'''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,
)


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

A_        =   "\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 					UpperCAmelCase__      (							_lowerCAmelCase						: int,			_lowerCAmelCase						: Any,			_lowerCAmelCase						: Optional[Any]=8)						->		Optional[int]:



						"""simple docstring"""

						snake_case   :					Any							=						height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Tuple,			_lowerCAmelCase						: List[Any]=512,			_lowerCAmelCase						: int=512)						->		Any:



						"""simple docstring"""

						snake_case   :					str							=						pil_image.resize((w, h),			resample=Image.BICUBIC,			reducing_gap=1)
						snake_case   :					List[Any]							=						np.array(pil_image.convert("RGB"))
						snake_case   :					Optional[Any]							=						arr.astype(np.floataa) / 127.5 - 1
						snake_case   :					int							=						np.transpose(_lowerCAmelCase,			[2, 0, 1])
						snake_case   :					str							=						torch.from_numpy(_lowerCAmelCase).unsqueeze(0)
						return image




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






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

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



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_				):
									# get the original timestep using init_timestep
									snake_case   :					List[Any]							=						min(int(num_inference_steps * strength				)							, snake_case_				)

									snake_case   :					Optional[int]							=						max(num_inference_steps - init_timestep							, 0				)
									snake_case   :					int							=						self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_=None				):
									if not isinstance(snake_case_							, (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(snake_case_				)}"""				)

									snake_case   :					Union[str, Any]							=						image.to(device=snake_case_							, dtype=snake_case_				)

									snake_case   :					str							=						batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															snake_case   :					Dict							=						image

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

															elif isinstance(snake_case_							, snake_case_				):
																					snake_case   :					Optional[Any]							=						[
																					    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(snake_case_				)
																					]
																					snake_case   :					Optional[Any]							=						torch.cat(snake_case_							, dim=0				)
															else:
																					snake_case   :					List[Any]							=						self.movq.encode(snake_case_				).latent_dist.sample(snake_case_				)

															snake_case   :					List[str]							=						self.movq.config.scaling_factor * init_latents

									snake_case   :					int							=						torch.cat([init_latents]							, dim=0				)

									snake_case   :					str							=						init_latents.shape
									snake_case   :					Dict							=						randn_tensor(snake_case_							, generator=snake_case_							, device=snake_case_							, dtype=snake_case_				)

									# get latents
									snake_case   :					int							=						self.scheduler.add_noise(snake_case_							, snake_case_							, snake_case_				)

									snake_case   :					Optional[int]							=						init_latents

									return latents



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

									snake_case   :					Dict							=						torch.device(F"""cuda:{gpu_id}"""				)

									snake_case   :					List[str]							=						[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(snake_case_							, snake_case_				)



			def      lowerCamelCase   (       self							, snake_case_=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."				)

									snake_case   :					Dict							=						torch.device(F"""cuda:{gpu_id}"""				)

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

									snake_case   :					Any							=						None
									for cpu_offloaded_model in [self.unet, self.movq]:
															snake_case							,				snake_case   :					Tuple							=						cpu_offload_with_hook(snake_case_							, snake_case_							, prev_module_hook=snake_case_				)

									# We'll offload the last model manually.
									snake_case   :					Union[str, Any]							=						hook



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def      lowerCamelCase   (       self				):
									if not hasattr(self.unet							, "_hf_hook"				):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(snake_case_							, "_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(snake_case_				)
			def __call__(       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = 512							, snake_case_ = 512							, snake_case_ = 100							, snake_case_ = 4.0							, snake_case_ = 0.3							, snake_case_ = 1							, snake_case_ = None							, snake_case_ = "pil"							, snake_case_ = True							, ):
									snake_case   :					List[Any]							=						self._execution_device

									snake_case   :					int							=						guidance_scale > 1.0

									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					Optional[Any]							=						torch.cat(snake_case_							, dim=0				)
									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					int							=						torch.cat(snake_case_							, dim=0				)
									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					List[Any]							=						torch.cat(snake_case_							, dim=0				)

									snake_case   :					Tuple							=						image_embeds.shape[0]

									if do_classifier_free_guidance:
															snake_case   :					Union[str, Any]							=						image_embeds.repeat_interleave(snake_case_							, dim=0				)
															snake_case   :					str							=						negative_image_embeds.repeat_interleave(snake_case_							, dim=0				)
															snake_case   :					Dict							=						hint.repeat_interleave(snake_case_							, dim=0				)

									snake_case   :					int							=						torch.cat([negative_image_embeds, image_embeds]							, dim=0				).to(dtype=self.unet.dtype							, device=snake_case_				)
									snake_case   :					Optional[Any]							=						torch.cat([hint, hint]							, dim=0				).to(dtype=self.unet.dtype							, device=snake_case_				)

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

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

									snake_case   :					int							=						self.movq.encode(snake_case_				)["latents"]
									snake_case   :					Tuple							=						latents.repeat_interleave(snake_case_							, dim=0				)
									self.scheduler.set_timesteps(snake_case_							, device=snake_case_				)
									snake_case							,				snake_case   :					str							=						self.get_timesteps(snake_case_							, snake_case_							, snake_case_				)
									snake_case   :					Dict							=						timesteps[:1].repeat(batch_size * num_images_per_prompt				)
									snake_case							,				snake_case   :					Union[str, Any]							=						downscale_height_and_width(snake_case_							, snake_case_							, self.movq_scale_factor				)
									snake_case   :					List[Any]							=						self.prepare_latents(
									    snake_case_							, snake_case_							, snake_case_							, snake_case_							, image_embeds.dtype							, snake_case_							, snake_case_				)
									for i, t in enumerate(self.progress_bar(snake_case_				)				):
															# expand the latents if we are doing classifier free guidance
															snake_case   :					Union[str, Any]							=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

															snake_case   :					Any							=						{"image_embeds": image_embeds, "hint": hint}
															snake_case   :					List[str]							=						self.unet(
															    sample=snake_case_							, timestep=snake_case_							, encoder_hidden_states=snake_case_							, added_cond_kwargs=snake_case_							, return_dict=snake_case_							, )[0]

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

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

															snake_case   :					Union[str, Any]							=						self.scheduler.step(
															    snake_case_							, snake_case_							, snake_case_							, generator=snake_case_							, )[0]

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

									if output_type == "pil":
															snake_case   :					str							=						self.numpy_to_pil(snake_case_				)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=snake_case_				)






'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable




class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									snake_case   :					dict[str, bool | None]							=						{literal: None for literal in literals}



			def __str__(       self				):
									return "{" + " , ".join(self.literals				) + "}"



			def __len__(       self				):
									return len(self.literals				)



			def      lowerCamelCase   (       self							, snake_case_				):
									for literal in self.literals:
															snake_case   :					Union[str, Any]							=						literal[:2]
															if symbol in model:
																					snake_case   :					str							=						model[symbol]
															else:
																					continue
															if value is not None:
																					# Complement assignment if literal is in complemented form
																					if literal.endswith("'"				):
																											snake_case   :					Dict							=						not value
															snake_case   :					str							=						value






			def      lowerCamelCase   (       self							, snake_case_				):
									for literal in self.literals:
															snake_case   :					int							=						literal.rstrip("'"				) if literal.endswith("'"				) else literal + "'"
															if symbol in self.literals:
																					return True

									self.assign(snake_case_				)
									for value in self.literals.values():
															if value in (True, None):
																					return value
									return any(self.literals.values()				)







class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									snake_case   :					Tuple							=						list(snake_case_				)






			def __str__(       self				):
									return "{" + " , ".join(str(snake_case_				) for clause in self.clauses				) + "}"







def 					UpperCAmelCase__      (							)						->		Clause:



						"""simple docstring"""

						snake_case   :					Optional[Any]							=						[]
						snake_case   :					Optional[Any]							=						random.randint(1,			5)
						snake_case   :					Tuple							=						"A"
						snake_case   :					Optional[Any]							=						0
						while i < no_of_literals:
												snake_case   :					Union[str, Any]							=						random.randint(1,			5)
												snake_case   :					List[Any]							=						base_var + str(_lowerCAmelCase)
												snake_case   :					Union[str, Any]							=						random.randint(0,			1)
												if var_complement == 1:
																		var_name += "'"
												if var_name in literals:
																		i -= 1
												else:
																		literals.append(_lowerCAmelCase)
												i += 1
						return Clause(_lowerCAmelCase)


def 					UpperCAmelCase__      (							)						->		Formula:



						"""simple docstring"""

						snake_case   :					set[Clause]							=						set()
						snake_case   :					Union[str, Any]							=						random.randint(1,			10)
						while len(_lowerCAmelCase) < no_of_clauses:
												clauses.add(generate_clause())
						return Formula(_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Formula)						->		tuple[list[Clause], list[str]]:



						"""simple docstring"""

						snake_case   :					Dict							=						formula.clauses
						snake_case   :					List[Any]							=						[]
						for clause in formula.clauses:
												for literal in clause.literals:
																		snake_case   :					Any							=						literal[:2]
																		if symbol not in symbols_set:
																								symbols_set.append(_lowerCAmelCase)
						return clauses, symbols_set


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: list[str],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[list[str], dict[str, bool | None]]:



						"""simple docstring"""

						snake_case   :					Optional[int]							=						[]
						snake_case   :					dict[str, bool | None]							=						{}
						snake_case   :					Dict							=						[]

						for clause in clauses:
												if clause.evaluate(_lowerCAmelCase):
																		continue
												for literal in clause.literals:
																		literals.append(_lowerCAmelCase)

						for s in symbols:
												snake_case   :					Dict							=						s + "'"
												if (s in literals and sym not in literals) or (
												    s not in literals and sym in literals
												):
																		pure_symbols.append(_lowerCAmelCase)
						for p in pure_symbols:
												snake_case   :					List[Any]							=						None
						for s in pure_symbols:
												snake_case   :					Optional[Any]							=						s + "'"
												if s in literals:
																		snake_case   :					Dict							=						True
												elif sym in literals:
																		snake_case   :					Tuple							=						False
						return pure_symbols, assignment


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[list[str], dict[str, bool | None]]:



						"""simple docstring"""

						snake_case   :					Any							=						[]
						for clause in clauses:
												if len(_lowerCAmelCase) == 1:
																		unit_symbols.append(list(clause.literals.keys())[0])
												else:
																		snake_case							,				snake_case   :					Union[str, Any]							=						0, 0
																		for literal, value in clause.literals.items():
																								if value is False:
																														f_count += 1
																								elif value is None:
																														snake_case   :					str							=						literal
																														n_count += 1
																		if f_count == len(_lowerCAmelCase) - 1 and n_count == 1:
																								unit_symbols.append(_lowerCAmelCase)
						snake_case   :					dict[str, bool | None]							=						{}
						for i in unit_symbols:
												snake_case   :					str							=						i[:2]
												snake_case   :					Any							=						len(_lowerCAmelCase) == 2
						snake_case   :					int							=						[i[:2] for i in unit_symbols]

						return unit_symbols, assignment


def 					UpperCAmelCase__      (							_lowerCAmelCase						: list[Clause],			_lowerCAmelCase						: list[str],			_lowerCAmelCase						: dict[str, bool | None])						->		tuple[bool | None, dict[str, bool | None] | None]:



						"""simple docstring"""

						snake_case   :					Optional[int]							=						True
						for clause in clauses:
												snake_case   :					List[Any]							=						clause.evaluate(_lowerCAmelCase)
												if clause_check is False:
																		return False, None
												elif clause_check is None:
																		snake_case   :					Union[str, Any]							=						False
																		continue

						if check_clause_all_true:
												return True, model

						try:
												snake_case							,				snake_case   :					Union[str, Any]							=						find_pure_symbols(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase)
						except RecursionError:
												print("raises a RecursionError and is")
												return None, {}
						snake_case   :					Optional[int]							=						None
						if len(_lowerCAmelCase) > 0:
												snake_case							,				snake_case   :					List[Any]							=						pure_symbols[0], assignment[pure_symbols[0]]

						if p:
												snake_case   :					str							=						model
												snake_case   :					Union[str, Any]							=						value
												snake_case   :					Optional[Any]							=						list(_lowerCAmelCase)
												if p in tmp_symbols:
																		tmp_symbols.remove(_lowerCAmelCase)
												return dpll_algorithm(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase)

						snake_case							,				snake_case   :					Any							=						find_unit_clauses(_lowerCAmelCase,			_lowerCAmelCase)
						snake_case   :					Dict							=						None
						if len(_lowerCAmelCase) > 0:
												snake_case							,				snake_case   :					List[str]							=						unit_symbols[0], assignment[unit_symbols[0]]
						if p:
												snake_case   :					Any							=						model
												snake_case   :					Union[str, Any]							=						value
												snake_case   :					Optional[int]							=						list(_lowerCAmelCase)
												if p in tmp_symbols:
																		tmp_symbols.remove(_lowerCAmelCase)
												return dpll_algorithm(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase)
						snake_case   :					int							=						symbols[0]
						snake_case   :					str							=						symbols[1:]
						snake_case							,				snake_case   :					Union[str, Any]							=						model, model
						snake_case							,				snake_case   :					Optional[Any]							=						True, False

						return dpll_algorithm(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase) or dpll_algorithm(_lowerCAmelCase,			_lowerCAmelCase,			_lowerCAmelCase)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A_        =   generate_formula()
						print(f"The formula {formula} is", end=" ")

						A_      ,   A_        =   generate_parameters(formula)
						A_      ,   A_        =   dpll_algorithm(clauses, symbols, {})

						if solution:
												print(f"satisfiable with the assignment {model}.")
						else:
												print("not satisfiable.")





