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        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			if not len(lowercase_       ) == rows * cols:
						raise ValueError('''The specified number of rows and columns are not correct.'''       )

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

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase="robotic cat with wings",		UpperCAmelCase=7.5,		UpperCAmelCase=50,		UpperCAmelCase=1,		UpperCAmelCase=42,		)->       Any:



			"""simple docstring"""







			lowercase							=		torch.Generator(pipeline.device       ).manual_seed(lowercase_       )
			lowercase							=		pipeline(
			    lowercase_,		guidance_scale=lowercase_,		num_inference_steps=lowercase_,		generator=lowercase_,		num_images_per_prompt=lowercase_,		).images
			lowercase							=		int(math.sqrt(lowercase_       )       )
			lowercase							=		image_grid(lowercase_,		rows=_rows,		cols=num_images_per_prompt // _rows       )
			return grid, images


A_      =      parse_args()
# Load models and create wrapper for stable diffusion
A_      =      CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
A_      =      CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
A_      =      AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
A_      =      UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

A_      =      StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A_      =      lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
						A_      =      load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, "unet", unet)
else:
						A_      =      unet.to(torch.device("cuda", args.cuda_id))
A_      =      pipeline.to(unet.device)
A_    ,    A_      =      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()))))
A_      =      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)))






from __future__ import annotations

from collections.abc import MutableSequence






class      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      MutableSequence[float]   )							->		None:


									'''simple docstring'''

									if len(__lowerCamelCase   ) != degree + 1:
												raise ValueError(
												    '''The number of coefficients should be equal to the degree + 1.'''   )

									lowercase							=		list(__lowerCamelCase   )
									lowercase							=		degree

						def __add__(	self				:      Any							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									if self.degree > polynomial_a.degree:
												lowercase							=		self.coefficients[:]
												for i in range(polynomial_a.degree + 1   ):
															coefficients[i] += polynomial_a.coefficients[i]
												return Polynomial(self.degree							,							__lowerCamelCase   )
									else:
												lowercase							=		polynomial_a.coefficients[:]
												for i in range(self.degree + 1   ):
															coefficients[i] += self.coefficients[i]
												return Polynomial(polynomial_a.degree							,							__lowerCamelCase   )

						def __sub__(	self				:      str							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									return self + polynomial_a * Polynomial(0							,							[-1]   )

						def __neg__(	self				:      str   )							->		Polynomial:


									'''simple docstring'''

									return Polynomial(self.degree							,							[-c for c in self.coefficients]   )

						def __mul__(	self				:      List[str]							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + polynomial_a.degree + 1)
									for i in range(self.degree + 1   ):
												for j in range(polynomial_a.degree + 1   ):
															coefficients[i + j] += (
															    self.coefficients[i] * polynomial_a.coefficients[j]
															)

									return Polynomial(self.degree + polynomial_a.degree							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      int | float   )							->		int | float:


									'''simple docstring'''

									lowercase							=		0
									for i in range(self.degree + 1   ):
												result += self.coefficients[i] * (substitution**i)
									return result

						def __str__(	self				:      str   )							->		str:


									'''simple docstring'''

									lowercase							=		''''''
									for i in range(self.degree							,							-1							,							-1   ):
												if self.coefficients[i] == 0:
															continue
												elif self.coefficients[i] > 0:
															if polynomial:
																		polynomial += " + "
												else:
															polynomial += " - "

												if i == 0:
															polynomial += str(abs(self.coefficients[i]   )   )
												elif i == 1:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x"
												else:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x^" + str(__lowerCamelCase   )

									return polynomial

						def __repr__(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									return self.__str__()

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


									'''simple docstring'''

									lowercase							=		[0] * self.degree
									for i in range(self.degree   ):
												lowercase							=		self.coefficients[i + 1] * (i + 1)
									return Polynomial(self.degree - 1							,							__lowerCamelCase   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int | float = 0   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + 2)
									lowercase							=		constant
									for i in range(self.degree + 1   ):
												lowercase							=		self.coefficients[i] / (i + 1)
									return Polynomial(self.degree + 1							,							__lowerCamelCase   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return False

									if self.degree != polynomial_a.degree:
												return False

									for i in range(self.degree + 1   ):
												if self.coefficients[i] != polynomial_a.coefficients[i]:
															return False

									return True



						def __ne__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									return not self.__eq__(__lowerCamelCase   )





