



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

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

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

							return config


def 					snake_case_	(   _lowerCAmelCase    :		Union[str, Any]    ) ->  Tuple:
							UpperCAmelCase					:			Tuple									=						[]

							# 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 					snake_case_	(   _lowerCAmelCase    :		Union[str, Any]  , _lowerCAmelCase    :		Union[str, Any]  , _lowerCAmelCase    :		List[str]    ) ->  int:
							UpperCAmelCase					:			Tuple									=						dct.pop(UpperCamelCase_    )
							UpperCAmelCase					:			str									=						val


def 					snake_case_	(   _lowerCAmelCase    :		Tuple  , _lowerCAmelCase    :		Optional[int]  , _lowerCAmelCase    :		List[str]    ) ->  List[Any]:
							UpperCAmelCase					:			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''',
							}
							UpperCAmelCase					:			Tuple									=						model_name_to_url[model_name]
							UpperCAmelCase					:			str									=						torch.hub.load_state_dict_from_url(UpperCamelCase_  , map_location='''cpu'''    )['''state_dict''']

							UpperCAmelCase					:			Tuple									=						get_upernet_config(UpperCamelCase_    )
							UpperCAmelCase					:			Optional[Any]									=						UperNetForSemanticSegmentation(UpperCamelCase_    )
							model.eval()

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

							# rename keys
							UpperCAmelCase					:			int									=						create_rename_keys(UpperCamelCase_    )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase_  , UpperCamelCase_  , UpperCamelCase_    )

							model.load_state_dict(UpperCamelCase_    )

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

							UpperCAmelCase					:			List[Any]									=						SegformerImageProcessor()
							UpperCAmelCase					:			Optional[int]									=						processor(UpperCamelCase_  , return_tensors='''pt'''    ).pixel_values

							with torch.no_grad():
														UpperCAmelCase					:			int									=						model(UpperCamelCase_    )

							if model_name == "upernet-convnext-tiny":
														UpperCAmelCase					:			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":
														UpperCAmelCase					:			List[str]									=						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":
														UpperCAmelCase					:			Optional[int]									=						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":
														UpperCAmelCase					:			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":
														UpperCAmelCase					:			str									=						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__":
						UpperCamelCase__:  List[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__:  Optional[int]				      =     parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


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






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None or resample is None:
																				raise ValueError("Size and resample must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("Image mean and std must be specified if do_normalize is True."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





