



"""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 			snake_case_						:






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






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
									    a_      )

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

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

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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     MaskaFormerConfig(
									    hidden_size=self.hidden_dim			,     )
									a_     :      int 						=     self.num_queries
									a_     :      Union[str, Any] 						=     self.num_labels

									a_     :      int 						=     [1, 1, 1, 1]
									a_     :      Any 						=     self.num_channels

									a_     :      str 						=     6_4
									a_     :      Optional[int] 						=     1_2_8
									a_     :      Optional[Any] 						=     self.hidden_dim
									a_     :      int 						=     self.hidden_dim
									a_     :      Dict 						=     self.hidden_dim
									return config






							def snake_case_  (   self      ):
									a_						,				a_						,				a_						,				a_						,				a_     :      Dict 						=     self.prepare_config_and_inputs()
									a_     :      Optional[Any] 						=     {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
									return config, inputs_dict






							def snake_case_  (   self			,     a_			,     a_      ):
									a_     :      Any 						=     output.encoder_hidden_states
									a_     :      Any 						=     output.pixel_decoder_hidden_states
									a_     :      Optional[int] 						=     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      ):
									with torch.no_grad():
											a_     :      Union[str, Any] 						=     MaskaFormerModel(config=a_      )
											model.to(a_      )
											model.eval()

											a_     :      str 						=     model(pixel_values=a_			,     pixel_mask=a_      )
											a_     :      Tuple 						=     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_      ):
									a_     :      int 						=     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():
											a_     :      Union[str, Any] 						=     model(pixel_values=a_			,     pixel_mask=a_      )
											a_     :      str 						=     model(a_      )

											comm_check_on_output(a_      )

											a_     :      Optional[int] 						=     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 			snake_case_						(						a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							__lowerCAmelCase    					=					{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     MaskaFormerModelTester(self      )
									a_     :      int 						=     ConfigTester(self			,     config_class=a_			,     has_text_modality=a_      )






							def snake_case_  (   self      ):
									self.config_tester.run_common_tests()






							def snake_case_  (   self      ):
									a_						,				a_     :      str 						=     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      ):
									a_     :      Dict 						=     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      ):
									pass






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






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






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






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






							def snake_case_  (   self      ):
									a_						,				a_     :      List[str] 						=     self.model_tester.prepare_config_and_inputs_for_common()

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

											a_     :      str 						=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     a_      )






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






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     (self.model_tester.min_size,) * 2
									a_     :      Optional[int] 						=     {
									    "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_     :      Tuple 						=     self.model_tester.get_config()

									a_     :      str 						=     MaskaFormerForUniversalSegmentation(a_      ).to(a_      )
									a_     :      str 						=     model(**a_      )
									self.assertTrue(outputs.loss is not None      )






							def snake_case_  (   self      ):
									a_						,				a_     :      List[str] 						=     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      ):
									a_						,				a_     :      str 						=     self.model_tester.prepare_config_and_inputs_for_common()

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






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

									a_     :      Union[str, Any] 						=     self.all_model_classes[1]
									a_						,				a_						,				a_						,				a_						,				a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()

									a_     :      Tuple 						=     model_class(a_      )
									model.to(a_      )
									model.train()

									a_     :      List[Any] 						=     model(a_			,     mask_labels=a_			,     class_labels=a_      ).loss
									loss.backward()






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.all_model_classes[1]
									a_						,				a_						,				a_						,				a_						,				a_     :      Union[str, Any] 						=     self.model_tester.prepare_config_and_inputs()
									a_     :      List[Any] 						=     True
									a_     :      str 						=     True

									a_     :      List[Any] 						=     model_class(a_      ).to(a_      )
									model.train()

									a_     :      Dict 						=     model(a_			,     mask_labels=a_			,     class_labels=a_      )

									a_     :      Tuple 						=     outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									a_     :      List[Any] 						=     outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									a_     :      Any 						=     outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									a_     :      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      )

SCREAMING_SNAKE_CASE_				= 1E-4



def        lowerCAmelCase_						(							)			->      str:
		a_     :      List[str] 						=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




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






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






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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(a_      )
									a_     :      Union[str, Any] 						=     self.default_image_processor
									a_     :      str 						=     prepare_img()
									a_     :      Optional[int] 						=     image_processor(a_			,     return_tensors="pt"      ).to(a_      )
									a_     :      str 						=     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, 3_8_4, 3_8_4)      )

									with torch.no_grad():
											a_     :      int 						=     model(**a_      )

									a_     :      Optional[Any] 						=     torch.tensor(
									    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]      ).to(a_      )
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]			,     a_			,     atol=a_      )      )

									a_     :      Tuple 						=     torch.tensor(
									    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]      ).to(a_      )
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,     a_			,     atol=a_      )      )

									a_     :      Any 						=     torch.tensor(
									    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]      ).to(a_      )
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,     a_			,     atol=a_      )      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(a_      ).eval()
									a_     :      str 						=     self.default_image_processor
									a_     :      int 						=     prepare_img()
									a_     :      Dict 						=     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, 3_8_4, 3_8_4)      )

									with torch.no_grad():
											a_     :      List[Any] 						=     model(**a_      )
									# masks_queries_logits
									a_     :      Dict 						=     outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape			,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
									a_     :      Any 						=     [
									    [-8.7_839, -9.0_056, -8.8_121],
									    [-7.4_104, -7.0_313, -6.5_401],
									    [-6.6_105, -6.3_427, -6.4_675],
									]
									a_     :      List[Any] 						=     torch.tensor(a_      ).to(a_      )
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,     a_			,     atol=a_      )      )
									# class_queries_logits
									a_     :      int 						=     outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape			,     (1, model.config.num_queries, model.config.num_labels + 1)      )
									a_     :      List[Any] 						=     torch.tensor(
									    [
									        [1.8_324, -8.0_835, -4.1_922],
									        [0.8_450, -9.0_050, -3.6_053],
									        [0.3_045, -7.7_293, -3.0_275],
									    ]      ).to(a_      )
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,     a_			,     atol=a_      )      )






							def snake_case_  (   self      ):
									a_     :      int 						=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(a_      ).eval()
									a_     :      Dict 						=     self.default_image_processor

									a_     :      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"			,     )

									a_     :      List[Any] 						=     inputs["pixel_values"].to(a_      )
									a_     :      Optional[int] 						=     [el.to(a_      ) for el in inputs["mask_labels"]]
									a_     :      List[Any] 						=     [el.to(a_      ) for el in inputs["class_labels"]]

									with torch.no_grad():
											a_     :      int 						=     model(**a_      )

									self.assertTrue(outputs.loss is not None      )




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2