


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__  ):
					lowercase_  							=			3_8_4
					if "tiny" in model_name:
										lowercase_  							=			[3, 3, 9, 3]
										lowercase_  							=			[9_6, 1_9_2, 3_8_4, 7_6_8]
					if "small" in model_name:
										lowercase_  							=			[3, 3, 2_7, 3]
										lowercase_  							=			[9_6, 1_9_2, 3_8_4, 7_6_8]
					if "base" in model_name:
										lowercase_  							=			[3, 3, 2_7, 3]
										lowercase_  							=			[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
										lowercase_  							=			5_1_2
					if "large" in model_name:
										lowercase_  							=			[3, 3, 2_7, 3]
										lowercase_  							=			[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
										lowercase_  							=			7_6_8
					if "xlarge" in model_name:
										lowercase_  							=			[3, 3, 2_7, 3]
										lowercase_  							=			[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
										lowercase_  							=			1_0_2_4

					# set label information
					lowercase_  							=			1_5_0
					lowercase_  							=			'''huggingface/label-files'''
					lowercase_  							=			'''ade20k-id2label.json'''
					lowercase_  							=			json.load(open(hf_hub_download(lowerCAmelCase_						,							lowerCAmelCase_						,							repo_type="""dataset"""  )						,							"""r"""  )  )
					lowercase_  							=			{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
					lowercase_  							=			{v: k for k, v in idalabel.items()}

					lowercase_  							=			ConvNextConfig(
					    depths=lowerCAmelCase_						,							hidden_sizes=lowerCAmelCase_						,							out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  )
					lowercase_  							=			UperNetConfig(
					    backbone_config=lowerCAmelCase_						,							auxiliary_in_channels=lowerCAmelCase_						,							num_labels=lowerCAmelCase_						,							idalabel=lowerCAmelCase_						,							labelaid=lowerCAmelCase_						,							)

					return config


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			[]

					# 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__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			dct.pop(lowerCAmelCase_  )
					lowercase_  							=			val


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			{
					    '''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''',
					}
					lowercase_  							=			model_name_to_url[model_name]
					lowercase_  							=			torch.hub.load_state_dict_from_url(lowerCAmelCase_						,							map_location="""cpu"""  )['''state_dict''']

					lowercase_  							=			get_upernet_config(lowerCAmelCase_  )
					lowercase_  							=			UperNetForSemanticSegmentation(lowerCAmelCase_  )
					model.eval()

					# replace "bn" => "batch_norm"
					for key in state_dict.copy().keys():
										lowercase_  							=			state_dict.pop(lowerCAmelCase_  )
										if "bn" in key:
															lowercase_  							=			key.replace("""bn"""						,							"""batch_norm"""  )
										lowercase_  							=			val

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

					model.load_state_dict(lowerCAmelCase_  )

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

					lowercase_  							=			SegformerImageProcessor()
					lowercase_  							=			processor(lowerCAmelCase_						,							return_tensors="""pt"""  ).pixel_values

					with torch.no_grad():
										lowercase_  							=			model(lowerCAmelCase_  )

					if model_name == "upernet-convnext-tiny":
										lowercase_  							=			torch.tensor(
										    [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]  )
					elif model_name == "upernet-convnext-small":
										lowercase_  							=			torch.tensor(
										    [[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]]  )
					elif model_name == "upernet-convnext-base":
										lowercase_  							=			torch.tensor(
										    [[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]]  )
					elif model_name == "upernet-convnext-large":
										lowercase_  							=			torch.tensor(
										    [[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]]  )
					elif model_name == "upernet-convnext-xlarge":
										lowercase_  							=			torch.tensor(
										    [[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]]  )
					print("""Logits:"""						,							outputs.logits[0, 0, :3, :3]  )
					assert torch.allclose(outputs.logits[0, 0, :3, :3]						,							lowerCAmelCase_						,							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(lowerCAmelCase_  )
										print(F'''Saving processor to {pytorch_dump_folder_path}'''  )
										processor.save_pretrained(lowerCAmelCase_  )

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






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__   ):

					@register_to_config
					def __init__(     self       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   bool = False							,					):






										'''simple docstring'''






										super().__init__()

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )
										lowercase_  							=			False

										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )

										lowercase_  							=			TaConfig(
										    vocab_size=UpperCamelCase__							,					d_model=UpperCamelCase__							,					num_heads=UpperCamelCase__							,					d_kv=UpperCamelCase__							,					d_ff=UpperCamelCase__							,					dropout_rate=UpperCamelCase__							,					feed_forward_proj=UpperCamelCase__							,					is_decoder=UpperCamelCase__							,					is_encoder_decoder=UpperCamelCase__							,					)

										lowercase_  							=			nn.ModuleList()
										for lyr_num in range(UpperCamelCase__       ):
															lowercase_  							=			TaBlock(UpperCamelCase__       )
															self.encoders.append(UpperCamelCase__       )

										lowercase_  							=			TaLayerNorm(UpperCamelCase__       )
										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.token_embedder(UpperCamelCase__       )

										lowercase_  							=			encoder_input_tokens.shape[1]
										lowercase_  							=			torch.arange(UpperCamelCase__							,					device=encoder_input_tokens.device       )
										x += self.position_encoding(UpperCamelCase__       )

										lowercase_  							=			self.dropout_pre(UpperCamelCase__       )

										# inverted the attention mask
										lowercase_  							=			encoder_input_tokens.size()
										lowercase_  							=			self.get_extended_attention_mask(UpperCamelCase__							,					UpperCamelCase__       )

										for lyr in self.encoders:
															lowercase_  							=			lyr(UpperCamelCase__							,					UpperCamelCase__       )[0]
										lowercase_  							=			self.layer_norm(UpperCamelCase__       )

										return self.dropout_post(UpperCamelCase__       ), encoder_inputs_mask


