

from __future__ import annotations

from collections.abc import Callable

A											=     list[list[float | int]]





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(lowercase__	)
	snake_case_      		= [[0 for _ in range(size + 1	)] for _ in range(lowercase__	)]
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42

	for row in range(lowercase__	):
		for col in range(lowercase__	):
			snake_case_      		= matrix[row][col]

		snake_case_      		= vector[row][0]

	snake_case_      		= 0
	snake_case_      		= 0
	while row < size and col < size:
		# pivoting
		snake_case_      		= max((abs(augmented[rowa][col]	), rowa) for rowa in range(lowercase__			, lowercase__	)	)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_		,       snake_case_      		= augmented[pivot_row], augmented[row]

		for rowa in range(row + 1			, lowercase__	):
			snake_case_      		= augmented[rowa][col] / augmented[row][col]
			snake_case_      		= 0
			for cola in range(col + 1			, size + 1	):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1			, lowercase__	):
		for row in range(lowercase__	):
			snake_case_      		= augmented[row][col] / augmented[col][col]
			for cola in range(lowercase__			, size + 1	):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]			, 10	)] for row in range(lowercase__	)
	]





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(lowercase__	)
	snake_case_      		= [[0 for _ in range(lowercase__	)] for _ in range(lowercase__	)]
	snake_case_      		= [[0] for _ in range(lowercase__	)]
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42

	for x_val, y_val in enumerate(lowercase__	):
		for col in range(lowercase__	):
			snake_case_      		= (x_val + 1) ** (size - col - 1)
		snake_case_      		= y_val

	snake_case_      		= solve(lowercase__			, lowercase__	)

	def interpolated_func(lowercase__	) -> int:
		return sum(
		    round(coeffs[x_val][0]	) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase__	)	)

	return interpolated_func





def a(lowercase__	):





	'''simple docstring'''
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)





def a(lowercase__ = question_function			, lowercase__ = 10	):





	'''simple docstring'''
	snake_case_      		= [func(lowercase__	) for x_val in range(1			, order + 1	)]

	snake_case_      		= [
	    interpolate(data_points[:max_coeff]	) for max_coeff in range(1			, order + 1	)
	]

	snake_case_      		= 0
	snake_case_      		= 42
	snake_case_      		= 42

	for poly in polynomials:
		snake_case_      		= 1
		while func(lowercase__	) == poly(lowercase__	):
			x_val += 1

		ret += poly(lowercase__	)

	return ret


if __name__ == "__main__":
		print(f"""{solution() = }""")




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
