



'''simple docstring'''

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor

class       __magic_name__		(       _UpperCamelCase  ):







							def 				__lowercase     (				self   :			Dict						):
									_a	: int								=				self.config_class(**self.inputs_dict						)
									self.parent.assertTrue(hasattr(_UpperCAmelCase      ,'hidden_sizes'						)						)
									self.parent.assertTrue(hasattr(_UpperCAmelCase      ,'num_attention_heads'						)						)

class       __magic_name__		:







							def __init__(				self   :			Dict      ,_UpperCAmelCase   :			Optional[int]      ,_UpperCAmelCase   :			Optional[int]=13      ,_UpperCAmelCase   :			Dict=64      ,_UpperCAmelCase   :			List[Any]=3      ,_UpperCAmelCase   :			Optional[int]=3      ,_UpperCAmelCase   :			Tuple=2      ,_UpperCAmelCase   :			List[Any]=1      ,_UpperCAmelCase   :			Tuple=16      ,_UpperCAmelCase   :			List[str]=[128, 256, 384]      ,_UpperCAmelCase   :			str=[4, 6, 8]      ,_UpperCAmelCase   :			str=[2, 3, 4]      ,_UpperCAmelCase   :			Any=[16, 16, 16]      ,_UpperCAmelCase   :			Dict=0      ,_UpperCAmelCase   :			Tuple=[2, 2, 2]      ,_UpperCAmelCase   :			List[Any]=[2, 2, 2]      ,_UpperCAmelCase   :			List[Any]=0.02      ,_UpperCAmelCase   :			List[str]=True      ,_UpperCAmelCase   :			List[str]=True      ,_UpperCAmelCase   :			Dict=2      ,):
									_a	: Any								=				parent
									_a	: Dict								=				batch_size
									_a	: Dict								=				image_size
									_a	: Any								=				num_channels
									_a	: Any								=				kernel_size
									_a	: Tuple								=				stride
									_a	: str								=				padding
									_a	: List[str]								=				hidden_sizes
									_a	: Optional[Any]								=				num_attention_heads
									_a	: Optional[Any]								=				depths
									_a	: Optional[int]								=				key_dim
									_a	: Tuple								=				drop_path_rate
									_a	: List[Any]								=				patch_size
									_a	: int								=				attention_ratio
									_a	: int								=				mlp_ratio
									_a	: List[str]								=				initializer_range
									_a	: Dict								=				[
									    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
									    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
									]
									_a	: List[Any]								=				is_training
									_a	: int								=				use_labels
									_a	: Union[str, Any]								=				num_labels
									_a	: str								=				initializer_range







							def 				__lowercase     (				self   :			Dict						):
									_a	: List[Any]								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_a	: Union[str, Any]								=				None
									if self.use_labels:
											_a	: Any								=				ids_tensor([self.batch_size]      ,self.num_labels						)

									_a	: Dict								=				self.get_config()
									return config, pixel_values, labels







							def 				__lowercase     (				self   :			List[Any]						):
									return LevitConfig(
									    image_size=self.image_size      ,num_channels=self.num_channels      ,kernel_size=self.kernel_size      ,stride=self.stride      ,padding=self.padding      ,patch_size=self.patch_size      ,hidden_sizes=self.hidden_sizes      ,num_attention_heads=self.num_attention_heads      ,depths=self.depths      ,key_dim=self.key_dim      ,drop_path_rate=self.drop_path_rate      ,mlp_ratio=self.mlp_ratio      ,attention_ratio=self.attention_ratio      ,initializer_range=self.initializer_range      ,down_ops=self.down_ops      ,)







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			Tuple      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[Any]						):
									_a	: Optional[Any]								=				LevitModel(config=_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									_a	: int								=				model(_UpperCAmelCase						)
									_a	: List[str]								=				(self.image_size, self.image_size)
									_a			,   _a	: Dict								=				image_size[0], image_size[1]
									for _ in range(4						):
											_a	: Optional[Any]								=				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
											_a	: Any								=				floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
									self.parent.assertEqual(
									    result.last_hidden_state.shape      ,(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])      ,)







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Optional[Any]      ,_UpperCAmelCase   :			List[Any]						):
									_a	: Tuple								=				self.num_labels
									_a	: List[str]								=				LevitForImageClassification(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									_a	: int								=				model(_UpperCAmelCase      ,labels=_UpperCAmelCase						)
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)						)







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Tuple								=				self.prepare_config_and_inputs()
									_a			,   _a			,   _a	: List[str]								=				config_and_inputs
									_a	: int								=				{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       str  							=   (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase			:       Tuple  							=   (
							    {
							        'feature-extraction': LevitModel,
							        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase			:       Dict  							=   False
							lowerCAmelCase			:       str  							=   False
							lowerCAmelCase			:       Optional[Any]  							=   False
							lowerCAmelCase			:       Union[str, Any]  							=   False
							lowerCAmelCase			:       List[str]  							=   False







							def 				__lowercase     (				self   :			Any						):
									_a	: List[Any]								=				LevitModelTester(self						)
									_a	: List[str]								=				ConfigTester(self      ,config_class=_UpperCAmelCase      ,has_text_modality=_UpperCAmelCase      ,hidden_size=37						)







							def 				__lowercase     (				self   :			List[Any]						):
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()







							def 				__lowercase     (				self   :			Any						):
									return







							@unittest.skip(reason='Levit does not use inputs_embeds'						)
							def 				__lowercase     (				self   :			Optional[int]						):
									pass







							@unittest.skip(reason='Levit does not support input and output embeddings'						)
							def 				__lowercase     (				self   :			List[Any]						):
									pass







							@unittest.skip(reason='Levit does not output attentions'						)
							def 				__lowercase     (				self   :			str						):
									pass







							def 				__lowercase     (				self   :			Dict						):
									_a			,   _a	: Optional[int]								=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a	: Union[str, Any]								=				model_class(_UpperCAmelCase						)
											_a	: Dict								=				inspect.signature(model.forward						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a	: List[str]								=				[*signature.parameters.keys()]

											_a	: str								=				['pixel_values']
											self.assertListEqual(arg_names[:1]      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									def check_hidden_states_output(_UpperCAmelCase   :			Optional[Any]      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]						):
											_a	: Any								=				model_class(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()

											with torch.no_grad():
													_a	: Optional[int]								=				model(**self._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase						)						)

											_a	: Tuple								=				outputs.hidden_states

											_a	: List[str]								=				len(self.model_tester.depths						) + 1
											self.assertEqual(len(_UpperCAmelCase						)      ,_UpperCAmelCase						)

											_a	: List[str]								=				(self.model_tester.image_size, self.model_tester.image_size)
											_a			,   _a	: Any								=				image_size[0], image_size[1]
											for _ in range(4						):
													_a	: Union[str, Any]								=				floor(
													    (
													        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
													_a	: Union[str, Any]								=				floor(
													    (
													        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)      ,[
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]      ,)

									_a			,   _a	: Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a	: Union[str, Any]								=				True
											check_hidden_states_output(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_a	: int								=				True

											check_hidden_states_output(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def 				__lowercase     (				self   :			Optional[int]						):
									pass







							def 				__lowercase     (				self   :			Dict      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[int]=False						):
									_a	: Dict								=				super()._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase      ,return_labels=_UpperCAmelCase						)

									if return_labels:
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													del inputs_dict["labels"]

									return inputs_dict







							def 				__lowercase     (				self   :			str						):
									_a	: str								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Tuple								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Any						):
									if not self.model_tester.is_training:
											return

									_a			,   _a	: Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
									_a	: Optional[int]								=				True

									for model_class in self.all_model_classes:
											# LevitForImageClassificationWithTeacher supports inference-only
											if (
											    model_class in get_values(_UpperCAmelCase						)
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
													continue
											_a	: int								=				model_class(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.train()
											_a	: int								=				self._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase      ,return_labels=_UpperCAmelCase						)
											_a	: List[str]								=				model(**_UpperCAmelCase						).loss
											loss.backward()







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a			,   _a	: Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
											return

									_a	: List[Any]								=				False
									_a	: List[str]								=				True

									for model_class in self.all_model_classes:
											if model_class in get_values(_UpperCAmelCase						) or not model_class.supports_gradient_checkpointing:
													continue
											# LevitForImageClassificationWithTeacher supports inference-only
											if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													continue
											_a	: List[Any]								=				model_class(_UpperCAmelCase						)
											model.gradient_checkpointing_enable()
											model.to(_UpperCAmelCase						)
											model.train()
											_a	: str								=				self._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase      ,return_labels=_UpperCAmelCase						)
											_a	: List[Any]								=				model(**_UpperCAmelCase						).loss
											loss.backward()







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a			,   _a	: Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()

									_a	: List[str]								=				[
									    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
									    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
									    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
									]

									for model_class in self.all_model_classes:
											if (
											    model_class
											    not in [
											        *get_values(_UpperCAmelCase						),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
													continue

											for problem_type in problem_types:
													with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""						):
															_a	: List[Any]								=				problem_type['title']
															_a	: Optional[Any]								=				problem_type['num_labels']

															_a	: Optional[Any]								=				model_class(_UpperCAmelCase						)
															model.to(_UpperCAmelCase						)
															model.train()

															_a	: Optional[int]								=				self._prepare_for_class(_UpperCAmelCase      ,_UpperCAmelCase      ,return_labels=_UpperCAmelCase						)

															if problem_type["num_labels"] > 1:
																	_a	: Any								=				inputs['labels'].unsqueeze(1						).repeat(1      ,problem_type['num_labels']						)

															_a	: List[str]								=				inputs['labels'].to(problem_type['dtype']						)

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=_UpperCAmelCase						) as warning_list:
																	_a	: Optional[Any]								=				model(**_UpperCAmelCase						).loss
															for w in warning_list:
																	if "Using a target size that is different to the input size" in str(w.message						):
																			raise ValueError(
																			    F"""Something is going wrong in the regression problem: intercepted {w.message}"""						)

															loss.backward()







							@slow
							def 				__lowercase     (				self   :			Optional[int]						):
									for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a	: Tuple								=				LevitModel.from_pretrained(_UpperCAmelCase						)
											self.assertIsNotNone(_UpperCAmelCase						)



def 	__lowerCamelCase	( )     -> Optional[int]:
		_a	: Union[str, Any]								=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
		return image

@require_torch
@require_vision
class       __magic_name__		(       unittest.TestCase  ):







							@cached_property
							def 				__lowercase     (				self   :			List[str]						):
									return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)







							@slow
							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Any								=				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
									    _UpperCAmelCase						)

									_a	: int								=				self.default_image_processor
									_a	: List[Any]								=				prepare_img()
									_a	: Optional[int]								=				image_processor(images=_UpperCAmelCase      ,return_tensors='pt'						).to(_UpperCAmelCase						)

									# forward pass
									with torch.no_grad():
											_a	: Any								=				model(**_UpperCAmelCase						)

									# verify the logits
									_a	: Optional[Any]								=				torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape      ,_UpperCAmelCase						)

									_a	: List[Any]								=				torch.tensor([1.04_48, -0.37_45, -1.83_17]						).to(_UpperCAmelCase						)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)









'''simple docstring'''

from __future__ import annotations

from math import gcd



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 2		,     lowerCAmelCase_ = 1		,     lowerCAmelCase_ = 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(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    ) -> int:
				return (pow(lowerCAmelCase_		,     2    ) + step) % modulus

		for _ in range(lowerCAmelCase_    ):
				# These track the position within the cycle detection logic.
				_a	: Optional[int]								=				seed
				_a	: str								=				seed

				while True:
						# At each iteration, the tortoise moves one step and the hare moves two.
						_a	: str								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Dict								=				rand_fn(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )
						_a	: Tuple								=				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``.
						_a	: Optional[Any]								=				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.
				_a	: Union[str, Any]								=				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		     =  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		     =  parser.parse_args()

							__lowerCAmelCase		     =  pollard_rho(args.num, attempts=args.attempts)
							if divisor is None:
														print(f"""{args.num} is probably prime""")
							else:
														__lowerCAmelCase		     =  args.num // divisor
														print(f"""{args.num} = {divisor} * {quotient}""")




