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

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

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

					return config





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    []

					# 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 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    dct.pop(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    val





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_SNAKE_CASE           =    {
					    "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",
					}
					SCREAMING_SNAKE_CASE           =    model_name_to_url[model_name]
					SCREAMING_SNAKE_CASE           =    torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,	map_location="cpu"					)["state_dict"]

					SCREAMING_SNAKE_CASE           =    get_upernet_config(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    UperNetForSemanticSegmentation(UpperCAmelCase__					)
					model.eval()

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

					# rename keys
					SCREAMING_SNAKE_CASE           =    create_rename_keys(UpperCAmelCase__					)
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					model.load_state_dict(UpperCAmelCase__					)

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

					SCREAMING_SNAKE_CASE           =    SegformerImageProcessor()
					SCREAMING_SNAKE_CASE           =    processor(UpperCAmelCase__ ,	return_tensors="pt"					).pixel_values

					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =    model(UpperCAmelCase__					)

					if model_name == "upernet-convnext-tiny":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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":
										SCREAMING_SNAKE_CASE           =    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] ,	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__":
							_lowerCamelCase   :				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.'''
							)

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





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class   lowercase      :
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    int
							lowercase__  :    int
							lowercase__  :    float
							lowercase__  :    float
							lowercase__  :    Tuple[int]




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
												assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
												assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2




							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.width, self.height]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Any )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch.arange(self.height * self.width )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_UpperCamelCase		,							self.width		,							rounding_mode="trunc" ),
												    ]		,							axis=1		,							)
												return coords




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE           =    self.shape
												SCREAMING_SNAKE_CASE           =    int(np.prod(_UpperCamelCase ) )

												SCREAMING_SNAKE_CASE           =    self.get_image_coords()
												SCREAMING_SNAKE_CASE           =    torch.broadcast_to(coords.unsqueeze(0 )		,							[batch_size * inner_batch_size, *coords.shape] )
												SCREAMING_SNAKE_CASE           =    self.get_camera_rays(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    rays.view(_UpperCamelCase		,							inner_batch_size * self.height * self.width		,							2		,							3 )

												return rays




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   torch.Tensor )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												SCREAMING_SNAKE_CASE           =    coords.view(_UpperCamelCase		,							-1		,							2 )

												SCREAMING_SNAKE_CASE           =    self.resolution()
												SCREAMING_SNAKE_CASE           =    self.fov()

												SCREAMING_SNAKE_CASE           =    (flat.float() / (res - 1)) * 2 - 1
												SCREAMING_SNAKE_CASE           =    fracs * torch.tan(fov / 2 )

												SCREAMING_SNAKE_CASE           =    fracs.view(_UpperCamelCase		,							-1		,							2 )
												SCREAMING_SNAKE_CASE           =    (
												    self.z.view(_UpperCamelCase		,							1		,							3 )
												    + self.x.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, :1]
												    + self.y.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, 1:]
												)
												SCREAMING_SNAKE_CASE           =    directions / directions.norm(dim=-1		,							keepdim=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        torch.broadcast_to(self.origin.view(_UpperCamelCase		,							1		,							3 )		,							[batch_size, directions.shape[1], 3] ),
												        directions,
												    ]		,							dim=2		,							)
												return rays.view(_UpperCamelCase		,							*_UpperCamelCase		,							2		,							3 )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int )   ->			"DifferentiableProjectiveCamera":



												'''simple docstring'''

												assert width * self.height == height * self.width, "The aspect ratio should not change."
												return DifferentiableProjectiveCamera(
												    origin=self.origin		,							x=self.x		,							y=self.y		,							z=self.z		,							width=_UpperCamelCase		,							height=_UpperCamelCase		,							x_fov=self.x_fov		,							y_fov=self.y_fov		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					for theta in np.linspace(0 ,	2 * np.pi ,	num=2_0					):
										SCREAMING_SNAKE_CASE           =    np.array([np.sin(UpperCAmelCase__					), np.cos(UpperCAmelCase__					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										SCREAMING_SNAKE_CASE           =    -z * 4
										SCREAMING_SNAKE_CASE           =    np.array([np.cos(UpperCAmelCase__					), -np.sin(UpperCAmelCase__					), 0.0]					)
										SCREAMING_SNAKE_CASE           =    np.cross(UpperCAmelCase__ ,	UpperCAmelCase__					)
										origins.append(UpperCAmelCase__					)
										xs.append(UpperCAmelCase__					)
										ys.append(UpperCAmelCase__					)
										zs.append(UpperCAmelCase__					)
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	x=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	y=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	z=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	width=UpperCAmelCase__ ,	height=UpperCAmelCase__ ,	x_fov=0.7 ,	y_fov=0.7 ,	shape=(1, len(UpperCAmelCase__					)) ,	)




