"""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    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Optional[Any]:





						"""simple docstring"""






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

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

						snake_case								=			ConvNextConfig(
						    depths=_UpperCamelCase ,			hidden_sizes=_UpperCamelCase ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']					)
						snake_case								=			UperNetConfig(
						    backbone_config=_UpperCamelCase ,			auxiliary_in_channels=_UpperCamelCase ,			num_labels=_UpperCamelCase ,			idalabel=_UpperCamelCase ,			labelaid=_UpperCamelCase ,			)

						return config





def    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple					)					->				List[Any]:





						"""simple docstring"""






						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       :				Optional[Any] ,			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				Any					)					->				Optional[Any]:





						"""simple docstring"""






						snake_case								=			dct.pop(_UpperCamelCase					)
						snake_case								=			val





def    lowerCAmelCase__    (			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				Union[str, Any] ,			_UpperCamelCase       :				str					)					->				Tuple:





						"""simple docstring"""






						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',
						}
						snake_case								=			model_name_to_url[model_name]
						snake_case								=			torch.hub.load_state_dict_from_url(_UpperCamelCase ,			map_location='cpu'					)['state_dict']

						snake_case								=			get_upernet_config(_UpperCamelCase					)
						snake_case								=			UperNetForSemanticSegmentation(_UpperCamelCase					)
						model.eval()

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

						# rename keys
						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
						snake_case								=			'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
						snake_case								=			Image.open(requests.get(_UpperCamelCase ,			stream=_UpperCamelCase					).raw					).convert('RGB'					)

						snake_case								=			SegformerImageProcessor()
						snake_case								=			processor(_UpperCamelCase ,			return_tensors='pt'					).pixel_values

						with torch.no_grad():
												snake_case								=			model(_UpperCamelCase					)

						if model_name == "upernet-convnext-tiny":
												snake_case								=			torch.tensor(
												    [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]					)
						elif model_name == "upernet-convnext-small":
												snake_case								=			torch.tensor(
												    [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]					)
						elif model_name == "upernet-convnext-base":
												snake_case								=			torch.tensor(
												    [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]					)
						elif model_name == "upernet-convnext-large":
												snake_case								=			torch.tensor(
												    [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]					)
						elif model_name == "upernet-convnext-xlarge":
												snake_case								=			torch.tensor(
												    [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]					)
						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__":
					SCREAMING_SNAKE_CASE__      						=  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."
					)

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







"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


