
'''simple docstring'''







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					str													=	[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						lowercase_     :					str													=	True if '''large''' in model_name or '''huge''' in model_name else False
						lowercase_     :					Union[str, Any]													=	True if '''large''' in model_name or '''huge''' in model_name else False
						lowercase_     :					Optional[Any]													=	True if '''large''' in model_name or '''huge''' in model_name else False

						if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
												if "fl3" in model_name:
																		lowercase_     :					List[str]													=	[3, 3, 3, 3]
																		lowercase_     :					Tuple													=	[5, 5, 5, 5]
												elif "fl4" in model_name:
																		lowercase_     :					str													=	[4, 4, 4, 4]
																		lowercase_     :					Optional[Any]													=	[3, 3, 3, 3]

						if "tiny" in model_name or "small" in model_name or "base" in model_name:
												lowercase_     :					Union[str, Any]													=	[3, 3, 3, 3]
												if "lrf" in model_name:
																		lowercase_     :					Optional[int]													=	[3, 3, 3, 3]
												else:
																		lowercase_     :					Optional[int]													=	[2, 2, 2, 2]

						if "tiny" in model_name:
												lowercase_     :					Union[str, Any]													=	96
						elif "small" in model_name:
												lowercase_     :					Dict													=	96
						elif "base" in model_name:
												lowercase_     :					Optional[int]													=	128
						elif "large" in model_name:
												lowercase_     :					int													=	192
						elif "xlarge" in model_name:
												lowercase_     :					str													=	256
						elif "huge" in model_name:
												lowercase_     :					List[Any]													=	352

						# set label information
						lowercase_     :					Optional[Any]													=	'''huggingface/label-files'''
						if "large" in model_name or "huge" in model_name:
												lowercase_     :					Any													=	'''imagenet-22k-id2label.json'''
						else:
												lowercase_     :					List[str]													=	'''imagenet-1k-id2label.json'''

						lowercase_     :					Tuple													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''							)				,				'''r'''							)							)
						lowercase_     :					int													=	{int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
						lowercase_     :					Optional[Any]													=	{v: k for k, v in idalabel.items()}

						lowercase_     :					List[Any]													=	FocalNetConfig(
						    embed_dim=__SCREAMING_SNAKE_CASE				,				depths=__SCREAMING_SNAKE_CASE				,				focal_levels=__SCREAMING_SNAKE_CASE				,				focal_windows=__SCREAMING_SNAKE_CASE				,				use_conv_embed=__SCREAMING_SNAKE_CASE				,				idalabel=__SCREAMING_SNAKE_CASE				,				labelaid=__SCREAMING_SNAKE_CASE				,				use_post_layernorm=__SCREAMING_SNAKE_CASE				,				use_layerscale=__SCREAMING_SNAKE_CASE				,				)

						return config
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						if "patch_embed.proj" in name:
												lowercase_     :					Optional[Any]													=	name.replace('''patch_embed.proj'''				,				'''embeddings.patch_embeddings.projection'''							)
						if "patch_embed.norm" in name:
												lowercase_     :					int													=	name.replace('''patch_embed.norm'''				,				'''embeddings.norm'''							)
						if "layers" in name:
												lowercase_     :					Optional[Any]													=	'''encoder.''' + name
						if "encoder.layers" in name:
												lowercase_     :					Tuple													=	name.replace('''encoder.layers'''				,				'''encoder.stages'''							)
						if "downsample.proj" in name:
												lowercase_     :					Optional[int]													=	name.replace('''downsample.proj'''				,				'''downsample.projection'''							)
						if "blocks" in name:
												lowercase_     :					List[Any]													=	name.replace('''blocks'''				,				'''layers'''							)
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												lowercase_     :					Optional[Any]													=	name.replace('''modulation.f'''				,				'''modulation.projection_in'''							)
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												lowercase_     :					List[Any]													=	name.replace('''modulation.h'''				,				'''modulation.projection_context'''							)
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												lowercase_     :					Dict													=	name.replace('''modulation.proj'''				,				'''modulation.projection_out'''							)

						if name == "norm.weight":
												lowercase_     :					str													=	'''layernorm.weight'''
						if name == "norm.bias":
												lowercase_     :					List[str]													=	'''layernorm.bias'''

						if "head" in name:
												lowercase_     :					List[Any]													=	name.replace('''head'''				,				'''classifier'''							)
						else:
												lowercase_     :					Tuple													=	'''focalnet.''' + name

						return name
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			List[str]=False							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	{
						    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
						    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
						    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
						    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
						    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
						    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
						    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
						    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
						    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
						    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
						}
						# fmt: on

						lowercase_     :					List[str]													=	model_name_to_url[model_name]
						print('''Checkpoint URL: '''				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					int													=	torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,				map_location='''cpu'''							)['''model''']

						# rename keys
						for key in state_dict.copy().keys():
												lowercase_     :					List[Any]													=	state_dict.pop(__SCREAMING_SNAKE_CASE							)
												lowercase_     :					Any													=	val

						lowercase_     :					Tuple													=	get_focalnet_config(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					int													=	FocalNetForImageClassification(__SCREAMING_SNAKE_CASE							)
						model.eval()

						# load state dict
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						# verify conversion
						lowercase_     :					List[str]													=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

						lowercase_     :					List[str]													=	BitImageProcessor(
						    do_resize=__SCREAMING_SNAKE_CASE				,				size={'''shortest_edge''': 256}				,				resample=PILImageResampling.BILINEAR				,				do_center_crop=__SCREAMING_SNAKE_CASE				,				crop_size=224				,				do_normalize=__SCREAMING_SNAKE_CASE				,				image_mean=__SCREAMING_SNAKE_CASE				,				image_std=__SCREAMING_SNAKE_CASE				,				)
						lowercase_     :					List[str]													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE							).raw							)
						lowercase_     :					List[str]													=	processor(images=__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''							)

						lowercase_     :					Union[str, Any]													=	transforms.Compose(
						    [
						        transforms.Resize(256							),
						        transforms.CenterCrop(224							),
						        transforms.ToTensor(),
						        transforms.Normalize(mean=[0.485, 0.456, 0.406]				,				std=[0.229, 0.224, 0.225]							),
						    ]							)

						lowercase_     :					Any													=	image_transforms(__SCREAMING_SNAKE_CASE							).unsqueeze(0							)

						# verify pixel_values
						assert torch.allclose(inputs.pixel_values				,				__SCREAMING_SNAKE_CASE				,				atol=1E-4							)

						lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	outputs.logits.argmax(-1							).item()
						print('''Predicted class:'''				,				model.config.idalabel[predicted_class_idx]							)

						print('''First values of logits:'''				,				outputs.logits[0, :3]							)

						if model_name == "focalnet-tiny":
												lowercase_     :					Tuple													=	torch.tensor([0.2166, -0.4368, 0.2191]							)
						elif model_name == "focalnet-tiny-lrf":
												lowercase_     :					Dict													=	torch.tensor([1.1669, 0.0125, -0.1695]							)
						elif model_name == "focalnet-small":
												lowercase_     :					Optional[int]													=	torch.tensor([0.4917, -0.0430, 0.1341]							)
						elif model_name == "focalnet-small-lrf":
												lowercase_     :					Tuple													=	torch.tensor([-0.2588, -0.5342, -0.2331]							)
						elif model_name == "focalnet-base":
												lowercase_     :					Union[str, Any]													=	torch.tensor([-0.1655, -0.4090, -0.1730]							)
						elif model_name == "focalnet-base-lrf":
												lowercase_     :					Optional[int]													=	torch.tensor([0.5306, -0.0483, -0.3928]							)
						assert torch.allclose(outputs.logits[0, :3]				,				__SCREAMING_SNAKE_CASE				,				atol=1E-4							)
						print('''Looks ok!'''							)

						if pytorch_dump_folder_path is not None:
												print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''							)
												model.save_pretrained(__SCREAMING_SNAKE_CASE							)
												processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						if push_to_hub:
												print(F'''Pushing model and processor of {model_name} to the hub...'''							)
												model.push_to_hub(F'''{model_name}'''							)
												processor.push_to_hub(F'''{model_name}'''							)


if __name__ == "__main__":
							_lowercase			:  Optional[int]   							=	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="focalnet-tiny",
							    type=str,
							    help="Name of the FocalNet model you'd like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub",
							    action="store_true",
							    help="Whether to push the model and processor to the hub.",
							)

							_lowercase			:  Any   							=	parser.parse_args()
							convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''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 			lowerCAmelCase__       :






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=10    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=32 * 4    ,							__SCREAMING_SNAKE_CASE=32 * 6    ,							__SCREAMING_SNAKE_CASE=4    ,							__SCREAMING_SNAKE_CASE=32    ,							):


											"""simple docstring"""





											lowercase_     :					Tuple													=	parent
											lowercase_     :					Optional[int]													=	batch_size
											lowercase_     :					Dict													=	is_training
											lowercase_     :					Optional[Any]													=	use_auxiliary_loss
											lowercase_     :					Optional[Any]													=	num_queries
											lowercase_     :					Any													=	num_channels
											lowercase_     :					str													=	min_size
											lowercase_     :					str													=	max_size
											lowercase_     :					Optional[Any]													=	num_labels
											lowercase_     :					List[str]													=	mask_feature_size






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
											    __SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	torch.ones([self.batch_size, self.min_size, self.max_size]    ,							device=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,							device=__SCREAMING_SNAKE_CASE		) > 0.5
											).float()
											lowercase_     :					str													=	(torch.rand((self.batch_size, self.num_labels)    ,							device=__SCREAMING_SNAKE_CASE		) > 0.5).long()

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											return MaskFormerConfig.from_backbone_and_decoder_configs(
											    backbone_config=SwinConfig(
											        depths=[1, 1, 1, 1]    ,							)    ,							decoder_config=DetrConfig(
											        decoder_ffn_dim=1_28    ,							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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Optional[Any]													=	self.prepare_config_and_inputs()
											lowercase_     :					Any													=	{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
											return config, inputs_dict






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	output.encoder_hidden_states
											lowercase_     :					List[Any]													=	output.pixel_decoder_hidden_states
											lowercase_     :					int													=	output.transformer_decoder_hidden_states

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





											with torch.no_grad():
																	lowercase_     :					Any													=	MaskFormerModel(config=__SCREAMING_SNAKE_CASE		)
																	model.to(__SCREAMING_SNAKE_CASE		)
																	model.eval()

																	lowercase_     :					Dict													=	model(pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Union[str, Any]													=	model(__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)
											# 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(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Dict													=	MaskFormerForInstanceSegmentation(config=__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.eval()

											def comm_check_on_output(__SCREAMING_SNAKE_CASE		):
																	# 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_     :					Tuple													=	model(pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					str													=	model(__SCREAMING_SNAKE_CASE		)

																	comm_check_on_output(__SCREAMING_SNAKE_CASE		)

																	lowercase_     :					Tuple													=	model(
																	    pixel_values=__SCREAMING_SNAKE_CASE    ,							pixel_mask=__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		)

											comm_check_on_output(__SCREAMING_SNAKE_CASE		)

											self.parent.assertTrue(result.loss is not None		)
											self.parent.assertEqual(result.loss.shape    ,							torch.Size([1]		)		)







@require_torch
class 			lowerCAmelCase__       (  lowerCamelCase_   ,						lowerCamelCase_   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
					lowerCAmelCase_					       =					(
					    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	MaskFormerModelTester(self		)
											lowercase_     :					Tuple													=	ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE    ,							has_text_modality=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__SCREAMING_SNAKE_CASE		)






					@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer is not a generative model'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''MaskFormer does not use token embeddings'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					@require_torch_multi_gpu
					@unittest.skip(
					    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






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


											"""simple docstring"""





											pass






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					Tuple													=	self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_     :					str													=	['''pixel_values''']
																	self.assertListEqual(arg_names[:1]    ,							__SCREAMING_SNAKE_CASE		)






					@slow
					def      _snake_case   (       self		):


											"""simple docstring"""





											for model_name in ["facebook/maskformer-swin-small-coco"]:
																	lowercase_     :					Tuple													=	MaskFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	(self.model_tester.min_size,) * 2
											lowercase_     :					int													=	{
											    '''pixel_values''': torch.randn((2, 3, *size)    ,							device=__SCREAMING_SNAKE_CASE		),
											    '''mask_labels''': torch.randn((2, 10, *size)    ,							device=__SCREAMING_SNAKE_CASE		),
											    '''class_labels''': torch.zeros(2    ,							10    ,							device=__SCREAMING_SNAKE_CASE		).long(),
											}

											lowercase_     :					Any													=	MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	model(**__SCREAMING_SNAKE_CASE		)
											self.assertTrue(outputs.loss is not None		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					int													=	self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							output_hidden_states=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_						,			lowercase_     :					str													=	self.model_tester.prepare_config_and_inputs_for_common()

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											if not self.model_tester.is_training:
																	return
											# only MaskFormerForInstanceSegmentation has the loss
											lowercase_     :					Optional[Any]													=	self.all_model_classes[1]
											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Tuple													=	self.model_tester.prepare_config_and_inputs()

											lowercase_     :					Optional[int]													=	model_class(__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.train()

											lowercase_     :					Any													=	model(__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		).loss
											loss.backward()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	self.all_model_classes[1]
											lowercase_						,			lowercase_						,			lowercase_						,			lowercase_						,			lowercase_     :					Any													=	self.model_tester.prepare_config_and_inputs()
											lowercase_     :					Tuple													=	True
											lowercase_     :					Optional[Any]													=	True

											lowercase_     :					Tuple													=	model_class(__SCREAMING_SNAKE_CASE		)
											model.to(__SCREAMING_SNAKE_CASE		)
											model.train()

											lowercase_     :					List[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							mask_labels=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		)

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

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

											lowercase_     :					Tuple													=	outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=__SCREAMING_SNAKE_CASE		)

											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			:  int   							=	1E-4
def  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
						return image






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






					@cached_property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return (
											    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    if is_vision_available()
											    else None
											)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	self.default_image_processor
											lowercase_     :					Dict													=	prepare_img()
											lowercase_     :					Any													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Any													=	model(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	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(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)

											lowercase_     :					List[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(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)

											lowercase_     :					Dict													=	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(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					List[str]													=	self.default_image_processor
											lowercase_     :					Union[str, Any]													=	prepare_img()
											lowercase_     :					List[str]													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Dict													=	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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE		)
											# masks_queries_logits
											lowercase_     :					Union[str, Any]													=	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)    ,							)
											lowercase_     :					Any													=	[
											    [-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],
											]
											lowercase_     :					List[str]													=	torch.tensor(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)
											# class_queries_logits
											lowercase_     :					Optional[int]													=	outputs.class_queries_logits
											self.assertEqual(
											    class_queries_logits.shape    ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
											lowercase_     :					Any													=	torch.tensor(
											    [
											        [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
											        [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
											        [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
											    ]		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					Tuple													=	self.default_image_processor
											lowercase_     :					Any													=	prepare_img()
											lowercase_     :					Union[str, Any]													=	image_processor(__SCREAMING_SNAKE_CASE    ,							return_tensors='''pt'''		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, 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(__SCREAMING_SNAKE_CASE    ,							(1, 3, 8_00, 10_88)		)

											with torch.no_grad():
																	lowercase_     :					Union[str, Any]													=	model(**__SCREAMING_SNAKE_CASE		)
											# masks_queries_logits
											lowercase_     :					Union[str, Any]													=	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)    ,							)
											lowercase_     :					Optional[int]													=	[[-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]]
											lowercase_     :					Dict													=	torch.tensor(__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)
											# class_queries_logits
											lowercase_     :					Any													=	outputs.class_queries_logits
											self.assertEqual(
											    class_queries_logits.shape    ,							(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
											lowercase_     :					Union[str, Any]													=	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(__SCREAMING_SNAKE_CASE		)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=__SCREAMING_SNAKE_CASE		)		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					int													=	(
											    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
											    .to(__SCREAMING_SNAKE_CASE		)
											    .eval()
											)
											lowercase_     :					int													=	self.default_image_processor

											lowercase_     :					Optional[Any]													=	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'''    ,							)

											lowercase_     :					Optional[int]													=	inputs['''pixel_values'''].to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	[el.to(__SCREAMING_SNAKE_CASE		) for el in inputs['''mask_labels''']]
											lowercase_     :					int													=	[el.to(__SCREAMING_SNAKE_CASE		) for el in inputs['''class_labels''']]

											with torch.no_grad():
																	lowercase_     :					List[str]													=	model(**__SCREAMING_SNAKE_CASE		)

											self.assertTrue(outputs.loss is not None		)



