"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   tempfile.mkdtemp()

							# fmt: off
							lowerCAmelCase__        =   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
							# fmt: on
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

							lowerCAmelCase__        =   {
							    """do_resize""": True,
							    """size""": {"""height""": 18, """width""": 18},
							    """do_normalize""": True,
							    """image_mean""": [0.5, 0.5, 0.5],
							    """image_std""": [0.5, 0.5, 0.5],
							}
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						snake_case__     )
							with open(self.image_processor_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as fp:
									json.dump(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						**snake_case__						:     str     ):
							return BertTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						**snake_case__						:     Dict     ):
							return ViTImageProcessor.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							shutil.rmtree(self.tmpdirname     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta     )]

							lowerCAmelCase__        =   [Image.fromarray(np.moveaxis(snake_case__  ,						0  ,						-1     )     ) for x in image_inputs]

							return image_inputs




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.get_tokenizer()
							lowerCAmelCase__        =   self.get_image_processor()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							processor.save_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

							self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer.get_vocab()     )
							self.assertIsInstance(processor.tokenizer  ,						(BertTokenizer, BertTokenizerFast)     )

							self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor.to_json_string()     )
							self.assertIsInstance(processor.image_processor  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()     )
							processor.save_pretrained(self.tmpdirname     )

							lowerCAmelCase__        =   self.get_tokenizer(bos_token="""(BOS)"""  ,						eos_token="""(EOS)"""     )
							lowerCAmelCase__        =   self.get_image_processor(do_normalize=snake_case__  ,						padding_value=1.0     )

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname  ,						bos_token="""(BOS)"""  ,						eos_token="""(EOS)"""  ,						do_normalize=snake_case__  ,						padding_value=1.0     )

							self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab()     )
							self.assertIsInstance(processor.tokenizer  ,						(BertTokenizer, BertTokenizerFast)     )

							self.assertEqual(processor.image_processor.to_json_string()  ,						image_processor_add_kwargs.to_json_string()     )
							self.assertIsInstance(processor.image_processor  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   image_processor(snake_case__  ,						return_tensors="""np"""     )
							lowerCAmelCase__        =   processor(images=snake_case__  ,						return_tensors="""np"""     )

							for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""

							lowerCAmelCase__        =   processor(text=snake_case__     )

							lowerCAmelCase__        =   tokenizer(snake_case__     )

							for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""
							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   processor(text=snake_case__  ,						images=snake_case__     )

							self.assertListEqual(list(inputs.keys()     )  ,						["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

							# test if it raises when no input is passed
							with self.assertRaises(snake_case__     ):
									processor()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCAmelCase__        =   processor.batch_decode(snake_case__     )
							lowerCAmelCase__        =   tokenizer.batch_decode(snake_case__     )

							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   self.get_image_processor()
							lowerCAmelCase__        =   self.get_tokenizer()

							lowerCAmelCase__        =   VisionTextDualEncoderProcessor(tokenizer=snake_case__  ,						image_processor=snake_case__     )

							lowerCAmelCase__        =   """lower newer"""
							lowerCAmelCase__        =   self.prepare_image_inputs()

							lowerCAmelCase__        =   processor(text=snake_case__  ,						images=snake_case__     )

							self.assertListEqual(list(inputs.keys()     )  ,						processor.model_input_names     )





"""simple docstring"""




from __future__ import annotations

from math import gcd







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = 2			,  lowerCamelCase__ = 1			,  lowerCamelCase__ = 3			,  ):





		"""simple docstring"""




		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(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ) -> int:
				return (pow(lowerCamelCase__			,  2     ) + step) % modulus

		for _ in range(lowerCamelCase__     ):
				# These track the position within the cycle detection logic.
				lowerCAmelCase__        =   seed
				lowerCAmelCase__        =   seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						lowerCAmelCase__        =   rand_fn(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
						lowerCAmelCase__        =   rand_fn(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
						lowerCAmelCase__        =   rand_fn(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

						# 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``.
						lowerCAmelCase__        =   gcd(hare - tortoise			,  lowerCamelCase__     )

						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.
				lowerCAmelCase__        =   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

			__lowerCAmelCase    :							Union[str, Any]													=						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",
			)
			__lowerCAmelCase    :							List[str]													=						parser.parse_args()

			__lowerCAmelCase    :							Dict													=						pollard_rho(args.num, attempts=args.attempts)
			if divisor is None:
						print(F"{args.num} is probably prime")
			else:
						__lowerCAmelCase    :							List[str]													=						args.num // divisor
						print(F"{args.num} = {divisor} * {quotient}")




