREAMING_SNAKE_CASE__      (   unittest.TestCase		):







					"""simple docstring"""






					def __init__(   self       :      Union[str, Any]		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      Any=7		,	UpperCAmelCase_       :      Dict=3		,	UpperCAmelCase_       :      Any=18		,	UpperCAmelCase_       :      int=30		,	UpperCAmelCase_       :      str=400		,	UpperCAmelCase_       :      Optional[Any]=True		,	UpperCAmelCase_       :      Any=None		,	UpperCAmelCase_       :      Dict=2		,	UpperCAmelCase_       :      int=True		,	UpperCAmelCase_       :      Union[str, Any]=[0.5, 0.5, 0.5]		,	UpperCAmelCase_       :      Dict=[0.5, 0.5, 0.5]		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[Any]     =     size if size is not None else {'''shortest_edge''': 30}
							__UpperCAmelCase	:							List[Any]     =     parent
							__UpperCAmelCase	:							str     =     batch_size
							__UpperCAmelCase	:							Any     =     num_channels
							__UpperCAmelCase	:							Any     =     image_size
							__UpperCAmelCase	:							Any     =     min_resolution
							__UpperCAmelCase	:							List[str]     =     max_resolution
							__UpperCAmelCase	:							List[str]     =     do_resize
							__UpperCAmelCase	:							List[Any]     =     size
							__UpperCAmelCase	:							Any     =     size_divisor
							__UpperCAmelCase	:							Tuple     =     do_normalize
							__UpperCAmelCase	:							Dict     =     image_mean
							__UpperCAmelCase	:							List[str]     =     image_std
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}






					def 		lowerCamelCase_   (   self       :      str		,	UpperCAmelCase_       :      Union[str, Any]		,	UpperCAmelCase_       :      List[Any]=False		):







							"""simple docstring"""






							if not batched:
									__UpperCAmelCase	:							Any     =     self.size['''shortest_edge''']
									__UpperCAmelCase	:							str     =     image_inputs[0]
									if isinstance(lowerCamelCase__		,	Image.Image		):
											__UpperCAmelCase	:							List[str]     =     image.size
									else:
											__UpperCAmelCase	:							Tuple     =     image.shape[1], image.shape[2]
									__UpperCAmelCase	:							int     =     size / min(lowerCamelCase__		,	lowerCamelCase__		)
									if h < w:
											__UpperCAmelCase	:							Any     =     size, scale * w
									else:
											__UpperCAmelCase	:							Tuple     =     scale * h, size

									__UpperCAmelCase	:							List[Any]     =     int((1_333 / 800) * size		)
									if max(lowerCamelCase__		,	lowerCamelCase__		) > max_size:
											__UpperCAmelCase	:							Union[str, Any]     =     max_size / max(lowerCamelCase__		,	lowerCamelCase__		)
											__UpperCAmelCase	:							str     =     newh * scale
											__UpperCAmelCase	:							int     =     neww * scale

									__UpperCAmelCase	:							Optional[int]     =     int(newh + 0.5		), int(neww + 0.5		)
									__UpperCAmelCase	:							Union[str, Any]     =     (
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

							else:
									__UpperCAmelCase	:							Optional[Any]     =     []
									for image in image_inputs:
											__UpperCAmelCase	:							List[Any]     =     self.get_expected_values([image]		)
											expected_values.append((expected_height, expected_width)		)
									__UpperCAmelCase	:							Dict     =     max(lowerCamelCase__		,	key=lambda UpperCAmelCase_		: item[0]		)[0]
									__UpperCAmelCase	:							Optional[int]     =     max(lowerCamelCase__		,	key=lambda UpperCAmelCase_		: item[1]		)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE__      (   __lowerCAmelCase							,unittest.TestCase		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	ViltImageProcessor if is_vision_available() else None
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     ViltImageProcessingTester(self		)
					@property
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"image_mean"		)		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"image_std"		)		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"do_normalize"		)		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"do_resize"		)		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"size"		)		)
							self.assertTrue(hasattr(lowerCamelCase__		,	"size_divisor"		)		)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.image_processing_class.from_dict(self.image_processor_dict		)
							self.assertEqual(image_processor.size		,	{"shortest_edge": 30}		)

							__UpperCAmelCase	:							List[str]     =     self.image_processing_class.from_dict(self.image_processor_dict		,	size=42		)
							self.assertEqual(image_processor.size		,	{"shortest_edge": 42}		)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							pass
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							__UpperCAmelCase	:							Optional[int]     =     prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase__		)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__		,	Image.Image		)

							# Test not batched input
							__UpperCAmelCase	:							Union[str, Any]     =     image_processing(image_inputs[0]		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							str     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

							# Test batched
							__UpperCAmelCase	:							List[Any]     =     image_processing(lowerCamelCase__		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							Dict     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		,	batched=lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,	)
					def 		lowerCamelCase_   (   self       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							__UpperCAmelCase	:							Union[str, Any]     =     prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase__		,	numpify=lowerCamelCase__		)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__		,	np.ndarray		)

							# Test not batched input
							__UpperCAmelCase	:							int     =     image_processing(image_inputs[0]		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							Tuple     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

							# Test batched
							__UpperCAmelCase	:							int     =     image_processing(lowerCamelCase__		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							Optional[int]     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		,	batched=lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,	)






					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							__UpperCAmelCase	:							int     =     prepare_image_inputs(self.image_processor_tester		,	equal_resolution=lowerCamelCase__		,	torchify=lowerCamelCase__		)
							for image in image_inputs:
									self.assertIsInstance(lowerCamelCase__		,	torch.Tensor		)

							# Test not batched input
							__UpperCAmelCase	:							Dict     =     image_processing(image_inputs[0]		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							int     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

							# Test batched
							__UpperCAmelCase	:							int     =     image_processing(lowerCamelCase__		,	return_tensors="pt"		).pixel_values

							__UpperCAmelCase	:							List[str]     =     self.image_processor_tester.get_expected_values(lowerCamelCase__		,	batched=lowerCamelCase__		)
							self.assertEqual(
							    encoded_images.shape		,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )		,	)








from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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

class    __magic_name__						(  __lowerCAmelCase):
     A:		Dict								=   ["safety_checker", "feature_extractor"]


     def __init__(   self				:  List[str]				,						lowerCamelCase__				:  AutoencoderKL				,						lowerCamelCase__				:  CLIPTextModel				,						lowerCamelCase__				:  CLIPTokenizer				,						lowerCamelCase__				:  UNetaDConditionModel				,						lowerCamelCase__				:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				,						lowerCamelCase__				:  StableDiffusionSafetyChecker				,						lowerCamelCase__				:  CLIPImageProcessor				,						lowerCamelCase__				:  bool = True				,						)	->   Optional[Any]:





      '''simple docstring'''






      super().__init__()
      if hasattr(scheduler.config				,						'''steps_offset'''      ) and scheduler.config.steps_offset != 1:
       UpperCamelCase__       :     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'''				,						lowerCamelCase__				,						standard_warn=lowerCamelCase__      )
       UpperCamelCase__       :     int 							= dict(scheduler.config      )
       UpperCamelCase__       :     List[str] 							= 1
       UpperCamelCase__       :     List[str] 							= FrozenDict(lowerCamelCase__      )

      self.register_modules(
          vae=lowerCamelCase__				,						text_encoder=lowerCamelCase__				,						tokenizer=lowerCamelCase__				,						unet=lowerCamelCase__				,						scheduler=lowerCamelCase__				,						safety_checker=lowerCamelCase__				,						feature_extractor=lowerCamelCase__				,						)
      self.register_to_config(requires_safety_checker=lowerCamelCase__      )


     @property
     def   UpperCAmelCase__						(   self				:  str      )	->   Dict[str, Any]:





      '''simple docstring'''






      return {k: getattr(self				,						lowerCamelCase__      ) for k in self.config.keys() if not k.startswith('''_'''      )}


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[Union[str, int]] = "auto"      )	->   str:





      '''simple docstring'''






      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       UpperCamelCase__       :     Dict 							= self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowerCamelCase__      )


     def   UpperCAmelCase__						(   self				:  int      )	->   Tuple:





      '''simple docstring'''






      self.enable_attention_slicing(lowerCamelCase__      )


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionInpaintPipelineLegacy(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						mask_image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  Union[torch.FloatTensor, PIL.Image.Image]				,						lowerCamelCase__				:  float = 0.8				,						lowerCamelCase__				:  Optional[int] = 50				,						lowerCamelCase__				:  Optional[float] = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  Optional[float] = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						**lowerCamelCase__				:  str				,						)	->   str:





      '''simple docstring'''






      return StableDiffusionImgaImgPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						image=lowerCamelCase__				,						strength=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


     @torch.no_grad()
     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Union[str, List[str]]				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 512				,						lowerCamelCase__				:  int = 50				,						lowerCamelCase__				:  float = 7.5				,						lowerCamelCase__				:  Optional[Union[str, List[str]]] = None				,						lowerCamelCase__				:  Optional[int] = 1				,						lowerCamelCase__				:  float = 0.0				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  Optional[torch.FloatTensor] = None				,						lowerCamelCase__				:  Optional[str] = "pil"				,						lowerCamelCase__				:  bool = True				,						lowerCamelCase__				:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,						lowerCamelCase__				:  int = 1				,						)	->   Optional[Any]:





      '''simple docstring'''






      return StableDiffusionPipeline(**self.components      )(
          prompt=lowerCamelCase__				,						height=lowerCamelCase__				,						width=lowerCamelCase__				,						num_inference_steps=lowerCamelCase__				,						guidance_scale=lowerCamelCase__				,						negative_prompt=lowerCamelCase__				,						num_images_per_prompt=lowerCamelCase__				,						eta=lowerCamelCase__				,						generator=lowerCamelCase__				,						latents=lowerCamelCase__				,						output_type=lowerCamelCase__				,						return_dict=lowerCamelCase__				,						callback=lowerCamelCase__				,						callback_steps=lowerCamelCase__				,						)


