





'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

snake_case_       :    Optional[Any]          =					"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Tuple,     SCREAMING_SNAKE_CASE__ :     List[str],     SCREAMING_SNAKE_CASE__ :     Union[str, Any]=8				)	->		Optional[int]:
						UpperCAmelCase_     : Dict         =					height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCAmelCase_     : str         =					width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     List[Any]=512,     SCREAMING_SNAKE_CASE__ :     Optional[int]=512				)	->		Tuple:
						UpperCAmelCase_     : str         =					pil_image.resize((w, h),     resample=Image.BICUBIC,     reducing_gap=1				)
						UpperCAmelCase_     : Optional[int]         =					np.array(pil_image.convert('''RGB'''				)				)
						UpperCAmelCase_     : Union[str, Any]         =					arr.astype(np.floataa				) / 1_27.5 - 1
						UpperCAmelCase_     : Union[str, Any]         =					np.transpose(SCREAMING_SNAKE_CASE__,     [2, 0, 1]				)
						UpperCAmelCase_     : List[str]         =					torch.from_numpy(SCREAMING_SNAKE_CASE__				).unsqueeze(0				)
						return image


class    __a (lowerCamelCase       ):

							def __init__(							self							:       Tuple    ,       __magic_name__							:       UNetaDConditionModel    ,       __magic_name__							:       DDPMScheduler    ,       __magic_name__							:       VQModel    ,       )   ->    Union[str, Any]:







													"""simple docstring"""
													super().__init__()

													self.register_modules(
													    unet=__magic_name__    ,       scheduler=__magic_name__    ,       movq=__magic_name__    ,       )
													UpperCAmelCase_     : Union[str, Any]         =					2 ** (len(self.movq.config.block_out_channels				) - 1)

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       int    ,       __magic_name__							:       Tuple				)   ->    Dict:







													"""simple docstring"""
													# get the original timestep using init_timestep
													UpperCAmelCase_     : List[Any]         =					min(int(num_inference_steps * strength				)    ,       __magic_name__				)

													UpperCAmelCase_     : Tuple         =					max(num_inference_steps - init_timestep    ,       0				)
													UpperCAmelCase_     : Union[str, Any]         =					self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       Optional[int]    ,       __magic_name__							:       List[Any]    ,       __magic_name__							:       Tuple    ,       __magic_name__							:       str    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       Optional[Any]    ,       __magic_name__							:       int=None				)   ->    Any:







													"""simple docstring"""
													if not isinstance(__magic_name__    ,       (torch.Tensor, PIL.Image.Image, list)				):
																			raise ValueError(
																			    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__magic_name__				)}"""				)

													UpperCAmelCase_     : Union[str, Any]         =					image.to(device=__magic_name__    ,       dtype=__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																			UpperCAmelCase_     : str         =					image

													else:
																			if isinstance(__magic_name__    ,       __magic_name__				) and len(__magic_name__				) != batch_size:
																									raise ValueError(
																									    F"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
																									    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

																			elif isinstance(__magic_name__    ,       __magic_name__				):
																									UpperCAmelCase_     : int         =					[
																									    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(__magic_name__				)
																									]
																									UpperCAmelCase_     : Union[str, Any]         =					torch.cat(__magic_name__    ,       dim=0				)
																			else:
																									UpperCAmelCase_     : Dict         =					self.movq.encode(__magic_name__				).latent_dist.sample(__magic_name__				)

																			UpperCAmelCase_     : Union[str, Any]         =					self.movq.config.scaling_factor * init_latents

													UpperCAmelCase_     : Union[str, Any]         =					torch.cat([init_latents]    ,       dim=0				)

													UpperCAmelCase_     : str         =					init_latents.shape
													UpperCAmelCase_     : Dict         =					randn_tensor(__magic_name__    ,       generator=__magic_name__    ,       device=__magic_name__    ,       dtype=__magic_name__				)

													# get latents
													UpperCAmelCase_     : Any         =					self.scheduler.add_noise(__magic_name__    ,       __magic_name__    ,       __magic_name__				)

													UpperCAmelCase_     : List[Any]         =					init_latents

													return latents

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       List[Any]=0				)   ->    str:







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

													UpperCAmelCase_     : str         =					torch.device(F"""cuda:{gpu_id}"""				)

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

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       Optional[int]=0				)   ->    str:







													"""simple docstring"""
													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.'''				)

													UpperCAmelCase_     : Union[str, Any]         =					torch.device(F"""cuda:{gpu_id}"""				)

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

													UpperCAmelCase_     : Dict         =					None
													for cpu_offloaded_model in [self.unet, self.movq]:
																			UpperCAmelCase_  ,    UpperCAmelCase_     : Tuple         =					cpu_offload_with_hook(__magic_name__    ,       __magic_name__    ,       prev_module_hook=__magic_name__				)

													# We'll offload the last model manually.
													UpperCAmelCase_     : Any         =					hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    Any:







													"""simple docstring"""
													if not hasattr(self.unet    ,       '''_hf_hook'''				):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(__magic_name__    ,       '''_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(__magic_name__				)
							def __call__(							self							:       Optional[Any]    ,       __magic_name__							:       Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       __magic_name__							:       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,       __magic_name__							:       Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       __magic_name__							:       torch.FloatTensor    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       int = 5_12    ,       __magic_name__							:       int = 1_00    ,       __magic_name__							:       float = 4.0    ,       __magic_name__							:       float = 0.3    ,       __magic_name__							:       int = 1    ,       __magic_name__							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       __magic_name__							:       Optional[str] = "pil"    ,       __magic_name__							:       bool = True    ,       )   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					self._execution_device

													UpperCAmelCase_     : Any         =					guidance_scale > 1.0

													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : List[Any]         =					torch.cat(__magic_name__    ,       dim=0				)
													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : Tuple         =					torch.cat(__magic_name__    ,       dim=0				)
													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : List[str]         =					torch.cat(__magic_name__    ,       dim=0				)

													UpperCAmelCase_     : str         =					image_embeds.shape[0]

													if do_classifier_free_guidance:
																			UpperCAmelCase_     : Union[str, Any]         =					image_embeds.repeat_interleave(__magic_name__    ,       dim=0				)
																			UpperCAmelCase_     : Union[str, Any]         =					negative_image_embeds.repeat_interleave(__magic_name__    ,       dim=0				)
																			UpperCAmelCase_     : Any         =					hint.repeat_interleave(__magic_name__    ,       dim=0				)

													UpperCAmelCase_     : str         =					torch.cat([negative_image_embeds, image_embeds]    ,       dim=0				).to(dtype=self.unet.dtype    ,       device=__magic_name__				)
													UpperCAmelCase_     : int         =					torch.cat([hint, hint]    ,       dim=0				).to(dtype=self.unet.dtype    ,       device=__magic_name__				)

													if not isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : Optional[Any]         =					[image]
													if not all(isinstance(__magic_name__    ,       (PIL.Image.Image, torch.Tensor)				) for i in image				):
																			raise ValueError(
																			    F"""Input is in incorrect format: {[type(__magic_name__				) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""				)

													UpperCAmelCase_     : Union[str, Any]         =					torch.cat([prepare_image(__magic_name__    ,       __magic_name__    ,       __magic_name__				) for i in image]    ,       dim=0				)
													UpperCAmelCase_     : str         =					image.to(dtype=image_embeds.dtype    ,       device=__magic_name__				)

													UpperCAmelCase_     : Union[str, Any]         =					self.movq.encode(__magic_name__				)['''latents''']
													UpperCAmelCase_     : List[str]         =					latents.repeat_interleave(__magic_name__    ,       dim=0				)
													self.scheduler.set_timesteps(__magic_name__    ,       device=__magic_name__				)
													UpperCAmelCase_  ,    UpperCAmelCase_     : Dict         =					self.get_timesteps(__magic_name__    ,       __magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Union[str, Any]         =					timesteps[:1].repeat(batch_size * num_images_per_prompt				)
													UpperCAmelCase_  ,    UpperCAmelCase_     : str         =					downscale_height_and_width(__magic_name__    ,       __magic_name__    ,       self.movq_scale_factor				)
													UpperCAmelCase_     : List[Any]         =					self.prepare_latents(
													    __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       image_embeds.dtype    ,       __magic_name__    ,       __magic_name__				)
													for i, t in enumerate(self.progress_bar(__magic_name__				)				):
																			# expand the latents if we are doing classifier free guidance
																			UpperCAmelCase_     : List[str]         =					torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

																			UpperCAmelCase_     : Tuple         =					{'''image_embeds''': image_embeds, '''hint''': hint}
																			UpperCAmelCase_     : Optional[Any]         =					self.unet(
																			    sample=__magic_name__    ,       timestep=__magic_name__    ,       encoder_hidden_states=__magic_name__    ,       added_cond_kwargs=__magic_name__    ,       return_dict=__magic_name__    ,       )[0]

																			if do_classifier_free_guidance:
																									UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[Any]         =					noise_pred.split(latents.shape[1]    ,       dim=1				)
																									UpperCAmelCase_  ,    UpperCAmelCase_     : Optional[int]         =					noise_pred.chunk(2				)
																									UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					variance_pred.chunk(2				)
																									UpperCAmelCase_     : Optional[int]         =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									UpperCAmelCase_     : List[Any]         =					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"]
																			):
																									UpperCAmelCase_  ,    UpperCAmelCase_     : Tuple         =					noise_pred.split(latents.shape[1]    ,       dim=1				)

																			# compute the previous noisy sample x_t -> x_t-1

																			UpperCAmelCase_     : Dict         =					self.scheduler.step(
																			    __magic_name__    ,       __magic_name__    ,       __magic_name__    ,       generator=__magic_name__    ,       )[0]

													# post-processing
													UpperCAmelCase_     : Any         =					self.movq.decode(__magic_name__    ,       force_not_quantize=__magic_name__				)['''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"]:
																			UpperCAmelCase_     : Dict         =					image * 0.5 + 0.5
																			UpperCAmelCase_     : Union[str, Any]         =					image.clamp(0    ,       1				)
																			UpperCAmelCase_     : Tuple         =					image.cpu().permute(0    ,       2    ,       3    ,       1				).float().numpy()

													if output_type == "pil":
																			UpperCAmelCase_     : str         =					self.numpy_to_pil(__magic_name__				)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__magic_name__				)












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





