
import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[int]         =							3_84
				if "tiny" in model_name:
								A__  :	Any         =							[3, 3, 9, 3]
								A__  :	Union[str, Any]         =							[96, 1_92, 3_84, 7_68]
				if "small" in model_name:
								A__  :	int         =							[3, 3, 27, 3]
								A__  :	Dict         =							[96, 1_92, 3_84, 7_68]
				if "base" in model_name:
								A__  :	Tuple         =							[3, 3, 27, 3]
								A__  :	List[Any]         =							[1_28, 2_56, 5_12, 10_24]
								A__  :	Union[str, Any]         =							5_12
				if "large" in model_name:
								A__  :	int         =							[3, 3, 27, 3]
								A__  :	Union[str, Any]         =							[1_92, 3_84, 7_68, 15_36]
								A__  :	Optional[Any]         =							7_68
				if "xlarge" in model_name:
								A__  :	Dict         =							[3, 3, 27, 3]
								A__  :	str         =							[2_56, 5_12, 10_24, 20_48]
								A__  :	List[str]         =							10_24

				# set label information
				A__  :	Any         =							1_50
				A__  :	Optional[int]         =							'''huggingface/label-files'''
				A__  :	Union[str, Any]         =							'''ade20k-id2label.json'''
				A__  :	List[Any]         =							json.load(open(hf_hub_download(__UpperCamelCase							,						__UpperCamelCase							,						repo_type='''dataset'''       )							,						'''r'''       )       )
				A__  :	Any         =							{int(__UpperCamelCase       ): v for k, v in idalabel.items()}
				A__  :	Any         =							{v: k for k, v in idalabel.items()}

				A__  :	int         =							ConvNextConfig(
				    depths=__UpperCamelCase							,						hidden_sizes=__UpperCamelCase							,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
				A__  :	int         =							UperNetConfig(
				    backbone_config=__UpperCamelCase							,						auxiliary_in_channels=__UpperCamelCase							,						num_labels=__UpperCamelCase							,						idalabel=__UpperCamelCase							,						labelaid=__UpperCamelCase							,						)

				return config

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[Any]         =							[]

				# fmt: off
				# stem
				rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')       )
				rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')       )
				rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')       )
				rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.bias''')       )
				# stages
				for i in range(len(config.backbone_config.depths       )       ):
								for j in range(config.backbone_config.depths[i]       ):
												rename_keys.append((F"backbone.stages.{i}.{j}.gamma", F"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.weight", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.bias", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.norm.weight", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.norm.bias", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")       )
												rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")       )
								if i > 0:
												rename_keys.append((F"backbone.downsample_layers.{i}.0.weight", F"backbone.encoder.stages.{i}.downsampling_layer.0.weight")       )
												rename_keys.append((F"backbone.downsample_layers.{i}.0.bias", F"backbone.encoder.stages.{i}.downsampling_layer.0.bias")       )
												rename_keys.append((F"backbone.downsample_layers.{i}.1.weight", F"backbone.encoder.stages.{i}.downsampling_layer.1.weight")       )
												rename_keys.append((F"backbone.downsample_layers.{i}.1.bias", F"backbone.encoder.stages.{i}.downsampling_layer.1.bias")       )

								rename_keys.append((F"backbone.norm{i}.weight", F"backbone.hidden_states_norms.stage{i+1}.weight")       )
								rename_keys.append((F"backbone.norm{i}.bias", F"backbone.hidden_states_norms.stage{i+1}.bias")       )

				# decode head
				rename_keys.extend(
				    [
				        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
				        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
				        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
				        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
				    ]       )
				# fmt: on

				return rename_keys

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Optional[int]       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[Any]         =							dct.pop(__UpperCamelCase       )
				A__  :	Dict         =							val

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						str							,						__UpperCamelCase :						Optional[Any]       ) ->       Tuple:

				"""simple docstring"""







				A__  :	int         =							{
				    '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
				    '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
				    '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
				    '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
				    '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
				}
				A__  :	Dict         =							model_name_to_url[model_name]
				A__  :	str         =							torch.hub.load_state_dict_from_url(__UpperCamelCase							,						map_location='''cpu'''       )['''state_dict''']

				A__  :	Dict         =							get_upernet_config(__UpperCamelCase       )
				A__  :	Union[str, Any]         =							UperNetForSemanticSegmentation(__UpperCamelCase       )
				model.eval()

				# replace "bn" => "batch_norm"
				for key in state_dict.copy().keys():
								A__  :	int         =							state_dict.pop(__UpperCamelCase       )
								if "bn" in key:
												A__  :	Union[str, Any]         =							key.replace('''bn'''							,						'''batch_norm'''       )
								A__  :	Union[str, Any]         =							val

				# rename keys
				A__  :	List[str]         =							create_rename_keys(__UpperCamelCase       )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				model.load_state_dict(__UpperCamelCase       )

				# verify on image
				A__  :	List[Any]         =							'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
				A__  :	int         =							Image.open(requests.get(__UpperCamelCase							,						stream=__UpperCamelCase       ).raw       ).convert('''RGB'''       )

				A__  :	Union[str, Any]         =							SegformerImageProcessor()
				A__  :	Union[str, Any]         =							processor(__UpperCamelCase							,						return_tensors='''pt'''       ).pixel_values

				with torch.no_grad():
								A__  :	Dict         =							model(__UpperCamelCase       )

				if model_name == "upernet-convnext-tiny":
								A__  :	Union[str, Any]         =							torch.tensor(
								    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]       )
				elif model_name == "upernet-convnext-small":
								A__  :	List[str]         =							torch.tensor(
								    [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]       )
				elif model_name == "upernet-convnext-base":
								A__  :	Any         =							torch.tensor(
								    [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]       )
				elif model_name == "upernet-convnext-large":
								A__  :	Union[str, Any]         =							torch.tensor(
								    [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]       )
				elif model_name == "upernet-convnext-xlarge":
								A__  :	List[Any]         =							torch.tensor(
								    [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]       )
				print('''Logits:'''							,						outputs.logits[0, 0, :3, :3]       )
				assert torch.allclose(outputs.logits[0, 0, :3, :3]							,						__UpperCamelCase							,						atol=1e-4       )
				print('''Looks ok!'''       )

				if pytorch_dump_folder_path is not None:
								print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
								model.save_pretrained(__UpperCamelCase       )
								print(F"Saving processor to {pytorch_dump_folder_path}"       )
								processor.save_pretrained(__UpperCamelCase       )

				if push_to_hub:
								print(F"Pushing model and processor for {model_name} to hub"       )
								model.push_to_hub(F"openmmlab/{model_name}"       )
								processor.push_to_hub(F"openmmlab/{model_name}"       )


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      int          =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='upernet-convnext-tiny',
					    type=str,
					    choices=[f"""upernet-convnext-{size}""" for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
					    help='Name of the ConvNext UperNet 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 or not to push the converted model to the 🤗 hub.'
					)

					_SCREAMING_SNAKE_CASE    :      Tuple          =			parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=13				,       UpperCamelCase__=[30, 30]				,       UpperCamelCase__=2				,       UpperCamelCase__=3				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=32				,       UpperCamelCase__=5				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=10				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=None				,       UpperCamelCase__=8				,       UpperCamelCase__=10				,       ):
					A__  :	Optional[int]         =							parent
					A__  :	List[Any]         =							batch_size
					A__  :	Dict         =							image_size
					A__  :	Any         =							patch_size
					A__  :	Dict         =							num_channels
					A__  :	List[Any]         =							is_training
					A__  :	int         =							use_labels
					A__  :	Any         =							hidden_size
					A__  :	List[str]         =							num_hidden_layers
					A__  :	Optional[int]         =							num_attention_heads
					A__  :	Optional[Any]         =							intermediate_size
					A__  :	str         =							hidden_act
					A__  :	str         =							hidden_dropout_prob
					A__  :	Optional[int]         =							attention_probs_dropout_prob
					A__  :	Optional[int]         =							type_sequence_label_size
					A__  :	Any         =							initializer_range
					A__  :	Optional[int]         =							num_labels
					A__  :	Union[str, Any]         =							scope
					A__  :	Union[str, Any]         =							n_targets
					A__  :	Dict         =							num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					A__  :	int         =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
					A__  :	List[str]         =							num_patches + 1 + self.num_detection_tokens





	def   __snake_case				(			self		):
					A__  :	Dict         =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]		)

					A__  :	int         =							None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									A__  :	Tuple         =							[]
									for i in range(self.batch_size		):
													A__  :	List[Any]         =							{}
													A__  :	Tuple         =							torch.randint(
													    high=self.num_labels				,       size=(self.n_targets,)				,       device=UpperCamelCase__		)
													A__  :	Any         =							torch.rand(self.n_targets				,       4				,       device=UpperCamelCase__		)
													labels.append(UpperCamelCase__		)

					A__  :	Union[str, Any]         =							self.get_config()

					return config, pixel_values, labels





	def   __snake_case				(			self		):
					return YolosConfig(
					    image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=UpperCamelCase__				,       initializer_range=self.initializer_range				,       num_detection_tokens=self.num_detection_tokens				,       num_labels=self.num_labels				,       )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							YolosModel(config=UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()
					A__  :	Optional[Any]         =							model(UpperCamelCase__		)
					self.parent.assertEqual(
					    result.last_hidden_state.shape				,       (self.batch_size, self.expected_seq_len, self.hidden_size)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Any         =							YolosForObjectDetection(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.eval()

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__		)
					A__  :	Optional[int]         =							model(UpperCamelCase__		)

					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)

					A__  :	Union[str, Any]         =							model(pixel_values=UpperCamelCase__				,       labels=UpperCamelCase__		)

					self.parent.assertEqual(result.loss.shape				,       ()		)
					self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1)		)
					self.parent.assertEqual(result.pred_boxes.shape				,       (self.batch_size, self.num_detection_tokens, 4)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.prepare_config_and_inputs()
					A__       ,							A__       ,							A__  :	Optional[Any]         =							config_and_inputs
					A__  :	Optional[int]         =							{'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_torch
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	_lowerCAmelCase							       =				(
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				False





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	Optional[int]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													A__  :	str         =							[]
													for i in range(self.model_tester.batch_size		):
																	A__  :	int         =							{}
																	A__  :	Dict         =							torch.ones(
																	    size=(self.model_tester.n_targets,)				,       device=UpperCamelCase__				,       dtype=torch.long		)
																	A__  :	Dict         =							torch.ones(
																	    self.model_tester.n_targets				,       4				,       device=UpperCamelCase__				,       dtype=torch.float		)
																	labels.append(UpperCamelCase__		)
													A__  :	Dict         =							labels

					return inputs_dict





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							YolosModelTester(self		)
					A__  :	List[str]         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       has_text_modality=UpperCamelCase__				,       hidden_size=37		)





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





	def   __snake_case				(			self		):
					# YOLOS does not use inputs_embeds
					pass





	def   __snake_case				(			self		):
					A__       ,							A__  :	Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	Any         =							model_class(UpperCamelCase__		)
									self.assertIsInstance(model.get_input_embeddings()				,       (nn.Module)		)
									A__  :	str         =							model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(UpperCamelCase__				,       nn.Linear		)		)





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

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

									A__  :	Optional[Any]         =							['''pixel_values''']
									self.assertListEqual(arg_names[:1]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__       ,							A__  :	List[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()
					A__  :	Tuple         =							True

					# in YOLOS, the seq_len is different
					A__  :	List[Any]         =							self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									A__  :	Any         =							True
									A__  :	Optional[int]         =							False
									A__  :	Optional[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	List[str]         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Optional[int]         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									A__  :	Tuple         =							True
									A__  :	Optional[Any]         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)
									A__  :	Tuple         =							outputs.attentions
									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )
									A__  :	List[Any]         =							len(UpperCamelCase__		)

									# Check attention is always last and order is fine
									A__  :	List[str]         =							True
									A__  :	List[Any]         =							True
									A__  :	int         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()
									with torch.no_grad():
													A__  :	Tuple         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Tuple         =							1
									self.assertEqual(out_len + added_hidden_states				,       len(UpperCamelCase__		)		)

									A__  :	List[str]         =							outputs.attentions

									self.assertEqual(len(UpperCamelCase__		)				,       self.model_tester.num_hidden_layers		)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)				,       [self.model_tester.num_attention_heads, seq_len, seq_len]				,       )





	def   __snake_case				(			self		):
					def check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
									A__  :	str         =							model_class(UpperCamelCase__		)
									model.to(UpperCamelCase__		)
									model.eval()

									with torch.no_grad():
													A__  :	int         =							model(**self._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__		)		)

									A__  :	Optional[Any]         =							outputs.hidden_states

									A__  :	int         =							getattr(
									    self.model_tester				,       '''expected_num_hidden_layers'''				,       self.model_tester.num_hidden_layers + 1		)
									self.assertEqual(len(UpperCamelCase__		)				,       UpperCamelCase__		)

									# YOLOS has a different seq_length
									A__  :	Union[str, Any]         =							self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				,       [seq_length, self.model_tester.hidden_size]				,       )

					A__       ,							A__  :	Optional[Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__  :	int         =							True
									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									A__  :	Optional[int]         =							True

									check_hidden_states_output(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__  :	Union[str, Any]         =							YolosModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      ) ->       List[str]:

				"""simple docstring"""







				A__  :	int         =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image







@require_torch
@require_vision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	@cached_property
	def   __snake_case				(			self		):
					return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''		) if is_vision_available() else None





	@slow
	def   __snake_case				(			self		):
					A__  :	Tuple         =							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''		).to(UpperCamelCase__		)

					A__  :	str         =							self.default_image_processor
					A__  :	Tuple         =							prepare_img()
					A__  :	Tuple         =							image_processor(images=UpperCamelCase__				,       return_tensors='''pt'''		).to(UpperCamelCase__		)

					# forward pass
					with torch.no_grad():
									A__  :	Any         =							model(inputs.pixel_values		)

					# verify outputs
					A__  :	List[Any]         =							torch.Size((1, 100, 92)		)
					self.assertEqual(outputs.logits.shape				,       UpperCamelCase__		)

					A__  :	Optional[int]         =							torch.tensor(
					    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]				,       device=UpperCamelCase__				,       )
					A__  :	Optional[int]         =							torch.tensor(
					    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]				,       device=UpperCamelCase__		)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)		)

					# verify postprocessing
					A__  :	Dict         =							image_processor.post_process_object_detection(
					    UpperCamelCase__				,       threshold=0.3				,       target_sizes=[image.size[::-1]]		)[0]
					A__  :	int         =							torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]		).to(UpperCamelCase__		)
					A__  :	str         =							[75, 75, 17, 63, 17]
					A__  :	Tuple         =							torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]		).to(UpperCamelCase__		)

					self.assertEqual(len(results['''scores''']		)				,       5		)
					self.assertTrue(torch.allclose(results['''scores''']				,       UpperCamelCase__				,       atol=1e-4		)		)
					self.assertSequenceEqual(results['''labels'''].tolist()				,       UpperCamelCase__		)
					self.assertTrue(torch.allclose(results['''boxes'''][0, :]				,       UpperCamelCase__		)		)