
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,
)


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

lowerCamelCase__	=							'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=8 )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		_UpperCamelCase						    =	width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


def 							__A(lowerCAmelCase						,  lowerCAmelCase=5_1_2						,  lowerCAmelCase=5_1_2 )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	pil_image.resize((w, h)						,  resample=Image.BICUBIC						,  reducing_gap=1 )
		_UpperCamelCase						    =	np.array(pil_image.convert("""RGB""" ) )
		_UpperCamelCase						    =	arr.astype(np.floataa ) / 1_2_7.5 - 1
		_UpperCamelCase						    =	np.transpose(__UpperCamelCase						,  [2, 0, 1] )
		_UpperCamelCase						    =	torch.from_numpy(__UpperCamelCase ).unsqueeze(0 )
		return image







class        lowerCAmelCase__      (   SCREAMING_SNAKE_CASE_	):

						def __init__(			self       ,							a       ,							a       ,							a       ,							)					->      Dict:


								'''simple docstring'''

								super().__init__()

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

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	min(int(num_inference_steps * strength				)       ,							UpperCamelCase__				)

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

								return timesteps, num_inference_steps - t_start

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a       ,							a=None				)					->      List[str]:


								'''simple docstring'''

								if not isinstance(UpperCamelCase__       ,							(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(UpperCamelCase__				)}'				)

								_UpperCamelCase						    =	image.to(device=UpperCamelCase__       ,							dtype=UpperCamelCase__				)

								_UpperCamelCase						    =	batch_size * num_images_per_prompt

								if image.shape[1] == 4:
										_UpperCamelCase						    =	image

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

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

										_UpperCamelCase						    =	self.movq.config.scaling_factor * init_latents

								_UpperCamelCase						    =	torch.cat([init_latents]       ,							dim=0				)

								_UpperCamelCase						    =	init_latents.shape
								_UpperCamelCase						    =	randn_tensor(UpperCamelCase__       ,							generator=UpperCamelCase__       ,							device=UpperCamelCase__       ,							dtype=UpperCamelCase__				)

								# get latents
								_UpperCamelCase						    =	self.scheduler.add_noise(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

								_UpperCamelCase						    =	init_latents

								return latents

						def  A_				(			self       ,							a=0				)					->      Union[str, Any]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	torch.device(F'cuda:{gpu_id}'				)

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

						def  A_				(			self       ,							a=0				)					->      Optional[Any]:


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

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

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

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

						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								if not hasattr(self.unet       ,							"""_hf_hook"""				):
										return self.device
								for module in self.unet.modules():
										if (
										    hasattr(UpperCamelCase__       ,							"""_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(UpperCamelCase__				)
						def __call__(			self       ,							a       ,							a       ,							a       ,							a = 5_12       ,							a = 5_12       ,							a = 1_00       ,							a = 4.0       ,							a = 0.3       ,							a = 1       ,							a = None       ,							a = "pil"       ,							a = True       ,							)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._execution_device

								_UpperCamelCase						    =	guidance_scale > 1.0

								if isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
										_UpperCamelCase						    =	torch.cat(UpperCamelCase__       ,							dim=0				)
								_UpperCamelCase						    =	image_embeds.shape[0]
								if isinstance(UpperCamelCase__       ,							UpperCamelCase__				):
										_UpperCamelCase						    =	torch.cat(UpperCamelCase__       ,							dim=0				)

								if do_classifier_free_guidance:
										_UpperCamelCase						    =	image_embeds.repeat_interleave(UpperCamelCase__       ,							dim=0				)
										_UpperCamelCase						    =	negative_image_embeds.repeat_interleave(UpperCamelCase__       ,							dim=0				)

								_UpperCamelCase						    =	torch.cat([negative_image_embeds, image_embeds]       ,							dim=0				).to(dtype=self.unet.dtype       ,							device=UpperCamelCase__				)

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

								_UpperCamelCase						    =	torch.cat([prepare_image(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				) for i in image]       ,							dim=0				)
								_UpperCamelCase						    =	image.to(dtype=image_embeds.dtype       ,							device=UpperCamelCase__				)

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

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

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

										# compute the previous noisy sample x_t -> x_t-1
										_UpperCamelCase						    =	self.scheduler.step(
										    UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							generator=UpperCamelCase__       ,							)[0]

								# post-processing
								_UpperCamelCase						    =	self.movq.decode(UpperCamelCase__       ,							force_not_quantize=UpperCamelCase__				)['''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						    =	image * 0.5 + 0.5
										_UpperCamelCase						    =	image.clamp(0       ,							1				)
										_UpperCamelCase						    =	image.cpu().permute(0       ,							2       ,							3       ,							1				).float().numpy()

								if output_type == "pil":
										_UpperCamelCase						    =	self.numpy_to_pil(UpperCamelCase__				)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=UpperCamelCase__				)



from __future__ import annotations


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	len(lowerCAmelCase )

		# If row is equal to the size of the board it means there are a queen in each row in
		# the current board (possible_board)
		if row == n:
				# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
				# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
				boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
				return

		# We iterate each column in the row to find all possible results in each row
		for col in range(lowerCAmelCase ):
				# We apply that we learned previously. First we check that in the current board
				# (possible_board) there are not other same value because if there is it means
				# that there are a collision in vertical. Then we apply the two formulas we
				# learned before:
				#
				# 45º: y - x = b or 45: row - col = b
				# 135º: y + x = b or row + col = b.
				#
				# And we verify if the results of this two formulas not exist in their variables
				# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
				#
				# If any or these are True it means there is a collision so we continue to the
				# next value in the for loop.
				if (
				    col in possible_board
				    or row - col in diagonal_right_collisions
				    or row + col in diagonal_left_collisions
				):
						continue

				# If it is False we call dfs function again and we update the inputs
				depth_first_search(
				    [*possible_board, col]						,  [*diagonal_right_collisions, row - col]						,  [*diagonal_left_collisions, row + col]						,  lowerCAmelCase						,  lowerCAmelCase						,  )


def 							__A(lowerCAmelCase )		->				None:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		depth_first_search([]						,  []						,  []						,  lowerCAmelCase						,  lowerCAmelCase )

		# Print all the boards
		for board in boards:
				for column in board:
						print(lowerCAmelCase )
				print("""""" )

		print(len(lowerCAmelCase )						,  """solutions were found.""" )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		n_queens_solution(4)



