



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

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

		lowerCAmelCase_			:     Dict									= ConvNextConfig(
		    depths=__UpperCamelCase     ,hidden_sizes=__UpperCamelCase     ,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
		lowerCAmelCase_			:     List[Any]									= UperNetConfig(
		    backbone_config=__UpperCamelCase     ,auxiliary_in_channels=__UpperCamelCase     ,num_labels=__UpperCamelCase     ,idalabel=__UpperCamelCase     ,labelaid=__UpperCamelCase     ,)

		return config


def 			UpperCamelCase(  __UpperCamelCase							:		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(  __UpperCamelCase							:		int     ,__UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		Tuple ):
		lowerCAmelCase_			:     Any									= dct.pop(__UpperCamelCase )
		lowerCAmelCase_			:     Tuple									= val


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     List[Any]									= {
		    '''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_			:     str									= model_name_to_url[model_name]
		lowerCAmelCase_			:     str									= torch.hub.load_state_dict_from_url(__UpperCamelCase     ,map_location='''cpu''' )['''state_dict''']

		lowerCAmelCase_			:     Optional[int]									= get_upernet_config(__UpperCamelCase )
		lowerCAmelCase_			:     Any									= UperNetForSemanticSegmentation(__UpperCamelCase )
		model.eval()

		# replace "bn" => "batch_norm"
		for key in state_dict.copy().keys():
				lowerCAmelCase_			:     Dict									= state_dict.pop(__UpperCamelCase )
				if "bn" in key:
						lowerCAmelCase_			:     List[str]									= key.replace('''bn'''     ,'''batch_norm''' )
				lowerCAmelCase_			:     Tuple									= val

		# rename keys
		lowerCAmelCase_			:     str									= create_rename_keys(__UpperCamelCase )
		for src, dest in rename_keys:
				rename_key(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )

		model.load_state_dict(__UpperCamelCase )

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

		lowerCAmelCase_			:     Dict									= SegformerImageProcessor()
		lowerCAmelCase_			:     Any									= processor(__UpperCamelCase     ,return_tensors='''pt''' ).pixel_values

		with torch.no_grad():
				lowerCAmelCase_			:     str									= model(__UpperCamelCase )

		if model_name == "upernet-convnext-tiny":
				lowerCAmelCase_			:     List[str]									= 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":
				lowerCAmelCase_			:     Union[str, Any]									= 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":
				lowerCAmelCase_			:     Dict									= 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":
				lowerCAmelCase_			:     Optional[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":
				lowerCAmelCase_			:     Dict									= 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__":
						A__  :  Optional[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.'''
						)

						A__  :  int								=					parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










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


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





