



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			:




				def __init__(						self					:						Tuple,					__lowercase					:						Union[str, Any],					__lowercase					:						Union[str, Any]=2,					__lowercase					:						List[str]=True,					__lowercase					:						Dict=False,					__lowercase					:						Dict=10,					__lowercase					:						List[Any]=3,					__lowercase					:						Union[str, Any]=32 * 8,					__lowercase					:						Optional[int]=32 * 8,					__lowercase					:						List[str]=4,					__lowercase					:						Tuple=64,					):
							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 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
							    __lowercase						)

							lowercase__					  =				torch.ones([self.batch_size, self.min_size, self.max_size],					device=__lowercase						)

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

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




				def 							A__      (						self					:						Dict						):
							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__					  =				64
							lowercase__					  =				128
							lowercase__					  =				self.hidden_dim
							lowercase__					  =				self.hidden_dim
							lowercase__					  =				self.hidden_dim
							return config




				def 							A__      (						self					:						str						):
							lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__  ,       lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__					  =				{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
							return config, inputs_dict




				def 							A__      (						self					:						str,					__lowercase					:						Tuple,					__lowercase					:						int						):
							lowercase__					  =				output.encoder_hidden_states
							lowercase__					  =				output.pixel_decoder_hidden_states
							lowercase__					  =				output.transformer_decoder_hidden_states

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




				def 							A__      (						self					:						List[Any],					__lowercase					:						Optional[Any],					__lowercase					:						List[str],					__lowercase					:						List[str],					__lowercase					:						Optional[Any]=False						):
							with torch.no_grad():
										lowercase__					  =				MaskaFormerModel(config=__lowercase						)
										model.to(__lowercase						)
										model.eval()

										lowercase__					  =				model(pixel_values=__lowercase,					pixel_mask=__lowercase						)
										lowercase__					  =				model(__lowercase,					output_hidden_states=__lowercase						)

							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(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						str,					__lowercase					:						int,					__lowercase					:						str,					__lowercase					:						Union[str, Any],					__lowercase					:						str						):
							lowercase__					  =				MaskaFormerForUniversalSegmentation(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()

							def comm_check_on_output(__lowercase					:						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():
										lowercase__					  =				model(pixel_values=__lowercase,					pixel_mask=__lowercase						)
										lowercase__					  =				model(__lowercase						)

										comm_check_on_output(__lowercase						)

										lowercase__					  =				model(
										    pixel_values=__lowercase,					pixel_mask=__lowercase,					mask_labels=__lowercase,					class_labels=__lowercase						)

							comm_check_on_output(__lowercase						)

							self.parent.assertTrue(result.loss is not None						)
							self.parent.assertEqual(result.loss.shape,					torch.Size([1]						)						)
@require_torch
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				UpperCamelCase__				:					Tuple     		={"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

				UpperCamelCase__				:					int     		=False
				UpperCamelCase__				:					Optional[int]     		=False
				UpperCamelCase__				:					Optional[int]     		=False
				UpperCamelCase__				:					List[str]     		=False




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				MaskaFormerModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__lowercase,					has_text_modality=__lowercase						)




				def 							A__      (						self					:						str						):
							self.config_tester.run_common_tests()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(__lowercase,					**__lowercase,					output_hidden_states=__lowercase						)




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowercase						)




				@unittest.skip(reason="Mask2Former does not use inputs_embeds"						)
				def 							A__      (						self					:						Optional[Any]						):
							pass




				@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"						)
				def 							A__      (						self					:						int						):
							pass




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




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




				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
				def 							A__      (						self					:						Optional[int]						):
							pass




				def 							A__      (						self					:						int						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__					  =				model_class(__lowercase						)
										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],					__lowercase						)




				@slow
				def 							A__      (						self					:						Optional[int]						):
							for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
										lowercase__					  =				MaskaFormerModel.from_pretrained(__lowercase						)
										self.assertIsNotNone(__lowercase						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				(self.model_tester.min_size,) * 2
							lowercase__					  =				{
							    "pixel_values": torch.randn((2, 3, *size),					device=__lowercase						),
							    "mask_labels": torch.randn((2, 10, *size),					device=__lowercase						),
							    "class_labels": torch.zeros(2,					10,					device=__lowercase						).long(),
							}
							lowercase__					  =				self.model_tester.get_config()

							lowercase__					  =				MaskaFormerForUniversalSegmentation(__lowercase						).to(__lowercase						)
							lowercase__					  =				model(**__lowercase						)
							self.assertTrue(outputs.loss is not None						)




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskaformer_model(__lowercase,					**__lowercase,					output_hidden_states=__lowercase						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

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




				def 							A__      (						self					:						Optional[int]						):
							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(__lowercase						)
							model.to(__lowercase						)
							model.train()

							lowercase__					  =				model(__lowercase,					mask_labels=__lowercase,					class_labels=__lowercase						).loss
							loss.backward()




				def 							A__      (						self					:						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(__lowercase						).to(__lowercase						)
							model.train()

							lowercase__					  =				model(__lowercase,					mask_labels=__lowercase,					class_labels=__lowercase						)

							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=__lowercase						)

							self.assertIsNotNone(encoder_hidden_states.grad						)
							self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
							self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
							self.assertIsNotNone(attentions.grad						)
lowercase_								=     1e-4



def __lowerCAmelCase (     ):
			lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image




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




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




				@cached_property
				def 							A__      (						self					:						List[Any]						):
							return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(__lowercase						)
							lowercase__					  =				self.default_image_processor
							lowercase__					  =				prepare_img()
							lowercase__					  =				image_processor(__lowercase,					return_tensors="pt"						).to(__lowercase						)
							lowercase__					  =				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(__lowercase,					(1, 3, 384, 384)						)

							with torch.no_grad():
										lowercase__					  =				model(**__lowercase						)

							lowercase__					  =				torch.tensor(
							    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(__lowercase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3],					__lowercase,					atol=__lowercase						)						)

							lowercase__					  =				torch.tensor(
							    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(__lowercase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],					__lowercase,					atol=__lowercase						)						)

							lowercase__					  =				torch.tensor(
							    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(__lowercase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3],					__lowercase,					atol=__lowercase						)						)




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(__lowercase						).eval()
							lowercase__					  =				self.default_image_processor
							lowercase__					  =				prepare_img()
							lowercase__					  =				image_processor(__lowercase,					return_tensors="pt"						).to(__lowercase						)
							lowercase__					  =				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(__lowercase,					(1, 3, 384, 384)						)

							with torch.no_grad():
										lowercase__					  =				model(**__lowercase						)
							# 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__					  =				[
							    [-8.7839, -9.0056, -8.8121],
							    [-7.4104, -7.0313, -6.5401],
							    [-6.6105, -6.3427, -6.4675],
							]
							lowercase__					  =				torch.tensor(__lowercase						).to(__lowercase						)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],					__lowercase,					atol=__lowercase						)						)
							# 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(
							    [
							        [1.8324, -8.0835, -4.1922],
							        [0.8450, -9.0050, -3.6053],
							        [0.3045, -7.7293, -3.0275],
							    ]						).to(__lowercase						)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],					__lowercase,					atol=__lowercase						)						)




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(__lowercase						).eval()
							lowercase__					  =				self.default_image_processor

							lowercase__					  =				image_processor(
							    [np.zeros((3, 800, 1333)						), np.zeros((3, 800, 1333)						)],					segmentation_maps=[np.zeros((384, 384)						).astype(np.floataa						), np.zeros((384, 384)						).astype(np.floataa						)],					return_tensors="pt",					)

							lowercase__					  =				inputs["pixel_values"].to(__lowercase						)
							lowercase__					  =				[el.to(__lowercase						) for el in inputs["mask_labels"]]
							lowercase__					  =				[el.to(__lowercase						) for el in inputs["class_labels"]]

							with torch.no_grad():
										lowercase__					  =				model(**__lowercase						)

							self.assertTrue(outputs.loss is not None						)









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




