
"""simple docstring"""

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
					import torch

					from transformers import UperNetForSemanticSegmentation
					from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=13		,			UpperCAmelCase_=32		,			UpperCAmelCase_=3		,			UpperCAmelCase_=4		,			UpperCAmelCase_=[10, 20, 30, 40]		,			UpperCAmelCase_=[2, 2, 3, 2]		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			UpperCAmelCase_=37		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=10		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=["stage2", "stage3", "stage4"]		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			)		->							Optional[int]:
			lowerCamelCase					:      int          =   parent
			lowerCamelCase					:      Optional[Any]          =   batch_size
			lowerCamelCase					:      Tuple          =   image_size
			lowerCamelCase					:      Optional[int]          =   num_channels
			lowerCamelCase					:      Union[str, Any]          =   num_stages
			lowerCamelCase					:      Union[str, Any]          =   hidden_sizes
			lowerCamelCase					:      List[str]          =   depths
			lowerCamelCase					:      Tuple          =   is_training
			lowerCamelCase					:      int          =   use_labels
			lowerCamelCase					:      Dict          =   intermediate_size
			lowerCamelCase					:      Dict          =   hidden_act
			lowerCamelCase					:      Dict          =   type_sequence_label_size
			lowerCamelCase					:      List[str]          =   initializer_range
			lowerCamelCase					:      Optional[Any]          =   out_features
			lowerCamelCase					:      Any          =   num_labels
			lowerCamelCase					:      str          =   scope
			lowerCamelCase					:      Tuple          =   num_stages




	def       _UpperCamelCase   (			self )		->							Dict:
			lowerCamelCase					:      List[str]          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

			lowerCamelCase					:      Union[str, Any]          =   None
			if self.use_labels:
					lowerCamelCase					:      Optional[int]          =   ids_tensor([self.batch_size]		,			self.type_sequence_label_size )

			lowerCamelCase					:      str          =   self.get_config()

			return config, pixel_values, labels




	def       _UpperCamelCase   (			self )		->							Dict:
			return ConvNextConfig(
			    num_channels=self.num_channels		,			num_stages=self.num_stages		,			hidden_sizes=self.hidden_sizes		,			depths=self.depths		,			is_training=self.is_training		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			out_features=self.out_features		,			)




	def       _UpperCamelCase   (			self )		->							Tuple:
			return UperNetConfig(
			    backbone_config=self.get_backbone_config()		,			hidden_size=512		,			pool_scales=[1, 2, 3, 6]		,			use_auxiliary_head=UpperCAmelCase_		,			auxiliary_loss_weight=0.4		,			auxiliary_in_channels=40		,			auxiliary_channels=256		,			auxiliary_num_convs=1		,			auxiliary_concat_input=UpperCAmelCase_		,			loss_ignore_index=255		,			num_labels=self.num_labels		,			)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   UperNetForSemanticSegmentation(config=UpperCAmelCase_ )
			model.to(UpperCAmelCase_ )
			model.eval()
			lowerCamelCase					:      List[Any]          =   model(UpperCAmelCase_ )
			self.parent.assertEqual(
			    result.logits.shape		,			(self.batch_size, self.num_labels, self.image_size, self.image_size) )




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Optional[int]          =   self.prepare_config_and_inputs()
			(
			    lowerCamelCase   
			)					:      Optional[int]          =   config_and_inputs
			lowerCamelCase					:      int          =   {'pixel_values': pixel_values}
			return config, inputs_dict






@require_torch
class 	_lowercase       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     (UperNetForSemanticSegmentation,) if is_torch_available() else ()
	lowercase_				=     {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False
	lowercase_				=     False




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      str          =   UperNetModelTester(self )
			lowerCamelCase					:      List[Any]          =   ConfigTester(self		,			config_class=UpperCAmelCase_		,			has_text_modality=UpperCAmelCase_		,			hidden_size=37 )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()




	def       _UpperCamelCase   (			self )		->							Tuple:
			return




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Optional[Any]          =   self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase					:      List[Any]          =   ['pixel_values']
					self.assertListEqual(arg_names[:1]		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_ )




	@unittest.skip(reason='UperNet does not use inputs_embeds' )
	def       _UpperCamelCase   (			self )		->							Any:
			pass




	@unittest.skip(reason='UperNet does not support input and output embeddings' )
	def       _UpperCamelCase   (			self )		->							int:
			pass




	@unittest.skip(reason='UperNet does not have a base model' )
	def       _UpperCamelCase   (			self )		->							str:
			pass




	@unittest.skip(reason='UperNet does not have a base model' )
	def       _UpperCamelCase   (			self )		->							int:
			pass




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




	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			pass




	def       _UpperCamelCase   (			self )		->							str:
			def check_hidden_states_output(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[int]          =   model_class(UpperCAmelCase_ )
					model.to(UpperCAmelCase_ )
					model.eval()

					with torch.no_grad():
							lowerCamelCase					:      Union[str, Any]          =   model(**self._prepare_for_class(UpperCAmelCase_		,			UpperCAmelCase_ ) )

					lowerCamelCase					:      List[str]          =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					lowerCamelCase					:      Dict          =   self.model_tester.num_stages
					self.assertEqual(len(UpperCAmelCase_ )		,			expected_num_stages + 1 )

					# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:] )		,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,			)

			lowerCamelCase					:      Any          =   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase					:      Any          =   True
					check_hidden_states_output(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					lowerCamelCase					:      Dict          =   True

					check_hidden_states_output(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Tuple          =   self.model_tester.prepare_config_and_inputs_for_common()

			lowerCamelCase					:      Dict          =   _config_zero_init(UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   _config_zero_init(configs_no_init.backbone_config )
			for model_class in self.all_model_classes:
					lowerCamelCase					:      Tuple          =   model_class(config=UpperCAmelCase_ )
					for name, param in model.named_parameters():
							if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1E9).round() / 1E9).item()		,			[0.0, 1.0]		,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,			)




	@unittest.skip(reason='UperNet does not have tied weights' )
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			pass




	@slow
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase					:      int          =   UperNetForSemanticSegmentation.from_pretrained(UpperCAmelCase_ )
					self.assertIsNotNone(UpperCAmelCase_ )


def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      int          =   hf_hub_download(
		    repo_id='hf-internal-testing/fixtures_ade20k',						repo_type='dataset',						filename='ADE_val_00000001.jpg'      )
		lowerCamelCase					:      Tuple          =   Image.open(a_      ).convert('RGB'      )
		return image






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




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      List[str]          =   AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny' )
			lowerCamelCase					:      Union[str, Any]          =   UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny' ).to(UpperCAmelCase_ )

			lowerCamelCase					:      str          =   prepare_img()
			lowerCamelCase					:      Any          =   processor(images=UpperCAmelCase_		,			return_tensors='pt' ).to(UpperCAmelCase_ )

			with torch.no_grad():
					lowerCamelCase					:      Any          =   model(**UpperCAmelCase_ )

			lowerCamelCase					:      Tuple          =   torch.Size((1, model.config.num_labels, 512, 512) )
			self.assertEqual(outputs.logits.shape		,			UpperCAmelCase_ )

			lowerCamelCase					:      str          =   torch.tensor(
			    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(UpperCAmelCase_ )
			self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,			UpperCAmelCase_		,			atol=1E-4 ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny' )
			lowerCamelCase					:      Union[str, Any]          =   UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny' ).to(UpperCAmelCase_ )

			lowerCamelCase					:      Any          =   prepare_img()
			lowerCamelCase					:      Any          =   processor(images=UpperCAmelCase_		,			return_tensors='pt' ).to(UpperCAmelCase_ )

			with torch.no_grad():
					lowerCamelCase					:      int          =   model(**UpperCAmelCase_ )

			lowerCamelCase					:      Optional[Any]          =   torch.Size((1, model.config.num_labels, 512, 512) )
			self.assertEqual(outputs.logits.shape		,			UpperCAmelCase_ )

			lowerCamelCase					:      Optional[int]          =   torch.tensor(
			    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(UpperCAmelCase_ )
			self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,			UpperCAmelCase_		,			atol=1E-4 ) )








"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   OrderedDict()
		for key, value in state_dict.items():
				if key.startswith('module.encoder'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.encoder',						'glpn.encoder'      )
				if key.startswith('module.decoder'      ):
						lowerCamelCase					:      str          =   key.replace('module.decoder',						'decoder.stages'      )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCamelCase					:      Any          =   key[key.find('patch_embed'      ) + len('patch_embed'      )]
						lowerCamelCase					:      Dict          =   key.replace(F"""patch_embed{idx}""",						F"""patch_embeddings.{int(a_      )-1}"""      )
				if "norm" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('norm',						'layer_norm'      )
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCamelCase					:      List[str]          =   key[key.find('glpn.encoder.layer_norm'      ) + len('glpn.encoder.layer_norm'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""layer_norm{idx}""",						F"""layer_norm.{int(a_      )-1}"""      )
				if "layer_norm1" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('layer_norm1',						'layer_norm_1'      )
				if "layer_norm2" in key:
						lowerCamelCase					:      str          =   key.replace('layer_norm2',						'layer_norm_2'      )
				if "block" in key:
						# replace for example block1 by block.0
						lowerCamelCase					:      Union[str, Any]          =   key[key.find('block'      ) + len('block'      )]
						lowerCamelCase					:      List[str]          =   key.replace(F"""block{idx}""",						F"""block.{int(a_      )-1}"""      )
				if "attn.q" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('attn.q',						'attention.self.query'      )
				if "attn.proj" in key:
						lowerCamelCase					:      Dict          =   key.replace('attn.proj',						'attention.output.dense'      )
				if "attn" in key:
						lowerCamelCase					:      int          =   key.replace('attn',						'attention.self'      )
				if "fc1" in key:
						lowerCamelCase					:      Any          =   key.replace('fc1',						'dense1'      )
				if "fc2" in key:
						lowerCamelCase					:      List[Any]          =   key.replace('fc2',						'dense2'      )
				if "linear_pred" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('linear_pred',						'classifier'      )
				if "linear_fuse" in key:
						lowerCamelCase					:      Union[str, Any]          =   key.replace('linear_fuse.conv',						'linear_fuse'      )
						lowerCamelCase					:      Optional[int]          =   key.replace('linear_fuse.bn',						'batch_norm'      )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCamelCase					:      str          =   key[key.find('linear_c'      ) + len('linear_c'      )]
						lowerCamelCase					:      List[Any]          =   key.replace(F"""linear_c{idx}""",						F"""linear_c.{int(a_      )-1}"""      )
				if "bot_conv" in key:
						lowerCamelCase					:      int          =   key.replace('bot_conv',						'0.convolution'      )
				if "skip_conv1" in key:
						lowerCamelCase					:      Any          =   key.replace('skip_conv1',						'1.convolution'      )
				if "skip_conv2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('skip_conv2',						'2.convolution'      )
				if "fusion1" in key:
						lowerCamelCase					:      str          =   key.replace('fusion1',						'1.fusion'      )
				if "fusion2" in key:
						lowerCamelCase					:      Optional[Any]          =   key.replace('fusion2',						'2.fusion'      )
				if "fusion3" in key:
						lowerCamelCase					:      List[str]          =   key.replace('fusion3',						'3.fusion'      )
				if "fusion" in key and "conv" in key:
						lowerCamelCase					:      Optional[int]          =   key.replace('conv',						'convolutional_layer'      )
				if key.startswith('module.last_layer_depth'      ):
						lowerCamelCase					:      Tuple          =   key.replace('module.last_layer_depth',						'head.head'      )
				lowerCamelCase					:      List[Any]          =   value

		return new_state_dict
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for i in range(config.num_encoder_blocks      ):
				for j in range(config.depths[i]      ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCamelCase					:      Any          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
						lowerCamelCase					:      Optional[Any]          =   state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
						# next, add keys and values (in that order) to the state dict
						lowerCamelCase					:      Any          =   kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[: config.hidden_sizes[i]]
						lowerCamelCase					:      Dict          =   kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCamelCase					:      List[Any]          =   kv_bias[config.hidden_sizes[i] :]
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False,						a_=None      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   GLPNConfig(hidden_sizes=[64, 128, 320, 512],						decoder_hidden_size=64,						depths=[3, 8, 27, 3]      )

		# load image processor (only resize + rescale)
		lowerCamelCase					:      Any          =   GLPNImageProcessor()

		# prepare image
		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      Tuple          =   image_processor(images=a_,						return_tensors='pt'      ).pixel_values

		logger.info('Converting model...'      )

		# load original state dict
		lowerCamelCase					:      Optional[Any]          =   torch.load(a_,						map_location=torch.device('cpu'      )      )

		# rename keys
		lowerCamelCase					:      Any          =   rename_keys(a_      )

		# key and value matrices need special treatment
		read_in_k_v(a_,						a_      )

		# create HuggingFace model and load state dict
		lowerCamelCase					:      Optional[int]          =   GLPNForDepthEstimation(a_      )
		model.load_state_dict(a_      )
		model.eval()

		# forward pass
		lowerCamelCase					:      str          =   model(a_      )
		lowerCamelCase					:      Any          =   outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCamelCase					:      Any          =   torch.tensor(
						    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]      )
				elif "kitti" in model_name:
						lowerCamelCase					:      str          =   torch.tensor(
						    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      )
				else:
						raise ValueError(F"""Unknown model name: {model_name}"""      )

				lowerCamelCase					:      int          =   torch.Size([1, 480, 640]      )

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3],						a_,						atol=1E-4      )
				print('Looks ok!'      )

		# finally, push to hub if required
		if push_to_hub:
				logger.info('Pushing model and image processor to the hub...'      )
				model.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add model',						use_temp_dir=a_,						)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_,						a_      ),						organization='nielsr',						commit_message='Add image processor',						use_temp_dir=a_,						)


if __name__ == "__main__":
					_A						=			argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path',
					    default=None,
					    type=str,
					    help='Path to the original PyTorch checkpoint (.pth file).',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
					)
					parser.add_argument(
					    '--model_name',
					    default='glpn-kitti',
					    type=str,
					    help='Name of the model in case you\'re pushing to the hub.',
					)
					_A						=			parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






