






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

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

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

     return config





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					[]

     # 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      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(__snake_case       )
     __lowerCAmelCase								=					val





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					{
         "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",
     }
     __lowerCAmelCase								=					model_name_to_url[model_name]
     __lowerCAmelCase								=					torch.hub.load_state_dict_from_url(__snake_case					, map_location="cpu"       )["state_dict"]

     __lowerCAmelCase								=					get_upernet_config(__snake_case       )
     __lowerCAmelCase								=					UperNetForSemanticSegmentation(__snake_case       )
     model.eval()

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

     # rename keys
     __lowerCAmelCase								=					create_rename_keys(__snake_case       )
     for src, dest in rename_keys:
          rename_key(__snake_case					, __snake_case					, __snake_case       )

     model.load_state_dict(__snake_case       )

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

     __lowerCAmelCase								=					SegformerImageProcessor()
     __lowerCAmelCase								=					processor(__snake_case					, return_tensors="pt"       ).pixel_values

     with torch.no_grad():
          __lowerCAmelCase								=					model(__snake_case       )

     if model_name == "upernet-convnext-tiny":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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":
          __lowerCAmelCase								=					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]					, __snake_case					, 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(__snake_case       )
          print(F"""Saving processor to {pytorch_dump_folder_path}"""       )
          processor.save_pretrained(__snake_case       )

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














from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase			:					int         =	1.0_5457_1817E-34  # unit of ℏ : J * s

lowerCamelCase			:					Union[str, Any]         =	3E8  # unit of c : m * s^-1





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

     if (force, area, distance).count(0       ) != 1:
          raise ValueError("One and only one argument must be 0"       )
     if force < 0:
          raise ValueError("Magnitude of force can not be negative"       )
     if distance < 0:
          raise ValueError("Distance can not be negative"       )
     if area < 0:
          raise ValueError("Area can not be negative"       )
     if force == 0:
          __lowerCAmelCase								=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
              240 * (distance) ** 4
          )
          return {"force": force}
     elif area == 0:
          __lowerCAmelCase								=					(240 * force * (distance) ** 4) / (
              REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
          )
          return {"area": area}
     elif distance == 0:
          __lowerCAmelCase								=					(
              (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
          ) ** (1 / 4)
          return {"distance": distance}
     raise ValueError("One and only one argument must be 0"       )


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()






