


"""simple docstring"""






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

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

			_UpperCamelCase					:Optional[Any]					     =	ConvNextConfig(
			    depths=snake_case__       ,					hidden_sizes=snake_case__       ,					out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		)
			_UpperCamelCase					:List[str]					     =	UperNetConfig(
			    backbone_config=snake_case__       ,					auxiliary_in_channels=snake_case__       ,					num_labels=snake_case__       ,					idalabel=snake_case__       ,					labelaid=snake_case__       ,					)

			return config





def 				A_       ( snake_case__		)					->  Any:
			_UpperCamelCase					:List[str]					     =	[]

			# 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 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Any:
			_UpperCamelCase					:Union[str, Any]					     =	dct.pop(snake_case__		)
			_UpperCamelCase					:str					     =	val





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  int:
			_UpperCamelCase					:str					     =	{
			    '''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''',
			}
			_UpperCamelCase					:str					     =	model_name_to_url[model_name]
			_UpperCamelCase					:Union[str, Any]					     =	torch.hub.load_state_dict_from_url(snake_case__       ,					map_location='''cpu'''		)['''state_dict''']

			_UpperCamelCase					:Any					     =	get_upernet_config(snake_case__		)
			_UpperCamelCase					:int					     =	UperNetForSemanticSegmentation(snake_case__		)
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						_UpperCamelCase					:Optional[Any]					     =	state_dict.pop(snake_case__		)
						if "bn" in key:
									_UpperCamelCase					:List[Any]					     =	key.replace('''bn'''       ,					'''batch_norm'''		)
						_UpperCamelCase					:Optional[Any]					     =	val

			# rename keys
			_UpperCamelCase					:Union[str, Any]					     =	create_rename_keys(snake_case__		)
			for src, dest in rename_keys:
						rename_key(snake_case__       ,					snake_case__       ,					snake_case__		)

			model.load_state_dict(snake_case__		)

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

			_UpperCamelCase					:int					     =	SegformerImageProcessor()
			_UpperCamelCase					:Dict					     =	processor(snake_case__       ,					return_tensors='''pt'''		).pixel_values

			with torch.no_grad():
						_UpperCamelCase					:Any					     =	model(snake_case__		)

			if model_name == "upernet-convnext-tiny":
						_UpperCamelCase					:Optional[int]					     =	torch.tensor(
						    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]		)
			elif model_name == "upernet-convnext-small":
						_UpperCamelCase					:Optional[int]					     =	torch.tensor(
						    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]		)
			elif model_name == "upernet-convnext-base":
						_UpperCamelCase					:Optional[Any]					     =	torch.tensor(
						    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]		)
			elif model_name == "upernet-convnext-large":
						_UpperCamelCase					:int					     =	torch.tensor(
						    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]		)
			elif model_name == "upernet-convnext-xlarge":
						_UpperCamelCase					:Optional[Any]					     =	torch.tensor(
						    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]		)
			print('''Logits:'''       ,					outputs.logits[0, 0, :3, :3]		)
			assert torch.allclose(outputs.logits[0, 0, :3, :3]       ,					snake_case__       ,					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(snake_case__		)
						print(f"Saving processor to {pytorch_dump_folder_path}"		)
						processor.save_pretrained(snake_case__		)

			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__":
					UpperCamelCase__				:Any			   =	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."""
					)

					UpperCamelCase__				:Union[str, Any]			   =	parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""






import os
import sys

UpperCamelCase__				:Union[str, Any]			   =	os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCamelCase__				:List[Any]			   =	[
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]





@add_start_docstrings(AutoConfig.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoConfig.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoTokenizer.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  int:
			return AutoTokenizer.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModel.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModel.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForCausalLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForMaskedLM.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Dict:
			return AutoModelForSequenceClassification.from_pretrained(*snake_case__       ,					**snake_case__		)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def 				A_       ( *snake_case__       ,					**snake_case__		)					->  Union[str, Any]:
			return AutoModelForQuestionAnswering.from_pretrained(*snake_case__       ,					**snake_case__		)

