


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,
)


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

_snake_case					:		Any      =    "\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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=8     ):
							__snake_case		:      Tuple										=       height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__snake_case		:      List[Any]										=       width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor

class     a		(__a				):




							"""simple docstring"""


							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  UNetaDConditionModel   ,     lowerCamelCase	:  DDPMScheduler   ,     lowerCamelCase	:  VQModel   ,     )     ->   str:
														super().__init__()

														self.register_modules(
														    unet=a__   ,     scheduler=a__   ,     movq=a__   ,     )
														__snake_case		:      Tuple										=       2 ** (len(self.movq.config.block_out_channels    ) - 1)

							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Tuple    )     ->   List[Any]:
														if latents is None:
																					__snake_case		:      Optional[Any]										=       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}'    )
																					__snake_case		:      str										=       latents.to(a__    )

														__snake_case		:      Union[str, Any]										=       latents * scheduler.init_noise_sigma
														return latents

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Optional[Any]=0    )     ->   List[Any]:
														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError("Please install accelerate via `pip install accelerate`"    )

														__snake_case		:      Optional[Any]										=       torch.device(F'cuda:{gpu_id}'    )

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

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  List[Any]=0    )     ->   List[str]:
														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."    )

														__snake_case		:      str										=       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)

														__snake_case		:      Union[str, Any]										=       None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					__snake_case			,      __snake_case		:      Tuple										=       cpu_offload_with_hook(a__   ,     a__   ,     prev_module_hook=a__    )

														# We'll offload the last model manually.
														__snake_case		:      Optional[int]										=       hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def    __snake_case (							self	:  Tuple    )     ->   int:
														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	:  int   ,     lowerCamelCase	:  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,     lowerCamelCase	:  Union[torch.FloatTensor, List[torch.FloatTensor]]   ,     lowerCamelCase	:  torch.FloatTensor   ,     lowerCamelCase	:  int = 512   ,     lowerCamelCase	:  int = 512   ,     lowerCamelCase	:  int = 100   ,     lowerCamelCase	:  float = 4.0   ,     lowerCamelCase	:  int = 1   ,     lowerCamelCase	:  Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,     lowerCamelCase	:  Optional[torch.FloatTensor] = None   ,     lowerCamelCase	:  Optional[str] = "pil"   ,     lowerCamelCase	:  bool = True   ,     )     ->   int:
														__snake_case		:      Any										=       self._execution_device

														__snake_case		:      Optional[int]										=       guidance_scale > 1.0

														if isinstance(a__   ,     a__    ):
																					__snake_case		:      List[Any]										=       torch.cat(a__   ,     dim=0    )
														if isinstance(a__   ,     a__    ):
																					__snake_case		:      Any										=       torch.cat(a__   ,     dim=0    )
														if isinstance(a__   ,     a__    ):
																					__snake_case		:      Union[str, Any]										=       torch.cat(a__   ,     dim=0    )

														__snake_case		:      List[str]										=       image_embeds.shape[0] * num_images_per_prompt

														if do_classifier_free_guidance:
																					__snake_case		:      str										=       image_embeds.repeat_interleave(a__   ,     dim=0    )
																					__snake_case		:      int										=       negative_image_embeds.repeat_interleave(a__   ,     dim=0    )
																					__snake_case		:      Dict										=       hint.repeat_interleave(a__   ,     dim=0    )

														__snake_case		:      Union[str, Any]										=       torch.cat([negative_image_embeds, image_embeds]   ,     dim=0    ).to(dtype=self.unet.dtype   ,     device=a__    )
														__snake_case		:      List[str]										=       torch.cat([hint, hint]   ,     dim=0    ).to(dtype=self.unet.dtype   ,     device=a__    )
														self.scheduler.set_timesteps(a__   ,     device=a__    )
														__snake_case		:      Dict										=       self.scheduler.timesteps

														__snake_case		:      Dict										=       self.movq.config.latent_channels

														__snake_case			,      __snake_case		:      Union[str, Any]										=       downscale_height_and_width(a__   ,     a__   ,     self.movq_scale_factor    )

														# create initial latent
														__snake_case		:      str										=       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
																					__snake_case		:      Any										=       torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

																					__snake_case		:      Any										=       {"image_embeds": image_embeds, "hint": hint}
																					__snake_case		:      int										=       self.unet(
																					    sample=a__   ,     timestep=a__   ,     encoder_hidden_states=a__   ,     added_cond_kwargs=a__   ,     return_dict=a__   ,     )[0]

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

																					# compute the previous noisy sample x_t -> x_t-1
																					__snake_case		:      Optional[Any]										=       self.scheduler.step(
																					    a__   ,     a__   ,     a__   ,     generator=a__   ,     )[0]
														# post-processing
														__snake_case		:      Tuple										=       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"]:
																					__snake_case		:      Union[str, Any]										=       image * 0.5 + 0.5
																					__snake_case		:      str										=       image.clamp(0   ,     1    )
																					__snake_case		:      Dict										=       image.cpu().permute(0   ,     2   ,     3   ,     1    ).float().numpy()

														if output_type == "pil":
																					__snake_case		:      int										=       self.numpy_to_pil(a__    )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=a__    )





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

