

'''simple docstring'''

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       UpperCAmelCase :




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




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Tuple:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
						    UpperCamelCase__				)

						lowercase         = torch.ones([self.batch_size, self.min_size, self.max_size]							,			device=UpperCamelCase__				)

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

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




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> List[str]:
						lowercase         = MaskaFormerConfig(
						    hidden_size=self.hidden_dim							,			)
						lowercase         = self.num_queries
						lowercase         = self.num_labels

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

						lowercase         = 6_4
						lowercase         = 1_2_8
						lowercase         = self.hidden_dim
						lowercase         = self.hidden_dim
						lowercase         = self.hidden_dim
						return config




		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = self.prepare_config_and_inputs()
						lowercase         = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
						return config, inputs_dict




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	List[Any]							,			A__      :	List[Any]				)					-> str:
						lowercase         = output.encoder_hidden_states
						lowercase         = output.pixel_decoder_hidden_states
						lowercase         = 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_layers				)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	str							,			A__      :	Optional[Any]							,			A__      :	Optional[int]							,			A__      :	List[str]=False				)					-> Dict:





						with torch.no_grad():
										lowercase         = MaskaFormerModel(config=UpperCamelCase__				)
										model.to(UpperCamelCase__				)
										model.eval()

										lowercase         = model(pixel_values=UpperCamelCase__							,			pixel_mask=UpperCamelCase__				)
										lowercase         = model(UpperCamelCase__							,			output_hidden_states=UpperCamelCase__				)

						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(UpperCamelCase__							,			UpperCamelCase__				)





		def 				UpperCAmelCase__   (self      :	str							,			A__      :	List[Any]							,			A__      :	str							,			A__      :	Optional[int]							,			A__      :	Any							,			A__      :	int				)					-> int:
						lowercase         = MaskaFormerForUniversalSegmentation(config=UpperCamelCase__				)
						model.to(UpperCamelCase__				)
						model.eval()

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

						with torch.no_grad():
										lowercase         = model(pixel_values=UpperCamelCase__							,			pixel_mask=UpperCamelCase__				)
										lowercase         = model(UpperCamelCase__				)

										comm_check_on_output(UpperCamelCase__				)

										lowercase         = 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       UpperCAmelCase (						_lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		UpperCAmelCase					:	int 			=		{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

		UpperCAmelCase					:	Optional[Any] 			=		False
		UpperCAmelCase					:	List[Any] 			=		False
		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	str 			=		False




		def 				UpperCAmelCase__   (self      :	Any				)					-> List[str]:
						lowercase         = MaskaFormerModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=UpperCamelCase__							,			has_text_modality=UpperCamelCase__				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Dict:
						self.config_tester.run_common_tests()




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[int]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__							,			**UpperCamelCase__							,			output_hidden_states=UpperCamelCase__				)




		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*UpperCamelCase__				)




		@unittest.skip(reason="Mask2Former does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	str				)					-> Union[str, Any]:
						pass




		@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Any:
						pass




		@unittest.skip(reason="Mask2Former is not a generative model"				)
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> str:
						pass




		@unittest.skip(reason="Mask2Former does not use token embeddings"				)
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Union[str, Any]:
						pass




		@require_torch_multi_gpu
		@unittest.skip(
		    reason="Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`"				)
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Dict:
						pass




		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Union[str, Any]:
						pass




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			UpperCamelCase__				)




		@slow
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Any:





						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
										lowercase         = MaskaFormerModel.from_pretrained(UpperCamelCase__				)
										self.assertIsNotNone(UpperCamelCase__				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Dict:
						lowercase         = (self.model_tester.min_size,) * 2
						lowercase         = {
						    "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(),
						}
						lowercase         = self.model_tester.get_config()

						lowercase         = MaskaFormerForUniversalSegmentation(UpperCamelCase__				).to(UpperCamelCase__				)
						lowercase         = model(**UpperCamelCase__				)
						self.assertTrue(outputs.loss is not None				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Optional[int]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(UpperCamelCase__							,			**UpperCamelCase__							,			output_hidden_states=UpperCamelCase__				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Dict:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[str]:





						if not self.model_tester.is_training:
										return

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

						lowercase         = model_class(UpperCamelCase__				)
						model.to(UpperCamelCase__				)
						model.train()

						lowercase         = model(UpperCamelCase__							,			mask_labels=UpperCamelCase__							,			class_labels=UpperCamelCase__				).loss
						loss.backward()





		def 				UpperCAmelCase__   (self      :	int				)					-> Dict:
						lowercase         = self.all_model_classes[1]
						lowercase   ,							lowercase   ,							lowercase   ,							lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs()
						lowercase         = True
						lowercase         = True

						lowercase         = model_class(UpperCamelCase__				).to(UpperCamelCase__				)
						model.train()

						lowercase         = model(UpperCamelCase__							,			mask_labels=UpperCamelCase__							,			class_labels=UpperCamelCase__				)

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

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

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

						lowercase         = 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 : List[Any]											=			1e-4







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






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


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




		@cached_property
		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Any:
						return "facebook/mask2former-swin-small-coco-instance"




		@cached_property
		def 				UpperCAmelCase__   (self      :	Dict				)					-> str:
						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[str]:
						lowercase         = MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(UpperCamelCase__				)
						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(UpperCamelCase__							,			return_tensors="pt"				).to(UpperCamelCase__				)
						lowercase         = 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, 3_8_4, 3_8_4)				)

						with torch.no_grad():
										lowercase         = model(**UpperCamelCase__				)

						lowercase         = torch.tensor(
						    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]				).to(UpperCamelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]							,			UpperCamelCase__							,			atol=UpperCamelCase__				)				)

						lowercase         = torch.tensor(
						    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]				).to(UpperCamelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,			UpperCamelCase__							,			atol=UpperCamelCase__				)				)

						lowercase         = torch.tensor(
						    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]				).to(UpperCamelCase__				)
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,			UpperCamelCase__							,			atol=UpperCamelCase__				)				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(UpperCamelCase__				).eval()
						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(UpperCamelCase__							,			return_tensors="pt"				).to(UpperCamelCase__				)
						lowercase         = 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, 3_8_4, 3_8_4)				)

						with torch.no_grad():
										lowercase         = model(**UpperCamelCase__				)
						# masks_queries_logits
						lowercase         = outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape							,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
						lowercase         = [
						    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
						    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
						    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
						]
						lowercase         = torch.tensor(UpperCamelCase__				).to(UpperCamelCase__				)
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,			UpperCamelCase__							,			atol=UpperCamelCase__				)				)
						# class_queries_logits
						lowercase         = outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape							,			(1, model.config.num_queries, model.config.num_labels + 1)				)
						lowercase         = torch.tensor(
						    [
						        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
						        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
						        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
						    ]				).to(UpperCamelCase__				)
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,			UpperCamelCase__							,			atol=UpperCamelCase__				)				)





		def 				UpperCAmelCase__   (self      :	Tuple				)					-> List[Any]:
						lowercase         = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(UpperCamelCase__				).eval()
						lowercase         = self.default_image_processor

						lowercase         = image_processor(
						    [np.zeros((3, 8_0_0, 1_3_3_3)				), np.zeros((3, 8_0_0, 1_3_3_3)				)]							,			segmentation_maps=[np.zeros((3_8_4, 3_8_4)				).astype(np.floataa				), np.zeros((3_8_4, 3_8_4)				).astype(np.floataa				)]							,			return_tensors="pt"							,			)

						lowercase         = inputs["pixel_values"].to(UpperCamelCase__				)
						lowercase         = [el.to(UpperCamelCase__				) for el in inputs["mask_labels"]]
						lowercase         = [el.to(UpperCamelCase__				) for el in inputs["class_labels"]]

						with torch.no_grad():
										lowercase         = model(**UpperCamelCase__				)

						self.assertTrue(outputs.loss is not None				)











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	Tuple							,			*A__      :	Union[str, Any]							,			**A__      :	Optional[Any]				)					-> None:
						warnings.warn(
						    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use CLIPImageProcessor instead."							,			A__							,			)
						super().__init__(*A__							,			**A__				)




