



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(      A       :    Any       )		->       Tuple:
					"""simple docstring"""





					a					     =   384
					if "tiny" in model_name:
										a					     =   [3, 3, 9, 3]
										a					     =   [96, 192, 384, 768]
					if "small" in model_name:
										a					     =   [3, 3, 27, 3]
										a					     =   [96, 192, 384, 768]
					if "base" in model_name:
										a					     =   [3, 3, 27, 3]
										a					     =   [128, 256, 512, 1024]
										a					     =   512
					if "large" in model_name:
										a					     =   [3, 3, 27, 3]
										a					     =   [192, 384, 768, 1536]
										a					     =   768
					if "xlarge" in model_name:
										a					     =   [3, 3, 27, 3]
										a					     =   [256, 512, 1024, 2048]
										a					     =   1024

					# set label information
					a					     =   150
					a					     =   """huggingface/label-files"""
					a					     =   """ade20k-id2label.json"""
					a					     =   json.load(open(hf_hub_download(lowerCamelCase__ ,     lowerCamelCase__ ,     repo_type="dataset"       ) ,     "r"       )       )
					a					     =   {int(lowerCamelCase__       ): v for k, v in idalabel.items()}
					a					     =   {v: k for k, v in idalabel.items()}

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

					return config






def     a(      A       :    Any       )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   []

					# 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(      A       :    Tuple ,     A       :    Optional[Any] ,     A       :    Tuple       )		->       Tuple:
					"""simple docstring"""





					a					     =   dct.pop(lowerCamelCase__       )
					a					     =   val






def     a(      A       :    Optional[int] ,     A       :    Union[str, Any] ,     A       :    Dict       )		->       Tuple:
					"""simple docstring"""





					a					     =   {
					    """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""",
					}
					a					     =   model_name_to_url[model_name]
					a					     =   torch.hub.load_state_dict_from_url(lowerCamelCase__ ,     map_location="cpu"       )["""state_dict"""]

					a					     =   get_upernet_config(lowerCamelCase__       )
					a					     =   UperNetForSemanticSegmentation(lowerCamelCase__       )
					model.eval()

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

					# rename keys
					a					     =   create_rename_keys(lowerCamelCase__       )
					for src, dest in rename_keys:
										rename_key(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__       )

					model.load_state_dict(lowerCamelCase__       )

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

					a					     =   SegformerImageProcessor()
					a					     =   processor(lowerCamelCase__ ,     return_tensors="pt"       ).pixel_values

					with torch.no_grad():
										a					     =   model(lowerCamelCase__       )

					if model_name == "upernet-convnext-tiny":
										a					     =   torch.tensor(
										    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
					elif model_name == "upernet-convnext-small":
										a					     =   torch.tensor(
										    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
					elif model_name == "upernet-convnext-base":
										a					     =   torch.tensor(
										    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
					elif model_name == "upernet-convnext-large":
										a					     =   torch.tensor(
										    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
					elif model_name == "upernet-convnext-xlarge":
										a					     =   torch.tensor(
										    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
					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__":
				_lowercase: List[str]      			=		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."
				)

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





'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser



def 	a			( lowerCamelCase__=None    ):
			'''simple docstring'''





			A_	:						int	=     argparse.ArgumentParser(add_help=lowerCamelCase__						,      allow_abbrev=lowerCamelCase__    )
			# The main config parser
			A_	:						int	=     config_command_parser(lowerCamelCase__    )
			# The subparser to add commands to
			A_	:						int	=     config_parser.add_subparsers(title="""subcommands"""						,      dest="""subcommand"""    )

			# Then add other parsers with the parent parser
			default_command_parser(lowerCamelCase__						,      parents=[parent_parser]    )
			update_command_parser(lowerCamelCase__						,      parents=[parent_parser]    )

			return config_parser



def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     get_config_parser()
			A_	:						List[str]	=     config_parser.parse_args()

			if not hasattr(lowerCamelCase__						,      """func"""    ):
						config_parser.print_help()
						exit(1    )

			# Run
			args.func(lowerCamelCase__    )


if __name__ == "__main__":
						main()