




'''simple docstring'''


from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class       A   :







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  Collection[float] | None = None )      ->							List[str]:


														"""simple docstring"""


														if components is None:
																					_a     					=    []
														_a     					=    list(__lowercase )







							def __len__(  self	:  Any )      ->							Optional[int]:


														"""simple docstring"""


														return len(self.__components )







							def __str__(  self	:  Any )      ->							Tuple:


														"""simple docstring"""


														return "(" + ",".join(map(__lowercase							,      self.__components ) ) + ")"







							def __add__(  self	:  List[str]							,      lowerCAmelCase_	:  Vector )      ->							str:


														"""simple docstring"""


														_a     					=    len(self )
														if size == len(__lowercase ):
																					_a     					=    [self.__components[i] + other.component(__lowercase ) for i in range(__lowercase )]
																					return Vector(__lowercase )
														else:
																					raise Exception('''must have the same size''' )







							def __sub__(  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Vector )      ->							int:


														"""simple docstring"""


														_a     					=    len(self )
														if size == len(__lowercase ):
																					_a     					=    [self.__components[i] - other.component(__lowercase ) for i in range(__lowercase )]
																					return Vector(__lowercase )
														else:  # error case
																					raise Exception('''must have the same size''' )







							@overload
							def __mul__(  self	:  str							,      lowerCAmelCase_	:  float )      ->							str:


														"""simple docstring"""


														...







							@overload
							def __mul__(  self	:  int							,      lowerCAmelCase_	:  Vector )      ->							List[str]:


														"""simple docstring"""


														...







							def __mul__(  self	:  List[str]							,      lowerCAmelCase_	:  float | Vector )      ->							Optional[int]:


														"""simple docstring"""


														if isinstance(__lowercase							,      (float, int) ):
																					_a     					=    [c * other for c in self.__components]
																					return Vector(__lowercase )
														elif isinstance(__lowercase							,      __lowercase ) and len(self ) == len(__lowercase ):
																					_a     					=    len(self )
																					_a     					=    [self.__components[i] * other.component(__lowercase ) for i in range(__lowercase )]
																					return sum(__lowercase )
														else:  # error case
																					raise Exception('''invalid operand!''' )







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Optional[Any]:


														"""simple docstring"""


														return Vector(self.__components )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  int )      ->							Tuple:


														"""simple docstring"""


														if isinstance(__lowercase							,      __lowercase ) and -len(self.__components ) <= i < len(self.__components ):
																					return self.__components[i]
														else:
																					raise Exception('''index out of range''' )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  float )      ->							int:


														"""simple docstring"""


														assert -len(self.__components ) <= pos < len(self.__components )
														_a     					=    value







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Optional[int]:


														"""simple docstring"""


														if len(self.__components ) == 0:
																					raise Exception('''Vector is empty''' )
														_a     					=    [c**2 for c in self.__components]
														return math.sqrt(sum(__lowercase ) )







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Vector							,      lowerCAmelCase_	:  bool = False )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    self * other
														_a     					=    self.euclidean_length() * other.euclidean_length()
														if deg:
																					return math.degrees(math.acos(num / den ) )
														else:
																					return math.acos(num / den )



def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							assert isinstance(__lowerCAmelCase			,						__lowerCAmelCase				)
							return Vector([0] * dimension				)
def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							assert isinstance(__lowerCAmelCase			,						__lowerCAmelCase				) and (isinstance(__lowerCAmelCase			,						__lowerCAmelCase				))
							_a     					=    [0] * dimension
							_a     					=    1
							return Vector(__lowerCAmelCase				)
def 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


							assert (
							    isinstance(__lowerCAmelCase			,						__lowerCAmelCase				)
							    and isinstance(__lowerCAmelCase			,						__lowerCAmelCase				)
							    and (isinstance(__lowerCAmelCase			,						(int, float)				))
							)
							return x * scalar + y
def 		snake_case_   (UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  int			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							random.seed(__lowerCAmelCase				)
							_a     					=    [random.randint(__lowerCAmelCase			,						__lowerCAmelCase				) for _ in range(__lowerCAmelCase				)]
							return Vector(__lowerCAmelCase				)

class       A   :







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  list[list[float]]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							List[str]:


														"""simple docstring"""


														_a     					=    matrix
														_a     					=    w
														_a     					=    h







							def __str__(  self	:  List[str] )      ->							Dict:


														"""simple docstring"""


														_a     					=    ''''''
														for i in range(self.__height ):
																					ans += "|"
																					for j in range(self.__width ):
																												if j < self.__width - 1:
																																			ans += str(self.__matrix[i][j] ) + ","
																												else:
																																			ans += str(self.__matrix[i][j] ) + "|\n"
														return ans







							def __add__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  Matrix )      ->							Dict:


														"""simple docstring"""


														if self.__width == other.width() and self.__height == other.height():
																					_a     					=    []
																					for i in range(self.__height ):
																												_a     					=    [
																												    self.__matrix[i][j] + other.component(__lowercase							,      __lowercase )
																												    for j in range(self.__width )
																												]
																												matrix.append(__lowercase )
																					return Matrix(__lowercase							,      self.__width							,      self.__height )
														else:
																					raise Exception('''matrix must have the same dimension!''' )







							def __sub__(  self	:  Union[str, Any]							,      lowerCAmelCase_	:  Matrix )      ->							List[str]:


														"""simple docstring"""


														if self.__width == other.width() and self.__height == other.height():
																					_a     					=    []
																					for i in range(self.__height ):
																												_a     					=    [
																												    self.__matrix[i][j] - other.component(__lowercase							,      __lowercase )
																												    for j in range(self.__width )
																												]
																												matrix.append(__lowercase )
																					return Matrix(__lowercase							,      self.__width							,      self.__height )
														else:
																					raise Exception('''matrices must have the same dimension!''' )







							@overload
							def __mul__(  self	:  List[Any]							,      lowerCAmelCase_	:  float )      ->							Tuple:


														"""simple docstring"""


														...







							@overload
							def __mul__(  self	:  int							,      lowerCAmelCase_	:  Vector )      ->							Union[str, Any]:


														"""simple docstring"""


														...







							def __mul__(  self	:  Any							,      lowerCAmelCase_	:  float | Vector )      ->							List[str]:


														"""simple docstring"""


														if isinstance(__lowercase							,      __lowercase ):  # matrix-vector
																					if len(__lowercase ) == self.__width:
																												_a     					=    zero_vector(self.__height )
																												for i in range(self.__height ):
																																			_a     					=    [
																																			    self.__matrix[i][j] * other.component(__lowercase )
																																			    for j in range(self.__width )
																																			]
																																			ans.change_component(__lowercase							,      sum(__lowercase ) )
																												return ans
																					else:
																												raise Exception(
																												    '''vector must have the same size as the '''
																												    '''number of columns of the matrix!''' )
														elif isinstance(__lowercase							,      (int, float) ):  # matrix-scalar
																					_a     					=    [
																					    [self.__matrix[i][j] * other for j in range(self.__width )]
																					    for i in range(self.__height )
																					]
																					return Matrix(__lowercase							,      self.__width							,      self.__height )
														return None







							def        __lowerCAmelCase      (  self	:  List[Any] )      ->							Optional[Any]:


														"""simple docstring"""


														return self.__height







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							List[str]:


														"""simple docstring"""


														return self.__width







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							int:


														"""simple docstring"""


														if 0 <= x < self.__height and 0 <= y < self.__width:
																					return self.__matrix[x][y]
														else:
																					raise Exception('''change_component: indices out of bounds''' )







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  float )      ->							Dict:


														"""simple docstring"""


														if 0 <= x < self.__height and 0 <= y < self.__width:
																					_a     					=    value
														else:
																					raise Exception('''change_component: indices out of bounds''' )







							def        __lowerCAmelCase      (  self	:  Union[str, Any]							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							Tuple:


														"""simple docstring"""


														if self.__height != self.__width:
																					raise Exception('''Matrix is not square''' )
														_a     					=    self.__matrix[:x] + self.__matrix[x + 1 :]
														for i in range(len(__lowercase ) ):
																					_a     					=    minor[i][:y] + minor[i][y + 1 :]
														return Matrix(__lowercase							,      self.__width - 1							,      self.__height - 1 ).determinant()







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int )      ->							List[str]:


														"""simple docstring"""


														if self.__height != self.__width:
																					raise Exception('''Matrix is not square''' )
														if 0 <= x < self.__height and 0 <= y < self.__width:
																					return (-1) ** (x + y) * self.minor(__lowercase							,      __lowercase )
														else:
																					raise Exception('''Indices out of bounds''' )







							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Union[str, Any]:


														"""simple docstring"""


														if self.__height != self.__width:
																					raise Exception('''Matrix is not square''' )
														if self.__height < 1:
																					raise Exception('''Matrix has no element''' )
														elif self.__height == 1:
																					return self.__matrix[0][0]
														elif self.__height == 2:
																					return (
																					    self.__matrix[0][0] * self.__matrix[1][1]
																					    - self.__matrix[0][1] * self.__matrix[1][0]
																					)
														else:
																					_a     					=    [
																					    self.__matrix[0][y] * self.cofactor(0							,      __lowercase ) for y in range(self.__width )
																					]
																					return sum(__lowercase )



def 		snake_case_   (UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


							_a     					=    [[0] * n for _ in range(__lowerCAmelCase				)]
							return Matrix(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase				)

def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							random.seed(__lowerCAmelCase				)
							_a     					=    [
							    [random.randint(__lowerCAmelCase			,						__lowerCAmelCase				) for _ in range(__lowerCAmelCase				)] for _ in range(__lowerCAmelCase				)
							]
							return Matrix(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase				)









'''simple docstring'''


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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

class       A   (		_a   ):







							def __init__(  self	:  Dict							,      lowerCAmelCase_	:  CLIPSegForImageSegmentation							,      lowerCAmelCase_	:  CLIPSegProcessor							,      lowerCAmelCase_	:  AutoencoderKL							,      lowerCAmelCase_	:  CLIPTextModel							,      lowerCAmelCase_	:  CLIPTokenizer							,      lowerCAmelCase_	:  UNetaDConditionModel							,      lowerCAmelCase_	:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,      lowerCAmelCase_	:  StableDiffusionSafetyChecker							,      lowerCAmelCase_	:  CLIPImageProcessor							,      )      ->							int:


														"""simple docstring"""


														super().__init__()

														if hasattr(scheduler.config							,      '''steps_offset''' ) and scheduler.config.steps_offset != 1:
																					_a     					=    (
																					    F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
																					    F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
																					    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																					    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																					    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																					    ''' file'''
																					)
																					deprecate('''steps_offset!=1'''							,      '''1.0.0'''							,      lowerCAmelCase_							,      standard_warn=lowerCAmelCase_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    1
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														if hasattr(scheduler.config							,      '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
																					_a     					=    (
																					    F'The configuration file of this scheduler: {scheduler} has not set the configuration'
																					    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
																					    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
																					    ''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
																					    ''' Hub, it would be very nice if you could open a Pull request for the'''
																					    ''' `scheduler/scheduler_config.json` file'''
																					)
																					deprecate('''skip_prk_steps not set'''							,      '''1.0.0'''							,      lowerCAmelCase_							,      standard_warn=lowerCAmelCase_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    True
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														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(
														    segmentation_model=lowerCAmelCase_							,      segmentation_processor=lowerCAmelCase_							,      vae=lowerCAmelCase_							,      text_encoder=lowerCAmelCase_							,      tokenizer=lowerCAmelCase_							,      unet=lowerCAmelCase_							,      scheduler=lowerCAmelCase_							,      safety_checker=lowerCAmelCase_							,      feature_extractor=lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[Union[str, int]] = "auto" )      ->							Any:


														"""simple docstring"""


														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					_a     					=    self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Any:


														"""simple docstring"""


														self.enable_attention_slicing(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[Any]:


														"""simple docstring"""


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

														_a     					=    torch.device('''cuda''' )

														for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowerCAmelCase_							,      lowerCAmelCase_ )







							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


														if self.device != torch.device('''meta''' ) or 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()
							def __call__(  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, List[str]]							,      lowerCAmelCase_	:  Union[torch.FloatTensor, PIL.Image.Image]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 50							,      lowerCAmelCase_	:  float = 7.5							,      lowerCAmelCase_	:  Optional[Union[str, List[str]]] = None							,      lowerCAmelCase_	:  Optional[int] = 1							,      lowerCAmelCase_	:  float = 0.0							,      lowerCAmelCase_	:  Optional[torch.Generator] = None							,      lowerCAmelCase_	:  Optional[torch.FloatTensor] = None							,      lowerCAmelCase_	:  Optional[str] = "pil"							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,      lowerCAmelCase_	:  int = 1							,      **lowerCAmelCase_	:  Any							,      )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.segmentation_processor(
														    text=[text]							,      images=[image]							,      padding='''max_length'''							,      return_tensors='''pt''' ).to(self.device )
														_a     					=    self.segmentation_model(**lowerCAmelCase_ )
														_a     					=    torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
														_a     					=    self.numpy_to_pil(lowerCAmelCase_ )[0].resize(image.size )

														# Run inpainting pipeline with the generated mask
														_a     					=    StableDiffusionInpaintPipeline(
														    vae=self.vae							,      text_encoder=self.text_encoder							,      tokenizer=self.tokenizer							,      unet=self.unet							,      scheduler=self.scheduler							,      safety_checker=self.safety_checker							,      feature_extractor=self.feature_extractor							,      )
														return inpainting_pipeline(
														    prompt=lowerCAmelCase_							,      image=lowerCAmelCase_							,      mask_image=lowerCAmelCase_							,      height=lowerCAmelCase_							,      width=lowerCAmelCase_							,      num_inference_steps=lowerCAmelCase_							,      guidance_scale=lowerCAmelCase_							,      negative_prompt=lowerCAmelCase_							,      num_images_per_prompt=lowerCAmelCase_							,      eta=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      latents=lowerCAmelCase_							,      output_type=lowerCAmelCase_							,      return_dict=lowerCAmelCase_							,      callback=lowerCAmelCase_							,      callback_steps=lowerCAmelCase_							,      )

