






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

							if is_vision_available():
														from transformers import MaskaFormerImageProcessor

if is_vision_available():
							from PIL import Image

class 				lowerCAmelCase_	:


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=False ,						_lowerCAmelCase=1_0 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=3_2 * 8 ,						_lowerCAmelCase=3_2 * 8 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=6_4 ,						):
											_lowercase							:   Dict 							=   parent
											_lowercase							:   Optional[Any] 							=   batch_size
											_lowercase							:   Union[str, Any] 							=   is_training
											_lowercase							:   Any 							=   use_auxiliary_loss
											_lowercase							:   Union[str, Any] 							=   num_queries
											_lowercase							:   str 							=   num_channels
											_lowercase							:   Dict 							=   min_size
											_lowercase							:   Dict 							=   max_size
											_lowercase							:   Optional[Any] 							=   num_labels
											_lowercase							:   Optional[Any] 							=   hidden_dim
											_lowercase							:   Dict 							=   hidden_dim


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
											    _lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   torch.ones([self.batch_size, self.min_size, self.max_size] ,						device=_lowerCAmelCase      )

											_lowercase							:   Any 							=   (
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,						device=_lowerCAmelCase      ) > 0.5
											).float()
											_lowercase							:   str 							=   (torch.rand((self.batch_size, self.num_labels) ,						device=_lowerCAmelCase      ) > 0.5).long()

											_lowercase							:   List[Any] 							=   self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels


				def 						__a					(					self      ):
											_lowercase							:   int 							=   MaskaFormerConfig(
											    hidden_size=self.hidden_dim ,						)
											_lowercase							:   List[Any] 							=   self.num_queries
											_lowercase							:   Optional[Any] 							=   self.num_labels

											_lowercase							:   Dict 							=   [1, 1, 1, 1]
											_lowercase							:   Optional[int] 							=   self.num_channels

											_lowercase							:   Optional[int] 							=   6_4
											_lowercase							:   List[Any] 							=   1_2_8
											_lowercase							:   List[Any] 							=   self.hidden_dim
											_lowercase							:   Optional[Any] 							=   self.hidden_dim
											_lowercase							:   List[str] 							=   self.hidden_dim
											return config


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   int 							=   self.prepare_config_and_inputs()
											_lowercase							:   Tuple 							=   {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
											return config, inputs_dict


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Any 							=   output.encoder_hidden_states
											_lowercase							:   Tuple 							=   output.pixel_decoder_hidden_states
											_lowercase							:   Any 							=   output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(_lowerCAmelCase      ) ,						len(config.backbone_config.depths      )      )
											self.parent.assertTrue(len(_lowerCAmelCase      ) ,						len(config.backbone_config.depths      )      )
											self.parent.assertTrue(len(_lowerCAmelCase      ) ,						config.decoder_layers      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=False      ):
											with torch.no_grad():
																		_lowercase							:   Dict 							=   MaskaFormerModel(config=_lowerCAmelCase      )
																		model.to(_lowerCAmelCase      )
																		model.eval()

																		_lowercase							:   List[Any] 							=   model(pixel_values=_lowerCAmelCase ,						pixel_mask=_lowerCAmelCase      )
																		_lowercase							:   Tuple 							=   model(_lowerCAmelCase ,						output_hidden_states=_lowerCAmelCase      )

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape ,						(self.batch_size, self.num_queries, self.hidden_dim) ,						)
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
											self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

											if output_hidden_states:
																		self.check_output_hidden_state(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Tuple 							=   MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()

											def comm_check_on_output(_lowerCAmelCase      ):
																		# let's still check that all the required stuff is there
																		self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None      )
																		self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None      )
																		self.parent.assertTrue(result.encoder_last_hidden_state is not None      )
																		# okay, now we need to check the logits shape
																		# due to the encoder compression, masks have a //4 spatial size
																		self.parent.assertEqual(
																		    result.masks_queries_logits.shape ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,						)
																		# + 1 for null class
																		self.parent.assertEqual(
																		    result.class_queries_logits.shape ,						(self.batch_size, self.num_queries, self.num_labels + 1)      )

											with torch.no_grad():
																		_lowercase							:   Dict 							=   model(pixel_values=_lowerCAmelCase ,						pixel_mask=_lowerCAmelCase      )
																		_lowercase							:   List[Any] 							=   model(_lowerCAmelCase      )

																		comm_check_on_output(_lowerCAmelCase      )

																		_lowercase							:   Dict 							=   model(
																		    pixel_values=_lowerCAmelCase ,						pixel_mask=_lowerCAmelCase ,						mask_labels=_lowerCAmelCase ,						class_labels=_lowerCAmelCase      )

											comm_check_on_output(_lowerCAmelCase      )

											self.parent.assertTrue(result.loss is not None      )
											self.parent.assertEqual(result.loss.shape ,						torch.Size([1]      )      )




@require_torch
class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[str]							       = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				_UpperCamelCase					:       Optional[Any]							       = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

				_UpperCamelCase					:       List[Any]							       = False
				_UpperCamelCase					:       Optional[int]							       = False
				_UpperCamelCase					:       Optional[int]							       = False
				_UpperCamelCase					:       Union[str, Any]							       = False


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   MaskaFormerModelTester(self      )
											_lowercase							:   Union[str, Any] 							=   ConfigTester(self ,						config_class=_lowerCAmelCase ,						has_text_modality=_lowerCAmelCase      )


				def 						__a					(					self      ):
											self.config_tester.run_common_tests()


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Dict 							=   self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase ,						**_lowerCAmelCase ,						output_hidden_states=_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCAmelCase      )


				@unittest.skip(reason='Mask2Former does not use inputs_embeds'      )
				def 						__a					(					self      ):
											pass


				@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'      )
				def 						__a					(					self      ):
											pass


				@unittest.skip(reason='Mask2Former is not a generative model'      )
				def 						__a					(					self      ):
											pass


				@unittest.skip(reason='Mask2Former does not use token embeddings'      )
				def 						__a					(					self      ):
											pass


				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
				def 						__a					(					self      ):
											pass


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


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Optional[Any] 							=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_lowercase							:   Tuple 							=   model_class(_lowerCAmelCase      )
																		_lowercase							:   str 							=   inspect.signature(model.forward      )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		_lowercase							:   List[Any] 							=   [*signature.parameters.keys()]

																		_lowercase							:   Any 							=   ['pixel_values']
																		self.assertListEqual(arg_names[:1] ,						_lowerCAmelCase      )


				@slow
				def 						__a					(					self      ):
											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																		_lowercase							:   List[Any] 							=   MaskaFormerModel.from_pretrained(_lowerCAmelCase      )
																		self.assertIsNotNone(_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   str 							=   (self.model_tester.min_size,) * 2
											_lowercase							:   int 							=   {
											    'pixel_values': torch.randn((2, 3, *size) ,						device=_lowerCAmelCase      ),
											    'mask_labels': torch.randn((2, 1_0, *size) ,						device=_lowerCAmelCase      ),
											    'class_labels': torch.zeros(2 ,						1_0 ,						device=_lowerCAmelCase      ).long(),
											}
											_lowercase							:   List[str] 							=   self.model_tester.get_config()

											_lowercase							:   List[Any] 							=   MaskaFormerForUniversalSegmentation(_lowerCAmelCase      ).to(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   model(**_lowerCAmelCase      )
											self.assertTrue(outputs.loss is not None      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase ,						**_lowerCAmelCase ,						output_hidden_states=_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Optional[int] 							=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		_lowercase							:   Union[str, Any] 							=   model_class(_lowerCAmelCase      ).to(_lowerCAmelCase      )
																		_lowercase							:   int 							=   model(**_lowerCAmelCase ,						output_attentions=_lowerCAmelCase      )
																		self.assertTrue(outputs.attentions is not None      )


				def 						__a					(					self      ):
											if not self.model_tester.is_training:
																		return

											_lowercase							:   Dict 							=   self.all_model_classes[1]
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   List[Any] 							=   self.model_tester.prepare_config_and_inputs()

											_lowercase							:   Optional[int] 							=   model_class(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.train()

											_lowercase							:   List[str] 							=   model(_lowerCAmelCase ,						mask_labels=_lowerCAmelCase ,						class_labels=_lowerCAmelCase      ).loss
											loss.backward()


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.all_model_classes[1]
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   List[str] 							=   self.model_tester.prepare_config_and_inputs()
											_lowercase							:   Dict 							=   True
											_lowercase							:   Optional[int] 							=   True

											_lowercase							:   Tuple 							=   model_class(_lowerCAmelCase      ).to(_lowerCAmelCase      )
											model.train()

											_lowercase							:   List[str] 							=   model(_lowerCAmelCase ,						mask_labels=_lowerCAmelCase ,						class_labels=_lowerCAmelCase      )

											_lowercase							:   int 							=   outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											_lowercase							:   List[Any] 							=   outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											_lowercase							:   int 							=   outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											_lowercase							:   Tuple 							=   outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=_lowerCAmelCase      )

											self.assertIsNotNone(encoder_hidden_states.grad      )
											self.assertIsNotNone(pixel_decoder_hidden_states.grad      )
											self.assertIsNotNone(transformer_decoder_hidden_states.grad      )
											self.assertIsNotNone(attentions.grad      )




UpperCamelCase								=		1E-4






def    __magic_name__    (	)     ->    Optional[int]:
							_lowercase							:   Tuple 							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							return image






@require_vision
@slow
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@cached_property
				def 						__a					(					self      ):
											return "facebook/mask2former-swin-small-coco-instance"


				@cached_property
				def 						__a					(					self      ):
											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(_lowerCAmelCase      )
											_lowercase							:   Optional[Any] 							=   self.default_image_processor
											_lowercase							:   List[str] 							=   prepare_img()
											_lowercase							:   List[str] 							=   image_processor(_lowerCAmelCase ,						return_tensors='pt'      ).to(_lowerCAmelCase      )
											_lowercase							:   int 							=   inputs['pixel_values'].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0      )
											# check size
											self.assertEqual(_lowerCAmelCase ,						(1, 3, 3_8_4, 3_8_4)      )

											with torch.no_grad():
																		_lowercase							:   Tuple 							=   model(**_lowerCAmelCase      )

											_lowercase							:   Tuple 							=   torch.tensor(
											    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]      ).to(_lowerCAmelCase      )
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3] ,						_lowerCAmelCase ,						atol=_lowerCAmelCase      )      )

											_lowercase							:   List[Any] 							=   torch.tensor(
											    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]      ).to(_lowerCAmelCase      )
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,						_lowerCAmelCase ,						atol=_lowerCAmelCase      )      )

											_lowercase							:   Dict 							=   torch.tensor(
											    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]      ).to(_lowerCAmelCase      )
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,						_lowerCAmelCase ,						atol=_lowerCAmelCase      )      )


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(_lowerCAmelCase      ).eval()
											_lowercase							:   Union[str, Any] 							=   self.default_image_processor
											_lowercase							:   Dict 							=   prepare_img()
											_lowercase							:   Dict 							=   image_processor(_lowerCAmelCase ,						return_tensors='pt'      ).to(_lowerCAmelCase      )
											_lowercase							:   Dict 							=   inputs['pixel_values'].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0      )
											# check size
											self.assertEqual(_lowerCAmelCase ,						(1, 3, 3_8_4, 3_8_4)      )

											with torch.no_grad():
																		_lowercase							:   List[str] 							=   model(**_lowerCAmelCase      )
											# masks_queries_logits
											_lowercase							:   Tuple 							=   outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape ,						(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
											_lowercase							:   Optional[Any] 							=   [
											    [-8.78_39, -9.00_56, -8.81_21],
											    [-7.41_04, -7.03_13, -6.54_01],
											    [-6.61_05, -6.34_27, -6.46_75],
											]
											_lowercase							:   Optional[int] 							=   torch.tensor(_lowerCAmelCase      ).to(_lowerCAmelCase      )
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,						_lowerCAmelCase ,						atol=_lowerCAmelCase      )      )
											# class_queries_logits
											_lowercase							:   int 							=   outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape ,						(1, model.config.num_queries, model.config.num_labels + 1)      )
											_lowercase							:   List[Any] 							=   torch.tensor(
											    [
											        [1.83_24, -8.08_35, -4.19_22],
											        [0.84_50, -9.00_50, -3.60_53],
											        [0.30_45, -7.72_93, -3.02_75],
											    ]      ).to(_lowerCAmelCase      )
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,						_lowerCAmelCase ,						atol=_lowerCAmelCase      )      )


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(_lowerCAmelCase      ).eval()
											_lowercase							:   List[str] 							=   self.default_image_processor

											_lowercase							:   List[Any] 							=   image_processor(
											    [np.zeros((3, 8_0_0, 1_3_3_3)      ), np.zeros((3, 8_0_0, 1_3_3_3)      )] ,						segmentation_maps=[np.zeros((3_8_4, 3_8_4)      ).astype(np.floataa      ), np.zeros((3_8_4, 3_8_4)      ).astype(np.floataa      )] ,						return_tensors='pt' ,						)

											_lowercase							:   Any 							=   inputs['pixel_values'].to(_lowerCAmelCase      )
											_lowercase							:   Optional[Any] 							=   [el.to(_lowerCAmelCase      ) for el in inputs['mask_labels']]
											_lowercase							:   List[Any] 							=   [el.to(_lowerCAmelCase      ) for el in inputs['class_labels']]

											with torch.no_grad():
																		_lowercase							:   Optional[int] 							=   model(**_lowerCAmelCase      )

											self.assertTrue(outputs.loss is not None      )









from math import sqrt

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (
          number >= 0
      ), "'number' must been an int and positive"

      lowercase__  =	True

      # 0 and 1 are none primes.
      if number <= 1:
            lowercase__  =	False

      for divisor in range(2    ,	int(round(sqrt(A__						)						)						) + 1						):
            # if 'number' divisible by 'divisor' then sets 'status'
            # of false and break up the loop.
            if number % divisor == 0:
                  lowercase__  =	False
                  break

    # precondition
      assert isinstance(A__    ,	A__						), "'status' must been from type bool"

      return status

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n > 2), "'N' must been an int and > 2"

      # beginList: contains all natural numbers from 2 up to N
      lowercase__  =	list(range(2    ,	n + 1						)						)

      lowercase__  =	[]  # this list will be returns.

      # actual sieve of erathostenes
      for i in range(len(A__						)						):
            for j in range(i + 1    ,	len(A__						)						):
                  if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                        lowercase__  =	0

    # filters actual prime numbers.
      lowercase__  =	[x for x in begin_list if x != 0]

      # precondition
      assert isinstance(A__    ,	A__						), "'ans' must been from type list"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n > 2), "'N' must been an int and > 2"

      lowercase__  =	[]

      # iterates over all numbers between 2 up to N+1
      # if a number is prime then appends to list 'ans'
      for number in range(2    ,	n + 1						):
            if is_prime(A__						):
                  ans.append(A__						)

    # precondition
      assert isinstance(A__    ,	A__						), "'ans' must been from type list"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and number >= 0, "'number' must been an int and >= 0"

      lowercase__  =	[]  # this list will be returns of the function.

      # potential prime number factors.

      lowercase__  =	2

      lowercase__  =	number

      if number == 0 or number == 1:
            ans.append(A__						)

      # if 'number' not prime then builds the prime factorization of 'number'
      elif not is_prime(A__						):
            while quotient != 1:
                  if is_prime(A__						) and (quotient % factor == 0):
                        ans.append(A__						)
                        quotient /= factor
                  else:
                        factor += 1

      else:
            ans.append(A__						)

      # precondition
      assert isinstance(A__    ,	A__						), "'ans' must been from type list"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (
          number >= 0
      ), "'number' bust been an int and >= 0"

      lowercase__  =	0

      # prime factorization of 'number'
      lowercase__  =	prime_factorization(A__						)

      lowercase__  =	max(A__						)

      # precondition
      assert isinstance(A__    ,	A__						), "'ans' must been from type int"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (
          number >= 0
      ), "'number' bust been an int and >= 0"

      lowercase__  =	0

      # prime factorization of 'number'
      lowercase__  =	prime_factorization(A__						)

      lowercase__  =	min(A__						)

      # precondition
      assert isinstance(A__    ,	A__						), "'ans' must been from type int"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						), "'number' must been an int"
      assert isinstance(number % 2 == 0    ,	A__						), "compare bust been from type bool"

      return number % 2 == 0

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						), "'number' must been an int"
      assert isinstance(number % 2 != 0    ,	A__						), "compare bust been from type bool"

      return number % 2 != 0

def 	_lowerCAmelCase (    A__						):
      assert (
          isinstance(A__    ,	A__						) and (number > 2) and is_even(A__						)
      ), "'number' must been an int, even and > 2"

      lowercase__  =	[]  # this list will returned

      # creates a list of prime numbers between 2 up to 'number'
      lowercase__  =	get_prime_numbers(A__						)
      lowercase__  =	len(A__						)

      # run variable for while-loops.
      lowercase__  =	0
      lowercase__  =	None

      # exit variable. for break up the loops
      lowercase__  =	True

      while i < len_pn and loop:
            lowercase__  =	i + 1

            while j < len_pn and loop:
                  if prime_numbers[i] + prime_numbers[j] == number:
                        lowercase__  =	False
                        ans.append(prime_numbers[i]						)
                        ans.append(prime_numbers[j]						)

                  j += 1

            i += 1

      # precondition
      assert (
          isinstance(A__    ,	A__						)
          and (len(A__						) == 2)
          and (ans[0] + ans[1] == number)
          and is_prime(ans[0]						)
          and is_prime(ans[1]						)
      ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

      return ans

def 	_lowerCAmelCase (    A__    ,	A__						):
      assert (
          isinstance(A__    ,	A__						)
          and isinstance(A__    ,	A__						)
          and (numbera >= 0)
          and (numbera >= 0)
      ), "'number1' and 'number2' must been positive integer."

      lowercase__  =	0

      while numbera != 0:
            lowercase__  =	numbera % numbera
            lowercase__  =	numbera
            lowercase__  =	rest

      # precondition
      assert isinstance(A__    ,	A__						) and (
          numbera >= 0
      ), "'number' must been from type int and positive"

      return numbera

def 	_lowerCAmelCase (    A__    ,	A__						):
      assert (
          isinstance(A__    ,	A__						)
          and isinstance(A__    ,	A__						)
          and (numbera >= 1)
          and (numbera >= 1)
      ), "'number1' and 'number2' must been positive integer."

      lowercase__  =	1  # actual answer that will be return.

      # for kgV (x,1)
      if numbera > 1 and numbera > 1:
            # builds the prime factorization of 'number1' and 'number2'
            lowercase__  =	prime_factorization(A__						)
            lowercase__  =	prime_factorization(A__						)

      elif numbera == 1 or numbera == 1:
            lowercase__  =	[]
            lowercase__  =	[]
            lowercase__  =	max(A__    ,	A__						)

      lowercase__  =	0
      lowercase__  =	0

      lowercase__  =	[]  # captured numbers int both 'primeFac1' and 'primeFac2'

      # iterates through primeFac1
      for n in prime_fac_a:
            if n not in done:
                  if n in prime_fac_a:
                        lowercase__  =	prime_fac_a.count(A__						)
                        lowercase__  =	prime_fac_a.count(A__						)

                        for _ in range(max(A__    ,	A__						)						):
                              ans *= n

                  else:
                        lowercase__  =	prime_fac_a.count(A__						)

                        for _ in range(A__						):
                              ans *= n

                  done.append(A__						)

    # iterates through primeFac2
      for n in prime_fac_a:
            if n not in done:
                  lowercase__  =	prime_fac_a.count(A__						)

                  for _ in range(A__						):
                        ans *= n

                  done.append(A__						)

    # precondition
      assert isinstance(A__    ,	A__						) and (
          ans >= 0
      ), "'ans' must been from type int and positive"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n >= 0), "'number' must been a positive int"

      lowercase__  =	0
      lowercase__  =	2  # this variable holds the answer

      while index < n:
            index += 1

            ans += 1  # counts to the next number

            # if ans not prime then
            # runs to the next prime number.
            while not is_prime(A__						):
                  ans += 1

    # precondition
      assert isinstance(A__    ,	A__						) and is_prime(
          A__						), "'ans' must been a prime number and from type int"

      return ans

def 	_lowerCAmelCase (    A__    ,	A__						):
      assert (
          is_prime(A__						) and is_prime(A__						) and (p_number_a < p_number_a)
      ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

      lowercase__  =	p_number_a + 1  # jump to the next number

      lowercase__  =	[]  # this list will be returns.

      # if number is not prime then
      # fetch the next prime number.
      while not is_prime(A__						):
            number += 1

      while number < p_number_a:
            ans.append(A__						)

            number += 1

            # fetch the next prime number.
            while not is_prime(A__						):
                  number += 1

    # precondition
      assert (
          isinstance(A__    ,	A__						)
          and ans[0] != p_number_a
          and ans[len(A__						) - 1] != p_number_a
      ), "'ans' must been a list without the arguments"

      # 'ans' contains not 'pNumber1' and 'pNumber2' !
      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n >= 1), "'n' must been int and >= 1"

      lowercase__  =	[]  # will be returned.

      for divisor in range(1    ,	n + 1						):
            if n % divisor == 0:
                  ans.append(A__						)

    # precondition
      assert ans[0] == 1 and ans[len(A__						) - 1] == n, "Error in function getDivisiors(...)"

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (
          number > 1
      ), "'number' must been an int and >= 1"

      lowercase__  =	get_divisors(A__						)

      # precondition
      assert (
          isinstance(A__    ,	A__						)
          and (divisors[0] == 1)
          and (divisors[len(A__						) - 1] == number)
      ), "Error in help-function getDivisiors(...)"

      # summed all divisors up to 'number' (exclusive), hence [:-1]
      return sum(divisors[:-1]						) == number

def 	_lowerCAmelCase (    A__    ,	A__						):
      assert (
          isinstance(A__    ,	A__						)
          and isinstance(A__    ,	A__						)
          and (denominator != 0)
      ), "The arguments must been from type int and 'denominator' != 0"

      # build the greatest common divisor of numerator and denominator.
      lowercase__  =	gcd(abs(A__						)    ,	abs(A__						)						)

      # precondition
      assert (
          isinstance(A__    ,	A__						)
          and (numerator % gcd_of_fraction == 0)
          and (denominator % gcd_of_fraction == 0)
      ), "Error in function gcd(...,...)"

      return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n >= 0), "'n' must been a int and >= 0"

      lowercase__  =	1  # this will be return.

      for factor in range(1    ,	n + 1						):
            ans *= factor

      return ans

def 	_lowerCAmelCase (    A__						):
      assert isinstance(A__    ,	A__						) and (n >= 0), "'n' must been an int and >= 0"

      lowercase__  =	0
      lowercase__  =	1
      lowercase__  =	1  # this will be return

      for _ in range(n - 1						):
            lowercase__  =	ans
            ans += fiba
            lowercase__  =	tmp

      return ans
