
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowercase__		:Tuple   =    "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=8		):
					'''simple docstring'''
					lowercase							=						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					lowercase							=						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,):
												super().__init__()

												self.register_modules(
												    unet=A__   ,scheduler=A__   ,movq=A__   ,)
												lowercase							=						2 ** (len(self.movq.config.block_out_channels) - 1)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												if latents is None:
																	lowercase							=						randn_tensor(A__   ,generator=A__   ,device=A__   ,dtype=A__)
												else:
																	if latents.shape != shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}')
																	lowercase							=						latents.to(A__)

												lowercase							=						latents * scheduler.init_noise_sigma
												return latents




							def  A__      (				self   ,A__=0):
												if is_accelerate_available():
																	from accelerate import cpu_offload
												else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`''')

												lowercase							=						torch.device(f'cuda:{gpu_id}')

												lowercase							=						[
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																						cpu_offload(A__   ,A__)




							def  A__      (				self   ,A__=0):
												if is_accelerate_available() and is_accelerate_version('''>='''   ,'''0.17.0.dev0'''):
																	from accelerate import cpu_offload_with_hook
												else:
																	raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''')

												lowercase							=						torch.device(f'cuda:{gpu_id}')

												if self.device.type != "cpu":
																	self.to('''cpu'''   ,silence_dtype_warnings=A__)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

												lowercase							=						None
												for cpu_offloaded_model in [self.unet, self.movq]:
																	lowercase	,						lowercase							=						cpu_offload_with_hook(A__   ,A__   ,prev_module_hook=A__)

												# We'll offload the last model manually.
												lowercase							=						hook




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def  A__      (				self):
												if not hasattr(self.unet   ,'''_hf_hook'''):
																	return self.device
												for module in self.unet.modules():
																	if (
																	    hasattr(A__   ,'''_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()
							@replace_example_docstring(A__)
							def __call__(				self   ,A__   ,A__   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 1_0_0   ,A__ = 4.0   ,A__ = 1   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,):
												lowercase							=						self._execution_device

												lowercase							=						guidance_scale > 1.0

												if isinstance(A__   ,A__):
																	lowercase							=						torch.cat(A__   ,dim=0)
												if isinstance(A__   ,A__):
																	lowercase							=						torch.cat(A__   ,dim=0)
												if isinstance(A__   ,A__):
																	lowercase							=						torch.cat(A__   ,dim=0)

												lowercase							=						image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																	lowercase							=						image_embeds.repeat_interleave(A__   ,dim=0)
																	lowercase							=						negative_image_embeds.repeat_interleave(A__   ,dim=0)
																	lowercase							=						hint.repeat_interleave(A__   ,dim=0)

												lowercase							=						torch.cat([negative_image_embeds, image_embeds]   ,dim=0).to(dtype=self.unet.dtype   ,device=A__)
												lowercase							=						torch.cat([hint, hint]   ,dim=0).to(dtype=self.unet.dtype   ,device=A__)
												self.scheduler.set_timesteps(A__   ,device=A__)
												lowercase							=						self.scheduler.timesteps

												lowercase							=						self.movq.config.latent_channels

												lowercase	,						lowercase							=						downscale_height_and_width(A__   ,A__   ,self.movq_scale_factor)

												# create initial latent
												lowercase							=						self.prepare_latents(
												    (batch_size, num_channels_latents, height, width)   ,image_embeds.dtype   ,A__   ,A__   ,A__   ,self.scheduler   ,)

												for i, t in enumerate(self.progress_bar(A__)):
																	# expand the latents if we are doing classifier free guidance
																	lowercase							=						torch.cat([latents] * 2) if do_classifier_free_guidance else latents

																	lowercase							=						{'''image_embeds''': image_embeds, '''hint''': hint}
																	lowercase							=						self.unet(
																	    sample=A__   ,timestep=A__   ,encoder_hidden_states=A__   ,added_cond_kwargs=A__   ,return_dict=A__   ,)[0]

																	if do_classifier_free_guidance:
																						lowercase	,						lowercase							=						noise_pred.split(latents.shape[1]   ,dim=1)
																						lowercase	,						lowercase							=						noise_pred.chunk(2)
																						lowercase	,						lowercase							=						variance_pred.chunk(2)
																						lowercase							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						lowercase							=						torch.cat([noise_pred, variance_pred_text]   ,dim=1)

																	if not (
																	    hasattr(self.scheduler.config   ,'''variance_type''')
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																						lowercase	,						lowercase							=						noise_pred.split(latents.shape[1]   ,dim=1)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(
																	    A__   ,A__   ,A__   ,generator=A__   ,)[0]
												# post-processing
												lowercase							=						self.movq.decode(A__   ,force_not_quantize=A__)['''sample''']

												if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}')

												if output_type in ["np", "pil"]:
																	lowercase							=						image * 0.5 + 0.5
																	lowercase							=						image.clamp(0   ,1)
																	lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




