




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''mra'''





						def __init__(							self      :	str     ,a__      :	List[Any]=5_0265     ,a__      :	Optional[Any]=768     ,a__      :	List[Any]=12     ,a__      :	Any=12     ,a__      :	List[Any]=3072     ,a__      :	Optional[Any]="gelu"     ,a__      :	int=0.1     ,a__      :	Any=0.1     ,a__      :	List[Any]=512     ,a__      :	Dict=1     ,a__      :	List[str]=0.02     ,a__      :	List[Any]=1E-5     ,a__      :	Union[str, Any]="absolute"     ,a__      :	int=4     ,a__      :	Dict="full"     ,a__      :	Dict=0     ,a__      :	Optional[Any]=0     ,a__      :	Tuple=1     ,a__      :	str=0     ,a__      :	List[Any]=2     ,**a__      :	Optional[int]     ,)						->       List[Any]:




											"""simple docstring"""







											super().__init__(pad_token_id=a__     ,bos_token_id=a__     ,eos_token_id=a__     ,**a__)

											_lowerCAmelCase:List[str]									=  vocab_size
											_lowerCAmelCase:Optional[int]									=  max_position_embeddings
											_lowerCAmelCase:List[Any]									=  hidden_size
											_lowerCAmelCase:List[str]									=  num_hidden_layers
											_lowerCAmelCase:List[Any]									=  num_attention_heads
											_lowerCAmelCase:str									=  intermediate_size
											_lowerCAmelCase:Optional[int]									=  hidden_act
											_lowerCAmelCase:Tuple									=  hidden_dropout_prob
											_lowerCAmelCase:Optional[Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:Tuple									=  initializer_range
											_lowerCAmelCase:Tuple									=  type_vocab_size
											_lowerCAmelCase:List[str]									=  layer_norm_eps
											_lowerCAmelCase:List[str]									=  position_embedding_type
											_lowerCAmelCase:Optional[Any]									=  block_per_row
											_lowerCAmelCase:Dict									=  approx_mode
											_lowerCAmelCase:Optional[Any]									=  initial_prior_first_n_blocks
											_lowerCAmelCase:int									=  initial_prior_diagonal_n_blocks












"""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 							UpperCAmelCase		(		snake_case			:					Tuple    ):
					_lowerCAmelCase:Tuple									=  384
					if "tiny" in model_name:
										_lowerCAmelCase:Any									=  [3, 3, 9, 3]
										_lowerCAmelCase:Union[str, Any]									=  [96, 192, 384, 768]
					if "small" in model_name:
										_lowerCAmelCase:List[str]									=  [3, 3, 27, 3]
										_lowerCAmelCase:Any									=  [96, 192, 384, 768]
					if "base" in model_name:
										_lowerCAmelCase:int									=  [3, 3, 27, 3]
										_lowerCAmelCase:int									=  [128, 256, 512, 1024]
										_lowerCAmelCase:Any									=  512
					if "large" in model_name:
										_lowerCAmelCase:Optional[int]									=  [3, 3, 27, 3]
										_lowerCAmelCase:List[str]									=  [192, 384, 768, 1536]
										_lowerCAmelCase:List[Any]									=  768
					if "xlarge" in model_name:
										_lowerCAmelCase:str									=  [3, 3, 27, 3]
										_lowerCAmelCase:Optional[int]									=  [256, 512, 1024, 2048]
										_lowerCAmelCase:Optional[int]									=  1024

					# set label information
					_lowerCAmelCase:Tuple									=  150
					_lowerCAmelCase:int									=  '''huggingface/label-files'''
					_lowerCAmelCase:Optional[int]									=  '''ade20k-id2label.json'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Dict									=  {int(snake_case    ): v for k, v in idalabel.items()}
					_lowerCAmelCase:Tuple									=  {v: k for k, v in idalabel.items()}

					_lowerCAmelCase:str									=  ConvNextConfig(
					    depths=snake_case      ,       hidden_sizes=snake_case      ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetConfig(
					    backbone_config=snake_case      ,       auxiliary_in_channels=snake_case      ,       num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case      ,       )

					return config





def 							UpperCAmelCase		(		snake_case			:					List[Any]    ):
					_lowerCAmelCase: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 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Optional[int]    ):
					_lowerCAmelCase:int									=  dct.pop(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  val





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					int      ,       snake_case			:					Optional[Any]    ):
					_lowerCAmelCase:Dict									=  {
					    '''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''',
					}
					_lowerCAmelCase:Optional[Any]									=  model_name_to_url[model_name]
					_lowerCAmelCase:Optional[Any]									=  torch.hub.load_state_dict_from_url(snake_case      ,       map_location='''cpu'''    )['''state_dict''']

					_lowerCAmelCase:Tuple									=  get_upernet_config(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  UperNetForSemanticSegmentation(snake_case    )
					model.eval()

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

					# rename keys
					_lowerCAmelCase:int									=  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
					_lowerCAmelCase:Optional[int]									=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
					_lowerCAmelCase:Optional[int]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    ).convert('''RGB'''    )

					_lowerCAmelCase:int									=  SegformerImageProcessor()
					_lowerCAmelCase:Optional[int]									=  processor(snake_case      ,       return_tensors='''pt'''    ).pixel_values

					with torch.no_grad():
										_lowerCAmelCase:str									=  model(snake_case    )

					if model_name == "upernet-convnext-tiny":
										_lowerCAmelCase:Union[str, Any]									=  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":
										_lowerCAmelCase:List[str]									=  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":
										_lowerCAmelCase:Dict									=  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":
										_lowerCAmelCase:Union[str, Any]									=  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":
										_lowerCAmelCase:List[str]									=  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__						=			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__						=			parser.parse_args()
				convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






