



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  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=2  ,  a_=True  ,  a_=False  ,  a_=10  ,  a_=3  ,  a_=32 * 8  ,  a_=32 * 8  ,  a_=4  ,  a_=64  ,  ):
											a__				      =		parent
											a__				      =		batch_size
											a__				      =		is_training
											a__				      =		use_auxiliary_loss
											a__				      =		num_queries
											a__				      =		num_channels
											a__				      =		min_size
											a__				      =		max_size
											a__				      =		num_labels
											a__				      =		hidden_dim
											a__				      =		hidden_dim


							def _a      (							self		):
											a__				      =		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
											    a_		)

											a__				      =		torch.ones([self.batch_size, self.min_size, self.max_size]  ,  device=a_		)

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

											a__				      =		self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels


							def _a      (							self		):
											a__				      =		MaskaFormerConfig(
											    hidden_size=self.hidden_dim  ,  )
											a__				      =		self.num_queries
											a__				      =		self.num_labels

											a__				      =		[1, 1, 1, 1]
											a__				      =		self.num_channels

											a__				      =		64
											a__				      =		128
											a__				      =		self.hidden_dim
											a__				      =		self.hidden_dim
											a__				      =		self.hidden_dim
											return config


							def _a      (							self		):
											a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		self.prepare_config_and_inputs()
											a__				      =		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
											return config, inputs_dict


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		output.encoder_hidden_states
											a__				      =		output.pixel_decoder_hidden_states
											a__				      =		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_layers		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_=False		):
											with torch.no_grad():
															a__				      =		MaskaFormerModel(config=a_		)
															model.to(a_		)
															model.eval()

															a__				      =		model(pixel_values=a_  ,  pixel_mask=a_		)
															a__				      =		model(a_  ,  output_hidden_states=a_		)

											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(a_  ,  a_		)







							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											a__				      =		MaskaFormerForUniversalSegmentation(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():
															a__				      =		model(pixel_values=a_  ,  pixel_mask=a_		)
															a__				      =		model(a_		)

															comm_check_on_output(a_		)

															a__				      =		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  __snake_case      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							UpperCamelCase__				:      Optional[int]         =					{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

							UpperCamelCase__				:      Dict         =					False
							UpperCamelCase__				:      Union[str, Any]         =					False
							UpperCamelCase__				:      Union[str, Any]         =					False
							UpperCamelCase__				:      int         =					False


							def _a      (							self		):
											a__				      =		MaskaFormerModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_  ,  has_text_modality=a_		)


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


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(a_  ,  **a_  ,  output_hidden_states=a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*a_		)


							@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		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															a__				      =		model_class(a_		)
															a__				      =		inspect.signature(model.forward		)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															a__				      =		[*signature.parameters.keys()]

															a__				      =		["""pixel_values"""]
															self.assertListEqual(arg_names[:1]  ,  a_		)


							@slow
							def _a      (							self		):
											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
															a__				      =		MaskaFormerModel.from_pretrained(a_		)
															self.assertIsNotNone(a_		)


							def _a      (							self		):
											a__				      =		(self.model_tester.min_size,) * 2
											a__				      =		{
											    """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(),
											}
											a__				      =		self.model_tester.get_config()

											a__				      =		MaskaFormerForUniversalSegmentation(a_		).to(a_		)
											a__				      =		model(**a_		)
											self.assertTrue(outputs.loss is not None		)


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(a_  ,  **a_  ,  output_hidden_states=a_		)


							def _a      (							self		):
											a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs_for_common()

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


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

											a__				      =		self.all_model_classes[1]
											a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs()

											a__				      =		model_class(a_		)
											model.to(a_		)
											model.train()

											a__				      =		model(a_  ,  mask_labels=a_  ,  class_labels=a_		).loss
											loss.backward()







							def _a      (							self		):
											a__				      =		self.all_model_classes[1]
											a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		self.model_tester.prepare_config_and_inputs()
											a__				      =		True
											a__				      =		True

											a__				      =		model_class(a_		).to(a_		)
											model.train()

											a__				      =		model(a_  ,  mask_labels=a_  ,  class_labels=a_		)

											a__				      =		outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											a__				      =		outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											a__				      =		outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											a__				      =		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		)




UpperCAmelCase							 =					1E-4

def 			A_      (	):

				"""simple docstring"""
				a__				      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
				return image




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







							'''simple docstring'''






							@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		):
											a__				      =		MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(a_		)
											a__				      =		self.default_image_processor
											a__				      =		prepare_img()
											a__				      =		image_processor(a_  ,  return_tensors="""pt"""		).to(a_		)
											a__				      =		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, 384, 384)		)

											with torch.no_grad():
															a__				      =		model(**a_		)

											a__				      =		torch.tensor(
											    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]		).to(a_		)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,  a_  ,  atol=a_		)		)

											a__				      =		torch.tensor(
											    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]		).to(a_		)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,  a_  ,  atol=a_		)		)

											a__				      =		torch.tensor(
											    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]		).to(a_		)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,  a_  ,  atol=a_		)		)


							def _a      (							self		):
											a__				      =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(a_		).eval()
											a__				      =		self.default_image_processor
											a__				      =		prepare_img()
											a__				      =		image_processor(a_  ,  return_tensors="""pt"""		).to(a_		)
											a__				      =		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, 384, 384)		)

											with torch.no_grad():
															a__				      =		model(**a_		)
											# masks_queries_logits
											a__				      =		outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape  ,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
											a__				      =		[
											    [-8.7_839, -9.0_056, -8.8_121],
											    [-7.4_104, -7.0_313, -6.5_401],
											    [-6.6_105, -6.3_427, -6.4_675],
											]
											a__				      =		torch.tensor(a_		).to(a_		)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,  a_  ,  atol=a_		)		)
											# class_queries_logits
											a__				      =		outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape  ,  (1, model.config.num_queries, model.config.num_labels + 1)		)
											a__				      =		torch.tensor(
											    [
											        [1.8_324, -8.0_835, -4.1_922],
											        [0.8_450, -9.0_050, -3.6_053],
											        [0.3_045, -7.7_293, -3.0_275],
											    ]		).to(a_		)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,  a_  ,  atol=a_		)		)







							def _a      (							self		):
											a__				      =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(a_		).eval()
											a__				      =		self.default_image_processor

											a__				      =		image_processor(
											    [np.zeros((3, 800, 1_333)		), np.zeros((3, 800, 1_333)		)]  ,  segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]  ,  return_tensors="""pt"""  ,  )

											a__				      =		inputs["""pixel_values"""].to(a_		)
											a__				      =		[el.to(a_		) for el in inputs["""mask_labels"""]]
											a__				      =		[el.to(a_		) for el in inputs["""class_labels"""]]

											with torch.no_grad():
															a__				      =		model(**a_		)

											self.assertTrue(outputs.loss is not None		)






def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				return abs(__a		) if a == 0 else greatest_common_divisor(b % a ,		__a		)

def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								a__      ,			a__				      =		y, x % y
				return abs(__a		)

def 			A_      (	):

				"""simple docstring"""
				try:
								a__				      =		input("""Enter two integers separated by comma (,): """		).split(""","""		)
								a__				      =		int(nums[0]		)
								a__				      =		int(nums[1]		)
								print(
								    F'''greatest_common_divisor({num_a}, {num_a}) = '''
								    F'''{greatest_common_divisor(__a ,		__a		)}'''		)
								print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__a ,		__a		)}'''		)
				except (IndexError, UnboundLocalError, ValueError):
								print("""Wrong input"""		)


if __name__ == "__main__":
	main()

