


"""simple docstring"""
import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

			if is_vision_available():
						from transformers import MaskFormerImageProcessor

if is_vision_available():
			from PIL import Image



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=2				,       a_=True				,       a_=False				,       a_=10				,       a_=3				,       a_=32 * 4				,       a_=32 * 6				,       a_=4				,       a_=32				,       ):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				parent
			__snake_case	:						List[Any]						=				batch_size
			__snake_case	:						Optional[Any]						=				is_training
			__snake_case	:						int						=				use_auxiliary_loss
			__snake_case	:						Dict						=				num_queries
			__snake_case	:						Optional[Any]						=				num_channels
			__snake_case	:						Any						=				min_size
			__snake_case	:						Union[str, Any]						=				max_size
			__snake_case	:						int						=				num_labels
			__snake_case	:						Tuple						=				mask_feature_size



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
			    a_					)

			__snake_case	:						str						=				torch.ones([self.batch_size, self.min_size, self.max_size]				,       device=a_					)

			__snake_case	:						List[Any]						=				(
			    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]				,       device=a_					) > 0.5
			).float()
			__snake_case	:						Any						=				(torch.rand((self.batch_size, self.num_labels)				,       device=a_					) > 0.5).long()

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return MaskFormerConfig.from_backbone_and_decoder_configs(
			    backbone_config=SwinConfig(
			        depths=[1, 1, 1, 1]				,       )				,       decoder_config=DetrConfig(
			        decoder_ffn_dim=1_28				,       num_queries=self.num_queries				,       decoder_attention_heads=2				,       d_model=self.mask_feature_size				,       )				,       mask_feature_size=self.mask_feature_size				,       fpn_feature_size=self.mask_feature_size				,       num_channels=self.num_channels				,       num_labels=self.num_labels				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						Tuple						=				self.prepare_config_and_inputs()
			__snake_case	:						Dict						=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
			return config, inputs_dict



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				output.encoder_hidden_states
			__snake_case	:						List[str]						=				output.pixel_decoder_hidden_states
			__snake_case	:						int						=				output.transformer_decoder_hidden_states

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_=False					):





			'''simple docstring'''

			with torch.no_grad():
				__snake_case	:						Optional[int]						=				MaskFormerModel(config=a_					)
				model.to(a_					)
				model.eval()

				__snake_case	:						Optional[Any]						=				model(pixel_values=a_				,       pixel_mask=a_					)
				__snake_case	:						int						=				model(a_				,       output_hidden_states=a_					)
			# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
			# encoder and pixel decoder
			self.parent.assertEqual(
			    output.transformer_decoder_last_hidden_state.shape				,       (self.batch_size, self.num_queries, self.mask_feature_size)				,       )
			# 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(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				MaskFormerForInstanceSegmentation(config=a_					)
			model.to(a_					)
			model.eval()

			def comm_check_on_output(a_					):
				# 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():
				__snake_case	:						Optional[Any]						=				model(pixel_values=a_				,       pixel_mask=a_					)
				__snake_case	:						Optional[int]						=				model(a_					)

				comm_check_on_output(a_					)

				__snake_case	:						List[str]						=				model(
				    pixel_values=a_				,       pixel_mask=a_				,       mask_labels=a_				,       class_labels=a_					)

			comm_check_on_output(a_					)

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



@require_torch
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
		lowerCamelCase__				=(
		    {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				MaskFormerModelTester(self					)
			__snake_case	:						int						=				ConfigTester(self				,       config_class=a_				,       has_text_modality=a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.create_and_check_maskformer_model(a_				,       **a_				,       output_hidden_states=a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*a_					)



		@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@unittest.skip(reason='''MaskFormer is not a generative model'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@unittest.skip(reason='''MaskFormer does not use token embeddings'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



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





			'''simple docstring'''

			pass



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





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs_for_common()

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

				__snake_case	:						str						=				['''pixel_values''']
				self.assertListEqual(arg_names[:1]				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in ["facebook/maskformer-swin-small-coco"]:
				__snake_case	:						Optional[Any]						=				MaskFormerModel.from_pretrained(a_					)
				self.assertIsNotNone(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				(self.model_tester.min_size,) * 2
			__snake_case	:						List[str]						=				{
			    '''pixel_values''': torch.randn((2, 3, *size)				,       device=a_					),
			    '''mask_labels''': torch.randn((2, 10, *size)				,       device=a_					),
			    '''class_labels''': torch.zeros(2				,       10				,       device=a_					).long(),
			}

			__snake_case	:						List[Any]						=				MaskFormerForInstanceSegmentation(MaskFormerConfig()					).to(a_					)
			__snake_case	:						Optional[Any]						=				model(**a_					)
			self.assertTrue(outputs.loss is not None					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						int						=				self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.create_and_check_maskformer_model(a_				,       **a_				,       output_hidden_states=a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

			for model_class in self.all_model_classes:
				__snake_case	:						int						=				model_class(a_					).to(a_					)
				__snake_case	:						str						=				model(**a_				,       output_attentions=a_					)
				self.assertTrue(outputs.attentions is not None					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.model_tester.is_training:
				return
			# only MaskFormerForInstanceSegmentation has the loss
			__snake_case	:						Union[str, Any]						=				self.all_model_classes[1]
			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						List[Any]						=				self.model_tester.prepare_config_and_inputs()

			__snake_case	:						Union[str, Any]						=				model_class(a_					)
			model.to(a_					)
			model.train()

			__snake_case	:						Any						=				model(a_				,       mask_labels=a_				,       class_labels=a_					).loss
			loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.all_model_classes[1]
			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						str						=				self.model_tester.prepare_config_and_inputs()
			__snake_case	:						str						=				True
			__snake_case	:						str						=				True

			__snake_case	:						Optional[int]						=				model_class(a_					)
			model.to(a_					)
			model.train()

			__snake_case	:						Union[str, Any]						=				model(a_				,       mask_labels=a_				,       class_labels=a_					)

			__snake_case	:						Optional[Any]						=				outputs.encoder_hidden_states[0]
			encoder_hidden_states.retain_grad()

			__snake_case	:						Union[str, Any]						=				outputs.pixel_decoder_hidden_states[0]
			pixel_decoder_hidden_states.retain_grad()
			# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
			__snake_case	:						int						=				outputs.transformer_decoder_hidden_states[0]
			transformer_decoder_hidden_states.retain_grad()

			__snake_case	:						Any						=				outputs.attentions[0]
			attentions.retain_grad()

			outputs.loss.backward(retain_graph=a_					)

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

SCREAMING_SNAKE_CASE :    str									=				1E-4






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
	return image



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






		'''simple docstring'''

		@cached_property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return (
			    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
			    if is_vision_available()
			    else None
			)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''					).to(a_					)
			__snake_case	:						Tuple						=				self.default_image_processor
			__snake_case	:						List[str]						=				prepare_img()
			__snake_case	:						Dict						=				image_processor(a_				,       return_tensors='''pt'''					).to(a_					)
			__snake_case	:						str						=				inputs['''pixel_values'''].shape
			# check size is divisible by 32
			self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
			# check size
			self.assertEqual(a_				,       (1, 3, 8_00, 10_88)					)

			with torch.no_grad():
				__snake_case	:						Tuple						=				model(**a_					)

			__snake_case	:						Dict						=				torch.tensor(
			    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]					).to(a_					)
			self.assertTrue(
			    torch.allclose(
			        outputs.encoder_last_hidden_state[0, 0, :3, :3]				,       a_				,       atol=a_					)					)

			__snake_case	:						int						=				torch.tensor(
			    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]					).to(a_					)
			self.assertTrue(
			    torch.allclose(
			        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]				,       a_				,       atol=a_					)					)

			__snake_case	:						Any						=				torch.tensor(
			    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]					).to(a_					)
			self.assertTrue(
			    torch.allclose(
			        outputs.transformer_decoder_last_hidden_state[0, :3, :3]				,       a_				,       atol=a_					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				(
			    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
			    .to(a_					)
			    .eval()
			)
			__snake_case	:						str						=				self.default_image_processor
			__snake_case	:						Union[str, Any]						=				prepare_img()
			__snake_case	:						Tuple						=				image_processor(a_				,       return_tensors='''pt'''					).to(a_					)
			__snake_case	:						str						=				inputs['''pixel_values'''].shape
			# check size is divisible by 32
			self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
			# check size
			self.assertEqual(a_				,       (1, 3, 8_00, 10_88)					)

			with torch.no_grad():
				__snake_case	:						List[Any]						=				model(**a_					)
			# masks_queries_logits
			__snake_case	:						Tuple						=				outputs.masks_queries_logits
			self.assertEqual(
			    masks_queries_logits.shape				,       (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				,       )
			__snake_case	:						Any						=				[
			    [-1.373_7124, -1.772_4937, -1.936_4233],
			    [-1.597_7281, -1.986_7939, -2.152_3695],
			    [-1.579_5398, -1.926_9832, -2.09_3942],
			]
			__snake_case	:						List[Any]						=				torch.tensor(a_					).to(a_					)
			self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,       a_				,       atol=a_					)					)
			# class_queries_logits
			__snake_case	:						Tuple						=				outputs.class_queries_logits
			self.assertEqual(
			    class_queries_logits.shape				,       (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
			__snake_case	:						str						=				torch.tensor(
			    [
			        [1.6512E00, -5.2572E00, -3.3519E00],
			        [3.6169E-02, -5.9025E00, -2.9313E00],
			        [1.0766E-04, -7.7630E00, -5.1263E00],
			    ]					).to(a_					)
			self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,       a_				,       atol=a_					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				(
			    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''					)
			    .to(a_					)
			    .eval()
			)
			__snake_case	:						Dict						=				self.default_image_processor
			__snake_case	:						Optional[Any]						=				prepare_img()
			__snake_case	:						List[Any]						=				image_processor(a_				,       return_tensors='''pt'''					).to(a_					)
			__snake_case	:						Optional[int]						=				inputs['''pixel_values'''].shape
			# check size is divisible by 32
			self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
			# check size
			self.assertEqual(a_				,       (1, 3, 8_00, 10_88)					)

			with torch.no_grad():
				__snake_case	:						List[str]						=				model(**a_					)
			# masks_queries_logits
			__snake_case	:						str						=				outputs.masks_queries_logits
			self.assertEqual(
			    masks_queries_logits.shape				,       (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				,       )
			__snake_case	:						int						=				[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
			__snake_case	:						str						=				torch.tensor(a_					).to(a_					)
			self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]				,       a_				,       atol=a_					)					)
			# class_queries_logits
			__snake_case	:						str						=				outputs.class_queries_logits
			self.assertEqual(
			    class_queries_logits.shape				,       (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)					)
			__snake_case	:						int						=				torch.tensor(
			    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]					).to(a_					)
			self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]				,       a_				,       atol=a_					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				(
			    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''					)
			    .to(a_					)
			    .eval()
			)
			__snake_case	:						Dict						=				self.default_image_processor

			__snake_case	:						Any						=				image_processor(
			    [np.zeros((3, 8_00, 13_33)					), np.zeros((3, 8_00, 13_33)					)]				,       segmentation_maps=[np.zeros((3_84, 3_84)					).astype(np.floataa					), np.zeros((3_84, 3_84)					).astype(np.floataa					)]				,       return_tensors='''pt'''				,       )

			__snake_case	:						Any						=				inputs['''pixel_values'''].to(a_					)
			__snake_case	:						int						=				[el.to(a_					) for el in inputs['''mask_labels''']]
			__snake_case	:						int						=				[el.to(a_					) for el in inputs['''class_labels''']]

			with torch.no_grad():
				__snake_case	:						List[Any]						=				model(**a_					)

			self.assertTrue(outputs.loss is not None					)









"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5     ,   int(math.sqrt(_snake_case     ) + 1     )     ,   6     ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





