"""simple docstring"""







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        super().__init__()

        if safety_checker is None:
             logger.warning(
                 f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                 ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                 ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                 ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                 ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                 ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'			)

        self.register_modules(
            speech_model=__UpperCamelCase     ,speech_processor=__UpperCamelCase     ,vae=__UpperCamelCase     ,text_encoder=__UpperCamelCase     ,tokenizer=__UpperCamelCase     ,unet=__UpperCamelCase     ,scheduler=__UpperCamelCase     ,feature_extractor=__UpperCamelCase     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = "auto"			)	->      int:


        '''simple docstring'''


        if slice_size == "auto":
             lowercase_	:				Dict													=	self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        self.enable_attention_slicing(__UpperCamelCase			)







   @torch.no_grad()
   def __call__(			self     ,__UpperCamelCase     ,__UpperCamelCase=1_6000     ,__UpperCamelCase = 512     ,__UpperCamelCase = 512     ,__UpperCamelCase = 50     ,__UpperCamelCase = 7.5     ,__UpperCamelCase = None     ,__UpperCamelCase = 1     ,__UpperCamelCase = 0.0     ,__UpperCamelCase = None     ,__UpperCamelCase = None     ,__UpperCamelCase = "pil"     ,__UpperCamelCase = True     ,__UpperCamelCase = None     ,__UpperCamelCase = 1     ,**__UpperCamelCase     ,)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.speech_processor.feature_extractor(
            __UpperCamelCase     ,return_tensors='pt'     ,sampling_rate=__UpperCamelCase			).input_features.to(self.device			)
        lowercase_	:				int													=	self.speech_model.generate(__UpperCamelCase     ,max_length=48_0000			)

        lowercase_	:				Union[str, Any]													=	self.speech_processor.tokenizer.batch_decode(__UpperCamelCase     ,skip_special_tokens=__UpperCamelCase     ,normalize=__UpperCamelCase			)[
            0
        ]

        if isinstance(__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				Optional[int]													=	1
        elif isinstance(__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				Union[str, Any]													=	len(__UpperCamelCase			)
        else:
             raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase			)}'''			)

        if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''			)

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__UpperCamelCase     ,__UpperCamelCase			) or callback_steps <= 0)
        ):
             raise ValueError(
                 f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                 f''' {type(__UpperCamelCase			)}.'''			)

        # get prompt text embeddings
        lowercase_	:				Tuple													=	self.tokenizer(
            __UpperCamelCase     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,return_tensors='pt'     ,)
        lowercase_	:				Optional[Any]													=	text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowercase_	:				List[Any]													=	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
             logger.warning(
                 'The following part of your input was truncated because CLIP can only handle sequences up to'
                 f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)
             lowercase_	:				List[Any]													=	text_input_ids[:, : self.tokenizer.model_max_length]
        lowercase_	:				Tuple													=	self.text_encoder(text_input_ids.to(self.device			)			)[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        lowercase_							,  lowercase_							,  lowercase_	:				Union[str, Any]													=	text_embeddings.shape
        lowercase_	:				Optional[Any]													=	text_embeddings.repeat(1     ,__UpperCamelCase     ,1			)
        lowercase_	:				Union[str, Any]													=	text_embeddings.view(bs_embed * num_images_per_prompt     ,__UpperCamelCase     ,-1			)

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        lowercase_	:				List[Any]													=	guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
             lowercase_	:				List[str]
             if negative_prompt is None:
                  lowercase_	:				Optional[int]													=	[''] * batch_size
             elif type(__UpperCamelCase			) is not type(__UpperCamelCase			):
                  raise TypeError(
                      f'''`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCamelCase			)} !='''
                      f''' {type(__UpperCamelCase			)}.'''			)
             elif isinstance(__UpperCamelCase     ,__UpperCamelCase			):
                  lowercase_	:				Optional[Any]													=	[negative_prompt]
             elif batch_size != len(__UpperCamelCase			):
                  raise ValueError(
                      f'''`negative_prompt`: {negative_prompt} has batch size {len(__UpperCamelCase			)}, but `prompt`:'''
                      f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                      ' the batch size of `prompt`.'			)
             else:
                  lowercase_	:				Optional[int]													=	negative_prompt

             lowercase_	:				Union[str, Any]													=	text_input_ids.shape[-1]
             lowercase_	:				List[str]													=	self.tokenizer(
                 __UpperCamelCase     ,padding='max_length'     ,max_length=__UpperCamelCase     ,truncation=__UpperCamelCase     ,return_tensors='pt'     ,)
             lowercase_	:				Dict													=	self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowercase_	:				Optional[Any]													=	uncond_embeddings.shape[1]
             lowercase_	:				List[str]													=	uncond_embeddings.repeat(1     ,__UpperCamelCase     ,1			)
             lowercase_	:				str													=	uncond_embeddings.view(batch_size * num_images_per_prompt     ,__UpperCamelCase     ,-1			)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             lowercase_	:				Optional[Any]													=	torch.cat([uncond_embeddings, text_embeddings]			)

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        lowercase_	:				Optional[Any]													=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        lowercase_	:				Any													=	text_embeddings.dtype
        if latents is None:
             if self.device.type == "mps":
                  # randn does not exist on mps
                  lowercase_	:				int													=	torch.randn(__UpperCamelCase     ,generator=__UpperCamelCase     ,device='cpu'     ,dtype=__UpperCamelCase			).to(
                      self.device			)
             else:
                  lowercase_	:				List[Any]													=	torch.randn(__UpperCamelCase     ,generator=__UpperCamelCase     ,device=self.device     ,dtype=__UpperCamelCase			)
        else:
             if latents.shape != latents_shape:
                  raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
             lowercase_	:				Any													=	latents.to(self.device			)

        # set timesteps
        self.scheduler.set_timesteps(__UpperCamelCase			)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        lowercase_	:				int													=	self.scheduler.timesteps.to(self.device			)

        # scale the initial noise by the standard deviation required by the scheduler
        lowercase_	:				List[Any]													=	latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        lowercase_	:				List[str]													=	'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
        lowercase_	:				Union[str, Any]													=	{}
        if accepts_eta:
             lowercase_	:				Union[str, Any]													=	eta

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

             # predict the noise residual
             lowercase_	:				Tuple													=	self.unet(__UpperCamelCase     ,__UpperCamelCase     ,encoder_hidden_states=__UpperCamelCase			).sample

             # perform guidance
             if do_classifier_free_guidance:
                  lowercase_							,  lowercase_	:				List[Any]													=	noise_pred.chunk(2			)
                  lowercase_	:				List[str]													=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             lowercase_	:				Any													=	self.scheduler.step(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                  callback(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	1 / 0.1_8215 * latents
        lowercase_	:				Any													=	self.vae.decode(__UpperCamelCase			).sample

        lowercase_	:				Any													=	(image / 2 + 0.5).clamp(0     ,1			)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        lowercase_	:				Any													=	image.cpu().permute(0     ,2     ,3     ,1			).float().numpy()

        if output_type == "pil":
             lowercase_	:				Union[str, Any]													=	self.numpy_to_pil(__UpperCamelCase			)

        if not return_dict:
             return image

        return StableDiffusionPipelineOutput(images=__UpperCamelCase     ,nsfw_content_detected=__UpperCamelCase			)


"""simple docstring"""







from __future__ import annotations

__SCREAMING_SNAKE_CASE    	=[]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if board[row][i] == 1:
               return False
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )     ,			range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )      ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(__SCREAMING_SNAKE_CASE     ,			-1     ,			-1      )     ,			range(__SCREAMING_SNAKE_CASE     ,			len(__SCREAMING_SNAKE_CASE      )      )      ):
          if board[i][j] == 1:
               return False
     return True



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     if row >= len(__SCREAMING_SNAKE_CASE      ):
          solution.append(__SCREAMING_SNAKE_CASE      )
          printboard(__SCREAMING_SNAKE_CASE      )
          print()
          return True
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if is_safe(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				int													=	1
               solve(__SCREAMING_SNAKE_CASE     ,			row + 1      )
               lowercase_	:				Dict													=	0
     return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          for j in range(len(__SCREAMING_SNAKE_CASE      )      ):
               if board[i][j] == 1:
                    print('Q'     ,			end=' '      )
               else:
                    print('.'     ,			end=' '      )
          print()


# n=int(input("The no. of queens"))
__SCREAMING_SNAKE_CASE    	=8
__SCREAMING_SNAKE_CASE    	=[[0 for i in range(n)] for j in range(n)]
solve(board, 0)







print("The total no. of solutions are :", len(solution))

