

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,
)


lowerCAmelCase							:  Union[str, Any]            =						logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase							:  Any            =						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=8   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						SCREAMING_SNAKE_CASE_:			Tuple  		=	width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					List[str]       ,						lowerCAmelCase__  :					UNetaDConditionModel       ,						lowerCAmelCase__  :					DDPMScheduler       ,						lowerCAmelCase__  :					VQModel       ,						):
									super().__init__()

									self.register_modules(
									    unet=lowerCAmelCase__       ,						scheduler=lowerCAmelCase__       ,						movq=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	2 ** (len(self.movq.config.block_out_channels) - 1)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple):
									if latents is None:
															SCREAMING_SNAKE_CASE_:			Dict  		=	randn_tensor(lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						device=lowerCAmelCase__       ,						dtype=lowerCAmelCase__)
									else:
															if latents.shape != shape:
																					raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
															SCREAMING_SNAKE_CASE_:			Tuple  		=	latents.to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	latents * scheduler.init_noise_sigma
									return latents



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=0):
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("Please install accelerate via `pip install accelerate`")

									SCREAMING_SNAKE_CASE_:			int  		=	torch.device(F"cuda:{gpu_id}")

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[Any]=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.")

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

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

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

									# We'll offload the last model manually.
									SCREAMING_SNAKE_CASE_:			List[str]  		=	hook



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									if not hasattr(self.unet       ,						"_hf_hook"):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(lowerCAmelCase__       ,						"_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(lowerCAmelCase__)
			def __call__(			self  :					Dict       ,						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       ,						):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._execution_device

									SCREAMING_SNAKE_CASE_:			Dict  		=	guidance_scale > 1.0

									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.cat(lowerCAmelCase__       ,						dim=0)
									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cat(lowerCAmelCase__       ,						dim=0)
									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Any  		=	torch.cat(lowerCAmelCase__       ,						dim=0)

									SCREAMING_SNAKE_CASE_:			Dict  		=	image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_embeds.repeat_interleave(lowerCAmelCase__       ,						dim=0)
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	negative_image_embeds.repeat_interleave(lowerCAmelCase__       ,						dim=0)
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hint.repeat_interleave(lowerCAmelCase__       ,						dim=0)

									SCREAMING_SNAKE_CASE_:			str  		=	torch.cat([negative_image_embeds, image_embeds]       ,						dim=0).to(dtype=self.unet.dtype       ,						device=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.cat([hint, hint]       ,						dim=0).to(dtype=self.unet.dtype       ,						device=lowerCAmelCase__)
									self.scheduler.set_timesteps(lowerCAmelCase__       ,						device=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.scheduler.timesteps

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.movq.config.latent_channels

									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Tuple  		=	downscale_height_and_width(lowerCAmelCase__       ,						lowerCAmelCase__       ,						self.movq_scale_factor)

									# create initial latent
									SCREAMING_SNAKE_CASE_:			int  		=	self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)       ,						image_embeds.dtype       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						self.scheduler       ,						)

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

															SCREAMING_SNAKE_CASE_:			Tuple  		=	{"image_embeds": image_embeds, "hint": hint}
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.unet(
															    sample=lowerCAmelCase__       ,						timestep=lowerCAmelCase__       ,						encoder_hidden_states=lowerCAmelCase__       ,						added_cond_kwargs=lowerCAmelCase__       ,						return_dict=lowerCAmelCase__       ,						)[0]

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

															# compute the previous noisy sample x_t -> x_t-1
															SCREAMING_SNAKE_CASE_:			Any  		=	self.scheduler.step(
															    lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						generator=lowerCAmelCase__       ,						)[0]
									# post-processing
									SCREAMING_SNAKE_CASE_:			Any  		=	self.movq.decode(lowerCAmelCase__       ,						force_not_quantize=lowerCAmelCase__)["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"]:
															SCREAMING_SNAKE_CASE_:			str  		=	image * 0.5 + 0.5
															SCREAMING_SNAKE_CASE_:			int  		=	image.clamp(0       ,						1)
															SCREAMING_SNAKE_CASE_:			Any  		=	image.cpu().permute(0       ,						2       ,						3       ,						1).float().numpy()

									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple            =						{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase							:  Any            =						1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase							:  Dict            =						0

lowerCAmelCase							:  Dict            =						0xe000
lowerCAmelCase							:  str            =						0xe001
lowerCAmelCase							:  str            =						0xe002
lowerCAmelCase							:  Optional[int]            =						0xe003
lowerCAmelCase							:  List[Any]            =						0xe004

# Maps special codepoints to human-readable names.
lowerCAmelCase							:  Dict[int, str]            =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase							:  Dict[str, int]            =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					str=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					List[Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Optional[int]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					int=2048       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_:			str  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Dict  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						add_prefix_space=lowerCAmelCase__       ,						model_max_length=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE_:			Dict[str, int]  		=	{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self._special_codepoints)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self._unicode_vocab_size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									return list(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					str):
									try:
															return ord(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid token: '{token}'")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					int):
									try:
															if index in SPECIAL_CODEPOINTS:
																					return SPECIAL_CODEPOINTS[index]
															return chr(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid id: {index}")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Dict):
									return "".join(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			int  		=	cls + token_ids_a + sep
									if token_ids_a is not None:
															result += token_ids_a + sep
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	[1] + ([0] * len(lowerCAmelCase__)) + [1]
									if token_ids_a is not None:
															result += ([0] * len(lowerCAmelCase__)) + [1]
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(cls + token_ids_a + sep) * [0]
									if token_ids_a is not None:
															result += len(token_ids_a + sep) * [1]
									return result




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									return ()






