





"""simple docstring"""






from __future__ import annotations

from math import gcd

def       __A (     a_  :int				,    a_  :int = 2				,    a_  :int = 1				,    a_  :int = 3				,    )						->						int | None:
				# A value less than 2 can cause an infinite loop in the algorithm.
				if num < 2:
								raise ValueError('''The input value cannot be less than 2''')

				# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
				# algorithm struggles to find factors that are divisible by two.
				# As a workaround, we specifically check for two and even inputs.
				#   See: https://math.stackexchange.com/a/2856214/165820
				if num > 2 and num % 2 == 0:
								return 2

				# Pollard's Rho algorithm requires a function that returns pseudorandom
				# values between 0 <= X < ``num``.  It doesn't need to be random in the
				# sense that the output value is cryptographically secure or difficult
				# to calculate, it only needs to be random in the sense that all output
				# values should be equally likely to appear.
				# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
				# However, the success of Pollard's algorithm isn't guaranteed and is
				# determined in part by the initial seed and the chosen random function.
				# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
				# where ``C`` is a value that we can modify between each attempt.
				def rand_fn(a_  :int				,    a_  :int				,    a_  :int) -> int:
								return (pow(a_				,    2) + step) % modulus

				for _ in range(a_):
								# These track the position within the cycle detection logic.
								__a	:  Any         =					seed
								__a	:  int         =					seed

								while True:
												# At each iteration, the tortoise moves one step and the hare moves two.
												__a	:  Optional[Any]         =					rand_fn(a_				,    a_				,    a_)
												__a	:  int         =					rand_fn(a_				,    a_				,    a_)
												__a	:  List[Any]         =					rand_fn(a_				,    a_				,    a_)

												# At some point both the tortoise and the hare will enter a cycle whose
												# length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
												# the tortoise and hare will end up on the same value modulo ``p``.
												# We can detect when this happens because the position difference between
												# the tortoise and the hare will share a common divisor with ``num``.
												__a	:  int         =					gcd(hare - tortoise				,    a_)

												if divisor == 1:
																# No common divisor yet, just keep searching.
																continue
												else:
																# We found a common divisor!
																if divisor == num:
																				# Unfortunately, the divisor is ``num`` itself and is useless.
																				break
																else:
																				# The divisor is a nontrivial factor of ``num``!
																				return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
								__a	:  Optional[int]         =					hare

								# The new step value for the random function can just be incremented.
								# At first the results will be similar to what the old function would
								# have produced, but the value will quickly diverge after a bit.
								step += 1

				# We haven't found a divisor within the requested number of attempts.
				# We were unlucky or ``num`` itself is actually prime.
				return None


if __name__ == "__main__":
		import argparse

		A	     =					argparse.ArgumentParser()
		parser.add_argument(
		    '''num''',
		    type=int,
		    help='''The value to find a divisor of''',
		)
		parser.add_argument(
		    '''--attempts''',
		    type=int,
		    default=3,
		    help='''The number of attempts before giving up''',
		)
		A	     =					parser.parse_args()

		A	     =					pollard_rho(args.num, attempts=args.attempts)
		if divisor is None:
				print(F'{args.num} is probably prime')
		else:
				A	     =					args.num // divisor
				print(F'{args.num} = {divisor} * {quotient}')






"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
		from PIL import Image




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
					    '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
					    '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
					)
					__lowerCAmelCase					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									requires_backends(self		,     ['''vision''']       )
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )