




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      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Tuple			   =   StableDiffusionDiffEditPipeline
       _UpperCAmelCase				:  Optional[int]			   =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
       _UpperCAmelCase				:  Any			   =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
       _UpperCAmelCase				:  int			   =   frozenset(
           [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       _UpperCAmelCase				:  Optional[int]			   =   frozenset([] )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          torch.manual_seed(0      )
          lowerCamelCase__       :						Any        =							UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)		, layers_per_block=2		, sample_size=3_2		, in_channels=4		, out_channels=4		, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		, cross_attention_dim=3_2		, attention_head_dim=(2, 4)		, use_linear_projection=A		, )
          lowerCamelCase__       :						Dict        =							DDIMScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=A		, set_alpha_to_one=A		, )
          lowerCamelCase__       :						Optional[int]        =							DDIMInverseScheduler(
              beta_start=0.0_00_85		, beta_end=0.0_12		, beta_schedule='''scaled_linear'''		, clip_sample=A		, set_alpha_to_zero=A		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Union[str, Any]        =							AutoencoderKL(
              block_out_channels=[3_2, 6_4]		, in_channels=3		, out_channels=3		, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		, latent_channels=4		, sample_size=1_2_8		, )
          torch.manual_seed(0      )
          lowerCamelCase__       :						Optional[Any]        =							CLIPTextConfig(
              bos_token_id=0		, eos_token_id=2		, hidden_size=3_2		, intermediate_size=3_7		, layer_norm_eps=1e-05		, num_attention_heads=4		, num_hidden_layers=5		, pad_token_id=1		, vocab_size=1_0_0_0		, hidden_act='''gelu'''		, projection_dim=5_1_2		, )
          lowerCamelCase__       :						List[str]        =							CLIPTextModel(A      )
          lowerCamelCase__       :						Optional[Any]        =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

          lowerCamelCase__       :						Optional[int]        =							{
              '''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 						__lowerCamelCase   (				self					:     str		, A					:     Any		, A					:     Union[str, Any]=0      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							floats_tensor((1, 1_6, 1_6)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						List[str]        =							floats_tensor((1, 2, 4, 1_6, 1_6)		, rng=random.Random(A      )      ).to(A      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						Dict        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						Optional[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 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     Union[str, Any]		, A					:     Optional[int]=0      )       ->List[Any]:
          lowerCamelCase__       :						List[str]        =							floats_tensor((1, 3, 3_2, 3_2)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						List[Any]        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						str        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						List[Any]        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						str        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						Union[str, Any]        =							{
              '''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 						__lowerCamelCase   (				self					:     int		, A					:     Tuple		, A					:     Optional[int]=0      )       ->Tuple:
          lowerCamelCase__       :						Optional[int]        =							floats_tensor((1, 3, 3_2, 3_2)		, rng=random.Random(A      )      ).to(A      )
          lowerCamelCase__       :						Tuple        =							image.cpu().permute(0		, 2		, 3		, 1      )[0]
          lowerCamelCase__       :						Union[str, Any]        =							Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      )
          if str(A      ).startswith('''mps'''      ):
             lowerCamelCase__       :						Any        =							torch.manual_seed(A      )
          else:
             lowerCamelCase__       :						Tuple        =							torch.Generator(device=A      ).manual_seed(A      )
          lowerCamelCase__       :						Union[str, Any]        =							{
              '''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 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          if not hasattr(self.pipeline_class		, '''_optional_components'''      ):
             return

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

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

          lowerCamelCase__       :						Dict        =							self.get_dummy_inputs(A      )
          lowerCamelCase__       :						int        =							pipe(**A      )[0]

          with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(A      )
             lowerCamelCase__       :						Optional[int]        =							self.pipeline_class.from_pretrained(A      )
             pipe_loaded.to(A      )
             pipe_loaded.set_progress_bar_config(disable=A      )

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

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_inputs(A      )
          lowerCamelCase__       :						List[str]        =							pipe_loaded(**A      )[0]

          lowerCamelCase__       :						int        =							np.abs(output - output_loaded      ).max()
          self.assertLess(A		, 1e-4      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							'''cpu'''

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_components()
          lowerCamelCase__       :						Tuple        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[Any]        =							self.get_dummy_mask_inputs(A      )
          lowerCamelCase__       :						Optional[Any]        =							pipe.generate_mask(**A      )
          lowerCamelCase__       :						Optional[int]        =							mask[0, -3:, -3:]

          self.assertEqual(mask.shape		, (1, 1_6, 1_6)      )
          lowerCamelCase__       :						Any        =							np.array([0] * 9      )
          lowerCamelCase__       :						Optional[int]        =							np.abs(mask_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )
          self.assertEqual(mask[0, -3, -4]		, 0      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						List[Any]        =							'''cpu'''

          lowerCamelCase__       :						Union[str, Any]        =							self.get_dummy_components()
          lowerCamelCase__       :						Optional[int]        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Dict        =							self.get_dummy_inversion_inputs(A      )
          lowerCamelCase__       :						Dict        =							pipe.invert(**A      ).images
          lowerCamelCase__       :						Optional[Any]        =							image[0, -1, -3:, -3:]

          self.assertEqual(image.shape		, (2, 3_2, 3_2, 3)      )
          lowerCamelCase__       :						Optional[int]        =							np.array(
              [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]		, )
          lowerCamelCase__       :						Optional[Any]        =							np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->int:
          super().test_inference_batch_single_identical(expected_max_diff=5e-3      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          lowerCamelCase__       :						List[str]        =							'''cpu'''

          lowerCamelCase__       :						Optional[int]        =							self.get_dummy_components()

          lowerCamelCase__       :						Dict        =							{'''beta_start''': 0.0_00_85, '''beta_end''': 0.0_12, '''beta_schedule''': '''scaled_linear'''}
          lowerCamelCase__       :						int        =							DPMSolverMultistepScheduler(**A      )
          lowerCamelCase__       :						Dict        =							DPMSolverMultistepInverseScheduler(**A      )

          lowerCamelCase__       :						Optional[Any]        =							self.pipeline_class(**A      )
          pipe.to(A      )
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[int]        =							self.get_dummy_inversion_inputs(A      )
          lowerCamelCase__       :						List[str]        =							pipe.invert(**A      ).images
          lowerCamelCase__       :						Tuple        =							image[0, -1, -3:, -3:]

          self.assertEqual(image.shape		, (2, 3_2, 3_2, 3)      )
          lowerCamelCase__       :						Dict        =							np.array(
              [0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99]		, )
          lowerCamelCase__       :						Any        =							np.abs(image_slice.flatten() - expected_slice      ).max()
          self.assertLessEqual(A		, 1e-3      )


@require_torch_gpu
@slow
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       @classmethod
       def 						__lowerCamelCase   (				cls					:     str      )       ->List[Any]:
          lowerCamelCase__       :						Dict        =							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''      )

          lowerCamelCase__       :						Dict        =							raw_image.convert('''RGB'''      ).resize((7_6_8, 7_6_8)      )

          lowerCamelCase__       :						List[Any]        =							raw_image







       def 						__lowerCamelCase   (				self					:     Any      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[Any]        =							torch.manual_seed(0      )

          lowerCamelCase__       :						int        =							StableDiffusionDiffEditPipeline.from_pretrained(
              '''stabilityai/stable-diffusion-2-1'''		, safety_checker=A		, torch_dtype=torch.floataa      )
          lowerCamelCase__       :						List[Any]        =							DDIMScheduler.from_config(pipe.scheduler.config      )
          lowerCamelCase__       :						Dict        =							DDIMInverseScheduler.from_config(pipe.scheduler.config      )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Optional[int]        =							'''a bowl of fruit'''
          lowerCamelCase__       :						Any        =							'''a bowl of pears'''

          lowerCamelCase__       :						str        =							pipe.generate_mask(
              image=self.raw_image		, source_prompt=A		, target_prompt=A		, generator=A		, )

          lowerCamelCase__       :						List[str]        =							pipe.invert(
              prompt=A		, image=self.raw_image		, inpaint_strength=0.7		, generator=A      ).latents

          lowerCamelCase__       :						Union[str, Any]        =							pipe(
              prompt=A		, mask_image=A		, image_latents=A		, generator=A		, negative_prompt=A		, inpaint_strength=0.7		, output_type='''numpy'''		, ).images[0]

          lowerCamelCase__       :						List[Any]        =							(
              np.array(
                  load_image(
                      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                      '''/diffedit/pears.png'''      ).resize((7_6_8, 7_6_8)      )      )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()      ) < 5e-1







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							torch.manual_seed(0      )

          lowerCamelCase__       :						Optional[Any]        =							StableDiffusionDiffEditPipeline.from_pretrained(
              '''stabilityai/stable-diffusion-2-1'''		, safety_checker=A		, torch_dtype=torch.floataa      )
          lowerCamelCase__       :						int        =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
          lowerCamelCase__       :						Optional[Any]        =							DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config      )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A      )

          lowerCamelCase__       :						Any        =							'''a bowl of fruit'''
          lowerCamelCase__       :						Union[str, Any]        =							'''a bowl of pears'''

          lowerCamelCase__       :						Optional[Any]        =							pipe.generate_mask(
              image=self.raw_image		, source_prompt=A		, target_prompt=A		, generator=A		, )

          lowerCamelCase__       :						Any        =							pipe.invert(
              prompt=A		, image=self.raw_image		, inpaint_strength=0.7		, generator=A		, num_inference_steps=2_5		, ).latents

          lowerCamelCase__       :						Any        =							pipe(
              prompt=A		, mask_image=A		, image_latents=A		, generator=A		, negative_prompt=A		, inpaint_strength=0.7		, num_inference_steps=2_5		, output_type='''numpy'''		, ).images[0]

          lowerCamelCase__       :						List[Any]        =							(
              np.array(
                  load_image(
                      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                      '''/diffedit/pears.png'''      ).resize((7_6_8, 7_6_8)      )      )
              / 2_5_5
          )
          assert np.abs((expected_image - image).max()      ) < 5e-1










import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_A :   Optional[Any]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







       def __init__(				self					:     Optional[Any]		, A					:     AutoencoderKL		, A					:     CLIPTextModel		, A					:     CLIPTokenizer		, A					:     UNetaDConditionModel		, A					:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		, A					:     StableDiffusionSafetyChecker		, A					:     CLIPImageProcessor		, )       ->List[Any]:
          super().__init__()
          self.register_modules(
              vae=A		, text_encoder=A		, tokenizer=A		, unet=A		, scheduler=A		, safety_checker=A		, feature_extractor=A		, )







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







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          self.enable_attention_slicing(A      )







       @torch.no_grad()
       def __call__(				self					:     Union[str, Any]		, A					:     Union[str, List[str]]		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 5_0		, A					:     float = 7.5		, A					:     Optional[Union[str, List[str]]] = None		, A					:     Optional[int] = 1		, A					:     float = 0.0		, A					:     Optional[torch.Generator] = None		, A					:     Optional[torch.FloatTensor] = None		, A					:     Optional[str] = "pil"		, A					:     bool = True		, A					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None		, A					:     int = 1		, A					:     Optional[torch.FloatTensor] = None		, **A					:     int		, )       ->Tuple:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						str        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							len(A      )
          else:
             raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(A      )}"      )

          if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."      )

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(A		, A      ) or callback_steps <= 0)
          ):
             raise ValueError(
                 F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 F" {type(A      )}."      )

          # get prompt text embeddings
          lowerCamelCase__       :						Optional[Any]        =							self.tokenizer(
              A		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, return_tensors='''pt'''		, )
          lowerCamelCase__       :						Dict        =							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase__       :						Optional[Any]        =							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 F" {self.tokenizer.model_max_length} tokens: {removed_text}"      )
             lowerCamelCase__       :						Any        =							text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             lowerCamelCase__       :						str        =							self.text_encoder(text_input_ids.to(self.device      )      )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						int        =							text_embeddings.shape
          lowerCamelCase__       :						int        =							text_embeddings.repeat(1		, A		, 1      )
          lowerCamelCase__       :						str        =							text_embeddings.view(bs_embed * num_images_per_prompt		, A		, -1      )

          # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
          # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
          # corresponds to doing no classifier free guidance.
          lowerCamelCase__       :						Optional[Any]        =							guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]
             if negative_prompt is None:
                lowerCamelCase__       :						Optional[int]        =							['''''']
             elif type(A      ) is not type(A      ):
                raise TypeError(
                    F"`negative_prompt` should be the same type to `prompt`, but got {type(A      )} !="
                    F" {type(A      )}."      )
             elif isinstance(A		, A      ):
                lowerCamelCase__       :						Optional[int]        =							[negative_prompt]
             elif batch_size != len(A      ):
                raise ValueError(
                    F"`negative_prompt`: {negative_prompt} has batch size {len(A      )}, but `prompt`:"
                    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    ''' the batch size of `prompt`.'''      )
             else:
                lowerCamelCase__       :						List[Any]        =							negative_prompt

             lowerCamelCase__       :						int        =							text_input_ids.shape[-1]
             lowerCamelCase__       :						Optional[int]        =							self.tokenizer(
                 A		, padding='''max_length'''		, max_length=A		, truncation=A		, return_tensors='''pt'''		, )
             lowerCamelCase__       :						Optional[Any]        =							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase__       :						Any        =							uncond_embeddings.shape[1]
             lowerCamelCase__       :						Union[str, Any]        =							uncond_embeddings.repeat(A		, A		, 1      )
             lowerCamelCase__       :						str        =							uncond_embeddings.view(batch_size * num_images_per_prompt		, A		, -1      )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             lowerCamelCase__       :						str        =							torch.cat([uncond_embeddings, text_embeddings]      )

          # get the initial random noise unless the user supplied it

          # Unlike in other pipelines, latents need to be generated in the target device
          # for 1-to-1 results reproducibility with the CompVis implementation.
          # However this currently doesn't work in `mps`.
          lowerCamelCase__       :						Dict        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          lowerCamelCase__       :						List[Any]        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          lowerCamelCase__       :						List[Any]        =							text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not exist on mps
                lowerCamelCase__       :						Dict        =							torch.randn(
                    A		, generator=A		, device='''cpu'''		, dtype=A      ).to(self.device      )
                lowerCamelCase__       :						Dict        =							torch.randn(A		, generator=A		, device='''cpu'''		, dtype=A      ).to(
                    self.device      )
             else:
                lowerCamelCase__       :						Optional[Any]        =							torch.randn(
                    A		, generator=A		, device=self.device		, dtype=A      )
                lowerCamelCase__       :						Union[str, Any]        =							torch.randn(A		, generator=A		, device=self.device		, dtype=A      )
          else:
             if latents_reference.shape != latents_shape:
                raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"      )
             lowerCamelCase__       :						Optional[Any]        =							latents_reference.to(self.device      )
             lowerCamelCase__       :						Optional[int]        =							latents.to(self.device      )

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          lowerCamelCase__       :						List[str]        =							(latents_shape[3] - latents_shape_reference[3]) // 2
          lowerCamelCase__       :						List[Any]        =							(latents_shape[2] - latents_shape_reference[2]) // 2
          lowerCamelCase__       :						Dict        =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          lowerCamelCase__       :						str        =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          lowerCamelCase__       :						int        =							0 if dx < 0 else dx
          lowerCamelCase__       :						Optional[int]        =							0 if dy < 0 else dy
          lowerCamelCase__       :						Dict        =							max(-dx		, 0      )
          lowerCamelCase__       :						int        =							max(-dy		, 0      )
          # import pdb
          # pdb.set_trace()
          lowerCamelCase__       :						str        =							latents_reference[:, :, dy : dy + h, dx : dx + w]

          # set timesteps
          self.scheduler.set_timesteps(A      )

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps.to(self.device      )

          # scale the initial noise by the standard deviation required by the scheduler
          lowerCamelCase__       :						List[Any]        =							latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          lowerCamelCase__       :						Union[str, Any]        =							'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
          lowerCamelCase__       :						List[Any]        =							{}
          if accepts_eta:
             lowerCamelCase__       :						Any        =							eta

          for i, t in enumerate(self.progress_bar(A      )      ):
             # expand the latents if we are doing classifier free guidance
             lowerCamelCase__       :						Optional[int]        =							torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
             lowerCamelCase__       :						Any        =							self.scheduler.scale_model_input(A		, A      )

             # predict the noise residual
             lowerCamelCase__       :						Union[str, Any]        =							self.unet(A		, A		, encoder_hidden_states=A      ).sample

             # perform guidance
             if do_classifier_free_guidance:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							noise_pred.chunk(2      )
                lowerCamelCase__       :						int        =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__       :						Dict        =							self.scheduler.step(A		, A		, A		, **A      ).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                callback(A		, A		, A      )

          lowerCamelCase__       :						Optional[Any]        =							1 / 0.1_82_15 * latents
          lowerCamelCase__       :						int        =							self.vae.decode(A      ).sample

          lowerCamelCase__       :						Dict        =							(image / 2 + 0.5).clamp(0		, 1      )

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          lowerCamelCase__       :						List[str]        =							image.cpu().permute(0		, 2		, 3		, 1      ).float().numpy()

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							self.feature_extractor(self.numpy_to_pil(A      )		, return_tensors='''pt'''      ).to(
                 self.device      )
             lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.safety_checker(
                 images=A		, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
          else:
             lowerCamelCase__       :						List[Any]        =							None

          if output_type == "pil":
             lowerCamelCase__       :						Optional[Any]        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A		, nsfw_content_detected=A      )



