






'''simple docstring'''







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 _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Dict=2			,							UpperCamelCase__:				Optional[int]=True			,							UpperCamelCase__:				Any=False			,							UpperCamelCase__:				Dict=10			,							UpperCamelCase__:				List[str]=3			,							UpperCamelCase__:				int=32 * 4			,							UpperCamelCase__:				List[str]=32 * 6			,							UpperCamelCase__:				Optional[int]=4			,							UpperCamelCase__:				int=32			,							):
														lowerCamelCase__					:	List[str]         =       parent
														lowerCamelCase__					:	List[Any]         =       batch_size
														lowerCamelCase__					:	Union[str, Any]         =       is_training
														lowerCamelCase__					:	List[Any]         =       use_auxiliary_loss
														lowerCamelCase__					:	List[Any]         =       num_queries
														lowerCamelCase__					:	List[str]         =       num_channels
														lowerCamelCase__					:	Optional[Any]         =       min_size
														lowerCamelCase__					:	List[Any]         =       max_size
														lowerCamelCase__					:	int         =       num_labels
														lowerCamelCase__					:	Optional[int]         =       mask_feature_size




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Any         =       floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]     ).to(
														    UpperCamelCase__     )

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

														lowerCamelCase__					:	Union[str, Any]         =       (
														    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]			,							device=UpperCamelCase__     ) > 0.5
														).float()
														lowerCamelCase__					:	Tuple         =       (torch.rand((self.batch_size, self.num_labels)			,							device=UpperCamelCase__     ) > 0.5).long()

														lowerCamelCase__					:	Tuple         =       self.get_config()
														return config, pixel_values, pixel_mask, mask_labels, class_labels




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return MaskFormerConfig.from_backbone_and_decoder_configs(
														    backbone_config=SwinConfig(
														        depths=[1, 1, 1, 1]			,							)			,							decoder_config=DetrConfig(
														        decoder_ffn_dim=128			,							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 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       self.prepare_config_and_inputs()
														lowerCamelCase__					:	Tuple         =       {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
														return config, inputs_dict




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       output.encoder_hidden_states
														lowerCamelCase__					:	List[Any]         =       output.pixel_decoder_hidden_states
														lowerCamelCase__					:	str         =       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 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Optional[int]=False     ):
														with torch.no_grad():
																					lowerCamelCase__					:	Optional[int]         =       MaskFormerModel(config=UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.eval()

																					lowerCamelCase__					:	Dict         =       model(pixel_values=UpperCamelCase__			,							pixel_mask=UpperCamelCase__     )
																					lowerCamelCase__					:	Optional[int]         =       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 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	List[Any]         =       MaskFormerForInstanceSegmentation(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()

														def comm_check_on_output(UpperCamelCase__:				List[str]     ):
																					# 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():
																					lowerCamelCase__					:	Optional[int]         =       model(pixel_values=UpperCamelCase__			,							pixel_mask=UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       model(UpperCamelCase__     )

																					comm_check_on_output(UpperCamelCase__     )

																					lowerCamelCase__					:	Optional[int]         =       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 _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							a				      =       (
							    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Any         =       MaskFormerModelTester(self     )
														lowerCamelCase__					:	List[Any]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														self.config_tester.run_common_tests()




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	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 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	List[str]         =       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 		lowerCamelCase_				(							self:				str     ):
														pass




							@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""     )
							def 		lowerCamelCase_				(							self:				str     ):
														pass




							@unittest.skip(reason="""MaskFormer is not a generative model"""     )
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														pass




							@unittest.skip(reason="""MaskFormer does not use token embeddings"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




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




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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__					:	Union[str, Any]         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														for model_name in ["facebook/maskformer-swin-small-coco"]:
																					lowerCamelCase__					:	Any         =       MaskFormerModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       (self.model_tester.min_size,) * 2
														lowerCamelCase__					:	Dict         =       {
														    """pixel_values""": torch.randn((2, 3, *size)			,							device=UpperCamelCase__     ),
														    """mask_labels""": torch.randn((2, 10, *size)			,							device=UpperCamelCase__     ),
														    """class_labels""": torch.zeros(2			,							10			,							device=UpperCamelCase__     ).long(),
														}

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




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskformer_model(UpperCamelCase__			,							**UpperCamelCase__			,							output_hidden_states=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	str         =       model_class(UpperCamelCase__     ).to(UpperCamelCase__     )
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__			,							output_attentions=UpperCamelCase__     )
																					self.assertTrue(outputs.attentions is not None     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														if not self.model_tester.is_training:
																					return
														# only MaskFormerForInstanceSegmentation has the loss
														lowerCamelCase__					:	Optional[Any]         =       self.all_model_classes[1]
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs()

														lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.train()

														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__			,							mask_labels=UpperCamelCase__			,							class_labels=UpperCamelCase__     ).loss
														loss.backward()




							def 		lowerCamelCase_				(							self:				str     ):
														# only MaskFormerForInstanceSegmentation has the loss
														lowerCamelCase__					:	List[str]         =       self.all_model_classes[1]
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														lowerCamelCase__					:	List[str]         =       True
														lowerCamelCase__					:	Tuple         =       True

														lowerCamelCase__					:	int         =       model_class(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.train()

														lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__			,							mask_labels=UpperCamelCase__			,							class_labels=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       outputs.encoder_hidden_states[0]
														encoder_hidden_states.retain_grad()

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

														lowerCamelCase__					:	str         =       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     )



_A	:			Optional[int]         =1e-4





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[int]:
							lowerCamelCase__					:	Any         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




							@cached_property
							def 		lowerCamelCase_				(							self:				Dict     ):
														return (
														    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""     )
														    if is_vision_available()
														    else None
														)




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	str         =       self.default_image_processor
														lowerCamelCase__					:	Dict         =       prepare_img()
														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       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(UpperCamelCase__			,							(1, 3, 800, 1_088)     )

														with torch.no_grad():
																					lowerCamelCase__					:	List[str]         =       model(**UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       torch.tensor(
														    [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]     ).to(UpperCamelCase__     )
														self.assertTrue(
														    torch.allclose(
														        outputs.encoder_last_hidden_state[0, 0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )

														lowerCamelCase__					:	Optional[Any]         =       torch.tensor(
														    [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]     ).to(UpperCamelCase__     )
														self.assertTrue(
														    torch.allclose(
														        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )

														lowerCamelCase__					:	List[str]         =       torch.tensor(
														    [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]     ).to(UpperCamelCase__     )
														self.assertTrue(
														    torch.allclose(
														        outputs.transformer_decoder_last_hidden_state[0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       (
														    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""     )
														    .to(UpperCamelCase__     )
														    .eval()
														)
														lowerCamelCase__					:	Optional[int]         =       self.default_image_processor
														lowerCamelCase__					:	List[Any]         =       prepare_img()
														lowerCamelCase__					:	Tuple         =       image_processor(UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       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(UpperCamelCase__			,							(1, 3, 800, 1_088)     )

														with torch.no_grad():
																					lowerCamelCase__					:	Dict         =       model(**UpperCamelCase__     )
														# masks_queries_logits
														lowerCamelCase__					:	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)			,							)
														lowerCamelCase__					:	int         =       [
														    [-1.3_737_124, -1.7_724_937, -1.9_364_233],
														    [-1.5_977_281, -1.9_867_939, -2.1_523_695],
														    [-1.5_795_398, -1.9_269_832, -2.093_942],
														]
														lowerCamelCase__					:	int         =       torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )
														# class_queries_logits
														lowerCamelCase__					:	Any         =       outputs.class_queries_logits
														self.assertEqual(
														    class_queries_logits.shape			,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
														lowerCamelCase__					:	Tuple         =       torch.tensor(
														    [
														        [1.6_512e00, -5.2_572e00, -3.3_519e00],
														        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
														        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
														    ]     ).to(UpperCamelCase__     )
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	Optional[int]         =       (
														    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""     )
														    .to(UpperCamelCase__     )
														    .eval()
														)
														lowerCamelCase__					:	str         =       self.default_image_processor
														lowerCamelCase__					:	List[str]         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       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(UpperCamelCase__			,							(1, 3, 800, 1_088)     )

														with torch.no_grad():
																					lowerCamelCase__					:	Any         =       model(**UpperCamelCase__     )
														# masks_queries_logits
														lowerCamelCase__					:	int         =       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)			,							)
														lowerCamelCase__					:	Optional[Any]         =       [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
														lowerCamelCase__					:	List[Any]         =       torch.tensor(UpperCamelCase__     ).to(UpperCamelCase__     )
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )
														# class_queries_logits
														lowerCamelCase__					:	Dict         =       outputs.class_queries_logits
														self.assertEqual(
														    class_queries_logits.shape			,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)     )
														lowerCamelCase__					:	Optional[int]         =       torch.tensor(
														    [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]     ).to(UpperCamelCase__     )
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]			,							UpperCamelCase__			,							atol=UpperCamelCase__     )     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Optional[int]         =       (
														    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""     )
														    .to(UpperCamelCase__     )
														    .eval()
														)
														lowerCamelCase__					:	Any         =       self.default_image_processor

														lowerCamelCase__					:	Optional[Any]         =       image_processor(
														    [np.zeros((3, 800, 1_333)     ), np.zeros((3, 800, 1_333)     )]			,							segmentation_maps=[np.zeros((384, 384)     ).astype(np.floataa     ), np.zeros((384, 384)     ).astype(np.floataa     )]			,							return_tensors="""pt"""			,							)

														lowerCamelCase__					:	Optional[int]         =       inputs["""pixel_values"""].to(UpperCamelCase__     )
														lowerCamelCase__					:	int         =       [el.to(UpperCamelCase__     ) for el in inputs["""mask_labels"""]]
														lowerCamelCase__					:	int         =       [el.to(UpperCamelCase__     ) for el in inputs["""class_labels"""]]

														with torch.no_grad():
																					lowerCamelCase__					:	List[Any]         =       model(**UpperCamelCase__     )

														self.assertTrue(outputs.loss is not None     )



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__A				=logging.get_logger(__name__)  # pylint: disable=invalid-name

__A				='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''




@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					super().__init__()

					self.register_modules(
					    prior=lowercase						,  image_encoder=lowercase						,  image_processor=lowercase						,  scheduler=lowercase						,  renderer=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if latents is None:
									lowerCamelCase_						=			randn_tensor(lowercase						,  generator=lowercase						,  device=lowercase						,  dtype=lowercase )
					else:
									if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
									lowerCamelCase_						=			latents.to(lowercase )

					lowerCamelCase_						=			latents * scheduler.init_noise_sigma
					return latents



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=0 )				->       int:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device(f'cuda:{gpu_id}' )

					lowerCamelCase_						=			[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					if self.device != torch.device("meta" ) or not hasattr(self.image_encoder						,  "_hf_hook" ):
									return self.device
					for module in self.image_encoder.modules():
									if (
									    hasattr(lowercase						,  "_hf_hook" )
									    and hasattr(module._hf_hook						,  "execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					if isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  torch.Tensor ):
									lowerCamelCase_						=			torch.cat(lowercase						,  axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase						,  axis=0 )

					if not isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			self.image_processor(lowercase						,  return_tensors="pt" ).pixel_values[0].unsqueeze(0 )

					lowerCamelCase_						=			image.to(dtype=self.image_encoder.dtype						,  device=lowercase )

					lowerCamelCase_						=			self.image_encoder(lowercase )["last_hidden_state"]
					lowerCamelCase_						=			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCamelCase_						=			image_embeds.repeat_interleave(lowercase						,  dim=0 )

					if do_classifier_free_guidance:
									lowerCamelCase_						=			torch.zeros_like(lowercase )

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCamelCase_						=			torch.cat([negative_image_embeds, image_embeds] )

					return image_embeds



	@torch.no_grad()
	@replace_example_docstring(lowercase )
	def __call__(    self						,  lowercase						,  lowercase = 1						,  lowercase = 25						,  lowercase = None						,  lowercase = None						,  lowercase = 4.0						,  lowercase = 64						,  lowercase = "pil"						,  lowercase = True						,  )				->       Union[str, Any]:

					if isinstance(lowercase						,  PIL.Image.Image ):
									lowerCamelCase_						=			1
					elif isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			image.shape[0]
					elif isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  (torch.Tensor, PIL.Image.Image) ):
									lowerCamelCase_						=			len(lowercase )
					else:
									raise ValueError(
									    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase )}' )

					lowerCamelCase_						=			self._execution_device

					lowerCamelCase_						=			batch_size * num_images_per_prompt

					lowerCamelCase_						=			guidance_scale > 1.0
					lowerCamelCase_						=			self._encode_image(lowercase						,  lowercase						,  lowercase						,  lowercase )

					# prior

					self.scheduler.set_timesteps(lowercase						,  device=lowercase )
					lowerCamelCase_						=			self.scheduler.timesteps

					lowerCamelCase_						=			self.prior.config.num_embeddings
					lowerCamelCase_						=			self.prior.config.embedding_dim

					lowerCamelCase_						=			self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)						,  image_embeds.dtype						,  lowercase						,  lowercase						,  lowercase						,  self.scheduler						,  )

					# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
					lowerCamelCase_						=			latents.reshape(latents.shape[0]						,  lowercase						,  lowercase )

					for i, t in enumerate(self.progress_bar(lowercase ) ):
									# expand the latents if we are doing classifier free guidance
									lowerCamelCase_						=			torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
									lowerCamelCase_						=			self.scheduler.scale_model_input(lowercase						,  lowercase )

									lowerCamelCase_						=			self.prior(
									    lowercase						,  timestep=lowercase						,  proj_embedding=lowercase						,  ).predicted_image_embedding

									# remove the variance
									lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.split(
									    scaled_model_input.shape[2]						,  dim=2 )  # batch_size, num_embeddings, embedding_dim

									if do_classifier_free_guidance is not None:
													lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.chunk(2 )
													lowerCamelCase_						=			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									lowerCamelCase_						=			self.scheduler.step(
									    lowercase						,  timestep=lowercase						,  sample=lowercase						,  ).prev_sample

					if output_type == "latent":
									return ShapEPipelineOutput(images=lowercase )

					lowerCamelCase_						=			[]
					for i, latent in enumerate(lowercase ):
									print()
									lowerCamelCase_						=			self.renderer.decode(
									    latent[None, :]						,  lowercase						,  size=lowercase						,  ray_batch_size=4096						,  n_coarse_samples=64						,  n_fine_samples=128						,  )

									images.append(lowercase )

					lowerCamelCase_						=			torch.stack(lowercase )

					if output_type not in ["np", "pil"]:
									raise ValueError(f'Only the output types `pil` and `np` are supported not output_type={output_type}' )

					lowerCamelCase_						=			images.cpu().numpy()

					if output_type == "pil":
									lowerCamelCase_						=			[self.numpy_to_pil(lowercase ) for image in images]

					# Offload last model to CPU
					if hasattr(self						,  "final_offload_hook" ) and self.final_offload_hook is not None:
									self.final_offload_hook.offload()

					if not return_dict:
									return (images,)

					return ShapEPipelineOutput(images=lowercase )


