

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=1_0			,				_a=3			,				_a=3_2 * 4			,				_a=3_2 * 6			,				_a=4			,				_a=3_2			,				)       ->       Union[str, Any]:
												_a      :       int   =						parent
												_a      :       List[str]   =						batch_size
												_a      :       Optional[int]   =						is_training
												_a      :       Tuple   =						use_auxiliary_loss
												_a      :       Tuple   =						num_queries
												_a      :       List[str]   =						num_channels
												_a      :       Tuple   =						min_size
												_a      :       Union[str, Any]   =						max_size
												_a      :       Optional[Any]   =						num_labels
												_a      :       int   =						mask_feature_size


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
												    _a			)

												_a      :       Any   =						torch.ones([self.batch_size, self.min_size, self.max_size]			,				device=_a			)

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

												_a      :       int   =						self.get_config()
												return config, pixel_values, pixel_mask, mask_labels, class_labels


							def 							__lowercase							(     self			)       ->       List[str]:
												return MaskFormerConfig.from_backbone_and_decoder_configs(
												    backbone_config=SwinConfig(
												        depths=[1, 1, 1, 1]			,				)			,				decoder_config=DetrConfig(
												        decoder_ffn_dim=1_2_8			,				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 							__lowercase							(     self			)       ->       List[Any]:
												_a ,   _a ,   _a ,   _a ,   _a      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       str   =						{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
												return config, inputs_dict


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       Dict   =						output.encoder_hidden_states
												_a      :       Tuple   =						output.pixel_decoder_hidden_states
												_a      :       Any   =						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 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a=False			)       ->       Tuple:
												with torch.no_grad():
																	_a      :       Tuple   =						MaskFormerModel(config=_a			)
																	model.to(_a			)
																	model.eval()

																	_a      :       Optional[int]   =						model(pixel_values=_a			,				pixel_mask=_a			)
																	_a      :       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 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			,				_a			)       ->       Tuple:
												_a      :       Tuple   =						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():
																	_a      :       Optional[Any]   =						model(pixel_values=_a			,				pixel_mask=_a			)
																	_a      :       str   =						model(_a			)

																	comm_check_on_output(_a			)

																	_a      :       Tuple   =						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_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		int             =       (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							UpperCAmelCase__			:		Dict             =       (
							    {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Dict             =       False
							UpperCAmelCase__			:		Any             =       False
							UpperCAmelCase__			:		Dict             =       False
							UpperCAmelCase__			:		List[str]             =       False


							def 							__lowercase							(     self			)       ->       str:
												_a      :       str   =						MaskFormerModelTester(self			)
												_a      :       Any   =						ConfigTester(self			,				config_class=_a			,				has_text_modality=_a			)


							def 							__lowercase							(     self			)       ->       str:
												self.config_tester.run_common_tests()


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       Tuple   =						self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(_a			,				**_a			,				output_hidden_states=_a			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Optional[Any]   =						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 							__lowercase							(     self			)       ->       Optional[int]:
												pass


							@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass


							@unittest.skip(reason='''MaskFormer is not a generative model'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							@unittest.skip(reason='''MaskFormer does not use token embeddings'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


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


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


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       Dict   =						self.model_tester.prepare_config_and_inputs_for_common()

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

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


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in ["facebook/maskformer-swin-small-coco"]:
																	_a      :       Any   =						MaskFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Any   =						(self.model_tester.min_size,) * 2
												_a      :       Union[str, Any]   =						{
												    '''pixel_values''': torch.randn((2, 3, *size)			,				device=_a			),
												    '''mask_labels''': torch.randn((2, 1_0, *size)			,				device=_a			),
												    '''class_labels''': torch.zeros(2			,				1_0			,				device=_a			).long(),
												}

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


							def 							__lowercase							(     self			)       ->       Any:
												_a ,   _a      :       Optional[Any]   =						self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskformer_model(_a			,				**_a			,				output_hidden_states=_a			)


							def 							__lowercase							(     self			)       ->       List[str]:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

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


							def 							__lowercase							(     self			)       ->       List[Any]:
												if not self.model_tester.is_training:
																	return
												# only MaskFormerForInstanceSegmentation has the loss
												_a      :       Tuple   =						self.all_model_classes[1]
												_a ,   _a ,   _a ,   _a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs()

												_a      :       Dict   =						model_class(_a			)
												model.to(_a			)
												model.train()

												_a      :       Optional[int]   =						model(_a			,				mask_labels=_a			,				class_labels=_a			).loss
												loss.backward()




							def 							__lowercase							(     self			)       ->       Dict:
												# only MaskFormerForInstanceSegmentation has the loss
												_a      :       List[str]   =						self.all_model_classes[1]
												_a ,   _a ,   _a ,   _a ,   _a      :       Tuple   =						self.model_tester.prepare_config_and_inputs()
												_a      :       Union[str, Any]   =						True
												_a      :       Tuple   =						True

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

												_a      :       Optional[int]   =						model(_a			,				mask_labels=_a			,				class_labels=_a			)

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

												_a      :       Optional[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
												_a      :       Union[str, Any]   =						outputs.transformer_decoder_hidden_states[0]
												transformer_decoder_hidden_states.retain_grad()

												_a      :       Optional[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			)



a__												=						1E-4



def   __UpperCAmelCase					(      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







@require_vision
@slow
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return (
												    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''			)
												    if is_vision_available()
												    else None
												)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Dict   =						MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''			).to(_a			)
												_a      :       Optional[int]   =						self.default_image_processor
												_a      :       List[Any]   =						prepare_img()
												_a      :       int   =						image_processor(_a			,				return_tensors='''pt'''			).to(_a			)
												_a      :       List[Any]   =						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(_a			,				(1, 3, 8_0_0, 1_0_8_8)			)

												with torch.no_grad():
																	_a      :       List[str]   =						model(**_a			)

												_a      :       Union[str, Any]   =						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			)			)

												_a      :       Optional[Any]   =						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			)			)

												_a      :       Tuple   =						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 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Tuple   =						(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''			)
												    .to(_a			)
												    .eval()
												)
												_a      :       Optional[int]   =						self.default_image_processor
												_a      :       Optional[int]   =						prepare_img()
												_a      :       Union[str, Any]   =						image_processor(_a			,				return_tensors='''pt'''			).to(_a			)
												_a      :       Tuple   =						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(_a			,				(1, 3, 8_0_0, 1_0_8_8)			)

												with torch.no_grad():
																	_a      :       Any   =						model(**_a			)
												# masks_queries_logits
												_a      :       Optional[Any]   =						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)			,				)
												_a      :       Optional[int]   =						[
												    [-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],
												]
												_a      :       Tuple   =						torch.tensor(_a			).to(_a			)
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,				_a			,				atol=_a			)			)
												# class_queries_logits
												_a      :       List[Any]   =						outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
												_a      :       Dict   =						torch.tensor(
												    [
												        [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
												        [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
												        [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
												    ]			).to(_a			)
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,				_a			,				atol=_a			)			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Any   =						(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''			)
												    .to(_a			)
												    .eval()
												)
												_a      :       Dict   =						self.default_image_processor
												_a      :       str   =						prepare_img()
												_a      :       Union[str, Any]   =						image_processor(_a			,				return_tensors='''pt'''			).to(_a			)
												_a      :       Union[str, Any]   =						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(_a			,				(1, 3, 8_0_0, 1_0_8_8)			)

												with torch.no_grad():
																	_a      :       int   =						model(**_a			)
												# masks_queries_logits
												_a      :       Optional[Any]   =						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)			,				)
												_a      :       Union[str, Any]   =						[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
												_a      :       str   =						torch.tensor(_a			).to(_a			)
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,				_a			,				atol=_a			)			)
												# class_queries_logits
												_a      :       str   =						outputs.class_queries_logits
												self.assertEqual(
												    class_queries_logits.shape			,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)			)
												_a      :       Union[str, Any]   =						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 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Dict   =						(
												    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''			)
												    .to(_a			)
												    .eval()
												)
												_a      :       Optional[Any]   =						self.default_image_processor

												_a      :       str   =						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'''			,				)

												_a      :       List[str]   =						inputs['''pixel_values'''].to(_a			)
												_a      :       Dict   =						[el.to(_a			) for el in inputs['''mask_labels''']]
												_a      :       List[str]   =						[el.to(_a			) for el in inputs['''class_labels''']]

												with torch.no_grad():
																	_a      :       Tuple   =						model(**_a			)

												self.assertTrue(outputs.loss is not None			)










_lowercase		:  Optional[Any]			       ="""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_lowercase		:  Union[str, Any]			       =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase		:  List[Any]			       ={
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}
