
'''simple docstring'''

from __future__ import annotations

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






class  A  :





	def __init__(       self      :							Dict   ,				__magic_name__      :							Collection[float] | None = None				):


				"""simple docstring"""

				if components is None:
							lowerCAmelCase__										=				[]
				lowerCAmelCase__										=				list(__magic_name__				)





	def __len__(       self      :							Optional[Any]				):


				"""simple docstring"""

				return len(self.__components				)





	def __str__(       self      :							Optional[Any]				):


				"""simple docstring"""

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





	def __add__(       self      :							Dict   ,				__magic_name__      :							Vector				):


				"""simple docstring"""

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





	def __sub__(       self      :							Tuple   ,				__magic_name__      :							Vector				):


				"""simple docstring"""

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





	@overload
	def __mul__(       self      :							Optional[Any]   ,				__magic_name__      :							float				):


				"""simple docstring"""

				...





	@overload
	def __mul__(       self      :							Union[str, Any]   ,				__magic_name__      :							Vector				):


				"""simple docstring"""

				...





	def __mul__(       self      :							Union[str, Any]   ,				__magic_name__      :							float | Vector				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				return Vector(self.__components				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							int				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							float				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							Vector   ,				__magic_name__      :							bool = False				):


				"""simple docstring"""

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







def 	A  (   UpperCamelCase_       :  int   )       ->							Vector:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_    ,						UpperCamelCase_   )
			return Vector([0] * dimension   )







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int   )       ->							Vector:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_    ,						UpperCamelCase_   ) and (isinstance(UpperCamelCase_    ,						UpperCamelCase_   ))
			lowerCAmelCase__										=				[0] * dimension
			lowerCAmelCase__										=				1
			return Vector(UpperCamelCase_   )







def 	A  (   UpperCamelCase_       :  float    ,						UpperCamelCase_       :  Vector    ,						UpperCamelCase_       :  Vector   )       ->							Vector:







			'''simple docstring'''



			assert (
			    isinstance(UpperCamelCase_    ,						UpperCamelCase_   )
			    and isinstance(UpperCamelCase_    ,						UpperCamelCase_   )
			    and (isinstance(UpperCamelCase_    ,						(int, float)   ))
			)
			return x * scalar + y







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int   )       ->							Vector:







			'''simple docstring'''



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






class  A  :





	def __init__(       self      :							Dict   ,				__magic_name__      :							list[list[float]]   ,				__magic_name__      :							int   ,				__magic_name__      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				matrix
				lowerCAmelCase__										=				w
				lowerCAmelCase__										=				h





	def __str__(       self      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				""
				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      :							int   ,				__magic_name__      :							Matrix				):


				"""simple docstring"""

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





	def __sub__(       self      :							Dict   ,				__magic_name__      :							Matrix				):


				"""simple docstring"""

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





	@overload
	def __mul__(       self      :							Optional[Any]   ,				__magic_name__      :							float				):


				"""simple docstring"""

				...





	@overload
	def __mul__(       self      :							Optional[Any]   ,				__magic_name__      :							Vector				):


				"""simple docstring"""

				...





	def __mul__(       self      :							Optional[int]   ,				__magic_name__      :							float | Vector				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				return self.__height





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any				):


				"""simple docstring"""

				return self.__width





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							int   ,				__magic_name__      :							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 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							int   ,				__magic_name__      :							float				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							int				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							int   ,				__magic_name__      :							int				):


				"""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(__magic_name__   ,				__magic_name__				)
				else:
							raise Exception("Indices out of bounds"				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[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:
							lowerCAmelCase__										=				[
							    self.__matrix[0][y] * self.cofactor(0   ,				__magic_name__				) for y in range(self.__width				)
							]
							return sum(__magic_name__				)







def 	A  (   UpperCamelCase_       :  int   )       ->							Matrix:







			'''simple docstring'''



			lowerCAmelCase__										=				[[0] * n for _ in range(UpperCamelCase_   )]
			return Matrix(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_   )







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int    ,						UpperCamelCase_       :  int   )       ->							Matrix:







			'''simple docstring'''



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












"""simple docstring"""
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser()
 parser.add_argument(
     '-m'  ,     '--pretrained_model_name_or_path'  ,     type=_UpperCAmelCase  ,     default=_UpperCAmelCase  ,     required=_UpperCAmelCase  ,     help='Path to pretrained model or model identifier from huggingface.co/models.'  ,     )
 parser.add_argument(
     '-c'  ,     '--caption'  ,     type=_UpperCAmelCase  ,     default='robotic cat with wings'  ,     help='Text used to generate images.'  ,     )
 parser.add_argument(
     '-n'  ,     '--images_num'  ,     type=_UpperCAmelCase  ,     default=4  ,     help='How much images to generate.'  ,     )
 parser.add_argument(
     '-s'  ,     '--seed'  ,     type=_UpperCAmelCase  ,     default=42  ,     help='Seed for random process.'  ,     )
 parser.add_argument(
     '-ci'  ,     '--cuda_id'  ,     type=_UpperCAmelCase  ,     default=0  ,     help='cuda_id.'  ,     )
 lowerCAmelCase    				= parser.parse_args()
 return args

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[int]			):
 if not len(_UpperCAmelCase			) == rows * cols:
  raise ValueError('The specified number of rows and columns are not correct.'			)

 lowerCAmelCase				,lowerCAmelCase    				= imgs[0].size
 lowerCAmelCase    				= Image.new('RGB'  ,     size=(cols * w, rows * h)			)
 lowerCAmelCase				,lowerCAmelCase    				= grid.size

 for i, img in enumerate(_UpperCAmelCase			):
  grid.paste(_UpperCAmelCase  ,     box=(i % cols * w, i // cols * h)			)
 return grid

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Union[str, Any]="robotic cat with wings"  ,     _UpperCAmelCase					:    Optional[int]=7.5  ,     _UpperCAmelCase					:    Dict=50  ,     _UpperCAmelCase					:    Tuple=1  ,     _UpperCAmelCase					:    int=42  ,     ):
 lowerCAmelCase    				= torch.Generator(pipeline.device			).manual_seed(_UpperCAmelCase			)
 lowerCAmelCase    				= pipeline(
     _UpperCAmelCase  ,     guidance_scale=_UpperCAmelCase  ,     num_inference_steps=_UpperCAmelCase  ,     generator=_UpperCAmelCase  ,     num_images_per_prompt=_UpperCAmelCase  ,     ).images
 lowerCAmelCase    				= int(math.sqrt(_UpperCAmelCase			)			)
 lowerCAmelCase    				= image_grid(_UpperCAmelCase  ,     rows=_rows  ,     cols=num_images_per_prompt // _rows			)
 return grid, images


__UpperCamelCase     :						Optional[Any]						       =  parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase     :						List[Any]						       =  CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase     :						str						       =  CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase     :						Optional[int]						       =  AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase     :						List[str]						       =  UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

__UpperCamelCase     :						Tuple						       =  StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase     :						Union[str, Any]						       =  lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
       __UpperCamelCase     :						Dict						       =  load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, '''unet''', unet)
else:
       __UpperCamelCase     :						Dict						       =  unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase     :						Optional[Any]						       =  pipeline.to(unet.device)
__UpperCamelCase       ,__UpperCamelCase     :						List[Any]						       =  generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase     :						int						       =  os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
       image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))





