


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    __magic_name__						(  unittest.TestCase):


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





      '''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   UpperCAmelCase__						(   self				:  Union[str, Any]      )	->   List[str]:





      '''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   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Union[str, Any]				,						lowerCamelCase__				:  List[Any]=False      )	->   List[Any]:





      '''simple docstring'''






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

       UpperCamelCase__       :     List[Any] 							= int((1333 / 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__							,					UpperCamelCase__       :     Optional[int] 							= int(newh + 0.5      ), int(neww + 0.5      )
       UpperCamelCase__							,					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__							,					UpperCamelCase__       :     List[Any] 							= self.get_expected_values([image]      )
        expected_values.append((expected_height, expected_width)      )
       UpperCamelCase__       :     Dict 							= max(lowerCamelCase__				,						key=lambda lowerCamelCase__      : item[0]      )[0]
       UpperCamelCase__       :     Optional[int] 							= max(lowerCamelCase__				,						key=lambda lowerCamelCase__      : item[1]      )[1]

      return expected_height, expected_width



@require_torch
@require_vision
class    __magic_name__						(  __lowerCAmelCase       ,					unittest.TestCase):
     A:		Tuple								=   ViltImageProcessor if is_vision_available() else None


     def   UpperCAmelCase__						(   self				:  Any      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Dict 							= ViltImageProcessingTester(self      )


     @property
     def   UpperCAmelCase__						(   self				:  int      )	->   List[Any]:





      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()


     def   UpperCAmelCase__						(   self				:  Dict      )	->   List[Any]:





      '''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   UpperCAmelCase__						(   self				:  Dict      )	->   Union[str, Any]:





      '''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   UpperCAmelCase__						(   self				:  Dict      )	->   Any:





      '''simple docstring'''






      pass


     def   UpperCAmelCase__						(   self				:  Dict      )	->   Optional[int]:





      '''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__							,					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__							,					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   UpperCAmelCase__						(   self				:  Dict      )	->   List[str]:





      '''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__							,					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__							,					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   UpperCAmelCase__						(   self				:  Any      )	->   Tuple:





      '''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__							,					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__							,					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__				,						)


