





"""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        _lowerCamelCase      :


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


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
								    __a		)

								UpperCamelCase   =	torch.ones([self.batch_size, self.min_size, self.max_size]   ,							device=__a		)

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

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


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	MaskaFormerConfig(
								    hidden_size=self.hidden_dim   ,							)
								UpperCamelCase   =	self.num_queries
								UpperCamelCase   =	self.num_labels

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

								UpperCamelCase   =	64
								UpperCamelCase   =	1_28
								UpperCamelCase   =	self.hidden_dim
								UpperCamelCase   =	self.hidden_dim
								UpperCamelCase   =	self.hidden_dim
								return config


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   =	{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
								return config, inputs_dict


						def     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	output.encoder_hidden_states
								UpperCamelCase   =	output.pixel_decoder_hidden_states
								UpperCamelCase   =	output.transformer_decoder_hidden_states

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


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a=False		) ->   Dict:
								with torch.no_grad():
										UpperCamelCase   =	MaskaFormerModel(config=__a		)
										model.to(__a		)
										model.eval()

										UpperCamelCase   =	model(pixel_values=__a   ,							pixel_mask=__a		)
										UpperCamelCase   =	model(__a   ,							output_hidden_states=__a		)

								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape   ,							(self.batch_size, self.num_queries, self.hidden_dim)   ,							)
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
								self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

								if output_hidden_states:
										self.check_output_hidden_state(__a   ,							__a		)




						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   int:
								UpperCamelCase   =	MaskaFormerForUniversalSegmentation(config=__a		)
								model.to(__a		)
								model.eval()

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

								with torch.no_grad():
										UpperCamelCase   =	model(pixel_values=__a   ,							pixel_mask=__a		)
										UpperCamelCase   =	model(__a		)

										comm_check_on_output(__a		)

										UpperCamelCase   =	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        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
						UpperCAmelCase_ =			{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

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


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	MaskaFormerModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							has_text_modality=__a		)


						def     snake_case_   (self		) ->   List[Any]:
								self.config_tester.run_common_tests()


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(__a   ,							**__a   ,							output_hidden_states=__a		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__a		)


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


						@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"		)
						def     snake_case_   (self		) ->   Tuple:
								pass


						@unittest.skip(reason="Mask2Former is not a generative model"		)
						def     snake_case_   (self		) ->   Any:
								pass


						@unittest.skip(reason="Mask2Former does not use token embeddings"		)
						def     snake_case_   (self		) ->   int:
								pass


						@require_torch_multi_gpu
						@unittest.skip(
						    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"		)
						def     snake_case_   (self		) ->   int:
								pass


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


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

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

										UpperCamelCase   =	["pixel_values"]
										self.assertListEqual(arg_names[:1]   ,							__a		)


						@slow
						def     snake_case_   (self		) ->   Optional[Any]:
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
										UpperCamelCase   =	MaskaFormerModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)


						def     snake_case_   (self		) ->   Tuple:
								UpperCamelCase   =	(self.model_tester.min_size,) * 2
								UpperCamelCase   =	{
								    "pixel_values": torch.randn((2, 3, *size)   ,							device=__a		),
								    "mask_labels": torch.randn((2, 10, *size)   ,							device=__a		),
								    "class_labels": torch.zeros(2   ,							10   ,							device=__a		).long(),
								}
								UpperCamelCase   =	self.model_tester.get_config()

								UpperCamelCase   =	MaskaFormerForUniversalSegmentation(__a		).to(__a		)
								UpperCamelCase   =	model(**__a		)
								self.assertTrue(outputs.loss is not None		)


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(__a   ,							**__a   ,							output_hidden_states=__a		)


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()

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


						def     snake_case_   (self		) ->   Any:
								if not self.model_tester.is_training:
										return

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

								UpperCamelCase   =	model_class(__a		)
								model.to(__a		)
								model.train()

								UpperCamelCase   =	model(__a   ,							mask_labels=__a   ,							class_labels=__a		).loss
								loss.backward()




						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	self.all_model_classes[1]
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								UpperCamelCase   =	True
								UpperCamelCase   =	True

								UpperCamelCase   =	model_class(__a		).to(__a		)
								model.train()

								UpperCamelCase   =	model(__a   ,							mask_labels=__a   ,							class_labels=__a		)

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

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

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

								UpperCamelCase   =	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		)



lowerCAmelCase__     			=			1E-4


def       a__					(					):




		"""simple docstring"""


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






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


						@cached_property
						def     snake_case_   (self		) ->   Any:
								return "facebook/mask2former-swin-small-coco-instance"


						@cached_property
						def     snake_case_   (self		) ->   Tuple:
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   =	MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(__a		)
								UpperCamelCase   =	self.default_image_processor
								UpperCamelCase   =	prepare_img()
								UpperCamelCase   =	image_processor(__a   ,							return_tensors="pt"		).to(__a		)
								UpperCamelCase   =	inputs["pixel_values"].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
								# check size
								self.assertEqual(__a   ,							(1, 3, 3_84, 3_84)		)

								with torch.no_grad():
										UpperCamelCase   =	model(**__a		)

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

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

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


						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(__a		).eval()
								UpperCamelCase   =	self.default_image_processor
								UpperCamelCase   =	prepare_img()
								UpperCamelCase   =	image_processor(__a   ,							return_tensors="pt"		).to(__a		)
								UpperCamelCase   =	inputs["pixel_values"].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
								# check size
								self.assertEqual(__a   ,							(1, 3, 3_84, 3_84)		)

								with torch.no_grad():
										UpperCamelCase   =	model(**__a		)
								# masks_queries_logits
								UpperCamelCase   =	outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape   ,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
								UpperCamelCase   =	[
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								UpperCamelCase   =	torch.tensor(__a		).to(__a		)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,							__a   ,							atol=__a		)		)
								# class_queries_logits
								UpperCamelCase   =	outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape   ,							(1, model.config.num_queries, model.config.num_labels + 1)		)
								UpperCamelCase   =	torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]		).to(__a		)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,							__a   ,							atol=__a		)		)




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(__a		).eval()
								UpperCamelCase   =	self.default_image_processor

								UpperCamelCase   =	image_processor(
								    [np.zeros((3, 8_00, 13_33)		), np.zeros((3, 8_00, 13_33)		)]   ,							segmentation_maps=[np.zeros((3_84, 3_84)		).astype(np.floataa		), np.zeros((3_84, 3_84)		).astype(np.floataa		)]   ,							return_tensors="pt"   ,							)

								UpperCamelCase   =	inputs["pixel_values"].to(__a		)
								UpperCamelCase   =	[el.to(__a		) for el in inputs["mask_labels"]]
								UpperCamelCase   =	[el.to(__a		) for el in inputs["class_labels"]]

								with torch.no_grad():
										UpperCamelCase   =	model(**__a		)

								self.assertTrue(outputs.loss is not None		)













"""simple docstring"""





import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCAmelCase__     			=			HUGGINGFACE_HUB_CACHE


lowerCAmelCase__     			=			'''config.json'''
lowerCAmelCase__     			=			'''diffusion_pytorch_model.bin'''
lowerCAmelCase__     			=			'''diffusion_flax_model.msgpack'''
lowerCAmelCase__     			=			'''model.onnx'''
lowerCAmelCase__     			=			'''diffusion_pytorch_model.safetensors'''
lowerCAmelCase__     			=			'''weights.pb'''
lowerCAmelCase__     			=			'''https://huggingface.co'''
lowerCAmelCase__     			=			default_cache_path
lowerCAmelCase__     			=			'''diffusers_modules'''
lowerCAmelCase__     			=			os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
lowerCAmelCase__     			=			['''fp16''', '''non-ema''']
lowerCAmelCase__     			=			'''.self_attn'''






