





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 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any]=2,			_UpperCAmelCase     :					List[Any]=True,			_UpperCAmelCase     :					Union[str, Any]=False,			_UpperCAmelCase     :					List[str]=1_0,			_UpperCAmelCase     :					Any=3,			_UpperCAmelCase     :					Tuple=3_2 * 4,			_UpperCAmelCase     :					List[str]=3_2 * 6,			_UpperCAmelCase     :					List[str]=4,			_UpperCAmelCase     :					Any=3_2,			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						parent
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						batch_size
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						is_training
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						use_auxiliary_loss
														SCREAMING_SNAKE_CASE__       :      str     =						num_queries
														SCREAMING_SNAKE_CASE__       :      str     =						num_channels
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						min_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						num_labels
														SCREAMING_SNAKE_CASE__       :      Dict     =						mask_feature_size






							def  A_     ( self     :					Any						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
														    _UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.ones([self.batch_size, self.min_size, self.max_size],			device=_UpperCAmelCase						)

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

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






							def  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														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  A_     ( self     :					Tuple						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__       :      int     =						{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
														return config, inputs_dict






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						output.encoder_hidden_states
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						output.pixel_decoder_hidden_states
														SCREAMING_SNAKE_CASE__       :      Any     =						output.transformer_decoder_hidden_states

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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Optional[int]=False						)				->			Tuple:





														"""simple docstring"""
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      str     =						MaskFormerModel(config=_UpperCAmelCase						)
																					model.to(_UpperCAmelCase						)
																					model.eval()

																					SCREAMING_SNAKE_CASE__       :      str     =						model(pixel_values=_UpperCAmelCase,			pixel_mask=_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						model(_UpperCAmelCase,			output_hidden_states=_UpperCAmelCase						)
														# 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(_UpperCAmelCase,			_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					int						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						MaskFormerForInstanceSegmentation(config=_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.eval()

														def comm_check_on_output(_UpperCAmelCase     :					Optional[int]						):
																					# 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():
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						model(pixel_values=_UpperCAmelCase,			pixel_mask=_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model(_UpperCAmelCase						)

																					comm_check_on_output(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(
																					    pixel_values=_UpperCAmelCase,			pixel_mask=_UpperCAmelCase,			mask_labels=_UpperCAmelCase,			class_labels=_UpperCAmelCase						)

														comm_check_on_output(_UpperCAmelCase						)

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


@require_torch
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase      ):







							"""simple docstring"""




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

							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					Any						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						MaskFormerModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						ConfigTester(self,			config_class=_UpperCAmelCase,			has_text_modality=_UpperCAmelCase						)






							def  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														self.config_tester.run_common_tests()






							def  A_     ( self     :					Tuple						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase,			**_UpperCAmelCase,			output_hidden_states=_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_UpperCAmelCase						)






							@unittest.skip(reason="MaskFormer does not use inputs_embeds"						)
							def  A_     ( self     :					Optional[int]						)				->			Any:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														pass






							@unittest.skip(reason="MaskFormer is not a generative model"						)
							def  A_     ( self     :					str						)				->			str:





														"""simple docstring"""
														pass






							@unittest.skip(reason="MaskFormer does not use token embeddings"						)
							def  A_     ( self     :					Tuple						)				->			List[Any]:





														"""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  A_     ( self     :					Dict						)				->			Dict:





														"""simple docstring"""
														pass






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





														"""simple docstring"""
														pass






							def  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

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

																					SCREAMING_SNAKE_CASE__       :      Any     =						["pixel_values"]
																					self.assertListEqual(arg_names[:1],			_UpperCAmelCase						)






							@slow
							def  A_     ( self     :					int						)				->			Union[str, Any]:





														"""simple docstring"""
														for model_name in ["facebook/maskformer-swin-small-coco"]:
																					SCREAMING_SNAKE_CASE__       :      str     =						MaskFormerModel.from_pretrained(_UpperCAmelCase						)
																					self.assertIsNotNone(_UpperCAmelCase						)






							def  A_     ( self     :					Union[str, Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						(self.model_tester.min_size,) * 2
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{
														    "pixel_values": torch.randn((2, 3, *size),			device=_UpperCAmelCase						),
														    "mask_labels": torch.randn((2, 1_0, *size),			device=_UpperCAmelCase						),
														    "class_labels": torch.zeros(2,			1_0,			device=_UpperCAmelCase						).long(),
														}

														SCREAMING_SNAKE_CASE__       :      int     =						MaskFormerForInstanceSegmentation(MaskFormerConfig()						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						model(**_UpperCAmelCase						)
														self.assertTrue(outputs.loss is not None						)






							def  A_     ( self     :					Union[str, Any]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase,			**_UpperCAmelCase,			output_hidden_states=_UpperCAmelCase						)






							def  A_     ( self     :					Any						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						self.model_tester.prepare_config_and_inputs_for_common()

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






							def  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





														"""simple docstring"""
														if not self.model_tester.is_training:
																					return
														# only MaskFormerForInstanceSegmentation has the loss
														SCREAMING_SNAKE_CASE__       :      str     =						self.all_model_classes[1]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						self.model_tester.prepare_config_and_inputs()

														SCREAMING_SNAKE_CASE__       :      Any     =						model_class(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.train()

														SCREAMING_SNAKE_CASE__       :      int     =						model(_UpperCAmelCase,			mask_labels=_UpperCAmelCase,			class_labels=_UpperCAmelCase						).loss
														loss.backward()






							def  A_     ( self     :					int						)				->			Union[str, Any]:





														"""simple docstring"""
														# only MaskFormerForInstanceSegmentation has the loss
														SCREAMING_SNAKE_CASE__       :      Any     =						self.all_model_classes[1]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      str     =						self.model_tester.prepare_config_and_inputs()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						True
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						True

														SCREAMING_SNAKE_CASE__       :      int     =						model_class(_UpperCAmelCase						)
														model.to(_UpperCAmelCase						)
														model.train()

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(_UpperCAmelCase,			mask_labels=_UpperCAmelCase,			class_labels=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						outputs.encoder_hidden_states[0]
														encoder_hidden_states.retain_grad()

														SCREAMING_SNAKE_CASE__       :      Tuple     =						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
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						outputs.transformer_decoder_hidden_states[0]
														transformer_decoder_hidden_states.retain_grad()

														SCREAMING_SNAKE_CASE__       :      int     =						outputs.attentions[0]
														attentions.retain_grad()

														outputs.loss.backward(retain_graph=_UpperCAmelCase						)

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


_lowerCamelCase :    int  							= 1e-4




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

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Tuple     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image







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







							"""simple docstring"""




							@cached_property
							def  A_     ( self     :					int						)				->			Dict:





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






							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco"						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      List[str]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						image_processor(_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[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(_UpperCAmelCase,			(1, 3, 8_0_0, 1_0_8_8)						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      Any     =						model(**_UpperCAmelCase						)

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

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

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






							def  A_     ( self     :					int						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						(
														    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"						)
														    .to(_UpperCAmelCase						)
														    .eval()
														)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      int     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						image_processor(_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      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(_UpperCAmelCase,			(1, 3, 8_0_0, 1_0_8_8)						)

														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__       :      str     =						model(**_UpperCAmelCase						)
														# masks_queries_logits
														SCREAMING_SNAKE_CASE__       :      List[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),			)
														SCREAMING_SNAKE_CASE__       :      Dict     =						[
														    [-1.3737124, -1.7724937, -1.9364233],
														    [-1.5977281, -1.9867939, -2.1523695],
														    [-1.5795398, -1.9269832, -2.093942],
														]
														SCREAMING_SNAKE_CASE__       :      Any     =						torch.tensor(_UpperCAmelCase						).to(_UpperCAmelCase						)
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],			_UpperCAmelCase,			atol=_UpperCAmelCase						)						)
														# class_queries_logits
														SCREAMING_SNAKE_CASE__       :      Any     =						outputs.class_queries_logits
														self.assertEqual(
														    class_queries_logits.shape,			(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.tensor(
														    [
														        [1.6512E00, -5.2572E00, -3.3519E00],
														        [3.6169E-02, -5.9025E00, -2.9313E00],
														        [1.0766E-04, -7.7630E00, -5.1263E00],
														    ]						).to(_UpperCAmelCase						)
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],			_UpperCAmelCase,			atol=_UpperCAmelCase						)						)






							def  A_     ( self     :					Dict						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						(
														    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff"						)
														    .to(_UpperCAmelCase						)
														    .eval()
														)
														SCREAMING_SNAKE_CASE__       :      int     =						self.default_image_processor
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						prepare_img()
														SCREAMING_SNAKE_CASE__       :      str     =						image_processor(_UpperCAmelCase,			return_tensors="pt"						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      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(_UpperCAmelCase,			(1, 3, 8_0_0, 1_0_8_8)						)

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






							def  A_     ( self     :					Any						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						(
														    MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco"						)
														    .to(_UpperCAmelCase						)
														    .eval()
														)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.default_image_processor

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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",			)

														SCREAMING_SNAKE_CASE__       :      int     =						inputs["pixel_values"].to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						[el.to(_UpperCAmelCase						) for el in inputs["mask_labels"]]
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[el.to(_UpperCAmelCase						) for el in inputs["class_labels"]]

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

														self.assertTrue(outputs.loss is not None						)













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






