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				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		3_84
     if "tiny" in model_name:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		[3, 3, 9, 3]
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[96, 1_92, 3_84, 7_68]
     if "small" in model_name:
          SCREAMING_SNAKE_CASE__				:				Dict			=		[3, 3, 27, 3]
          SCREAMING_SNAKE_CASE__				:				Dict			=		[96, 1_92, 3_84, 7_68]
     if "base" in model_name:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		[3, 3, 27, 3]
          SCREAMING_SNAKE_CASE__				:				Any			=		[1_28, 2_56, 5_12, 10_24]
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		5_12
     if "large" in model_name:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		[3, 3, 27, 3]
          SCREAMING_SNAKE_CASE__				:				Any			=		[1_92, 3_84, 7_68, 15_36]
          SCREAMING_SNAKE_CASE__				:				List[str]			=		7_68
     if "xlarge" in model_name:
          SCREAMING_SNAKE_CASE__				:				Any			=		[3, 3, 27, 3]
          SCREAMING_SNAKE_CASE__				:				Dict			=		[2_56, 5_12, 10_24, 20_48]
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		10_24

     # set label information
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		1_50
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'huggingface/label-files'
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'ade20k-id2label.json'
     SCREAMING_SNAKE_CASE__				:				List[str]			=		json.load(open(hf_hub_download(lowercase__     ,  lowercase__     ,  repo_type='dataset' )     ,  'r' ) )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{int(lowercase__ ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__				:				Any			=		{v: k for k, v in idalabel.items()}

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

     return config







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				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     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		dct.pop(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		val







def     _a				(      lowercase__  :			int     ,  lowercase__  :			List[str]     ,  lowercase__  :			Union[str, Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, 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',
     }
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model_name_to_url[model_name]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.hub.load_state_dict_from_url(lowercase__     ,  map_location='cpu' )['state_dict']

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		get_upernet_config(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				int			=		UperNetForSemanticSegmentation(lowercase__ )
     model.eval()

     # replace "bn" => "batch_norm"
     for key in state_dict.copy().keys():
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		state_dict.pop(lowercase__ )
          if "bn" in key:
               SCREAMING_SNAKE_CASE__				:				int			=		key.replace('bn'     ,  'batch_norm' )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		val

     # rename keys
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_rename_keys(lowercase__ )
     for src, dest in rename_keys:
          rename_key(lowercase__     ,  lowercase__     ,  lowercase__ )

     model.load_state_dict(lowercase__ )

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

     SCREAMING_SNAKE_CASE__				:				List[str]			=		SegformerImageProcessor()
     SCREAMING_SNAKE_CASE__				:				List[str]			=		processor(lowercase__     ,  return_tensors='pt' ).pixel_values

     with torch.no_grad():
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(lowercase__ )

     if model_name == "upernet-convnext-tiny":
          SCREAMING_SNAKE_CASE__				:				Any			=		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__				:				List[str]			=		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__				:				Optional[Any]			=		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__				:				Dict			=		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__				:				List[Any]			=		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]     ,  lowercase__     ,  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(lowercase__ )
          print(f'''Saving processor to {pytorch_dump_folder_path}''' )
          processor.save_pretrained(lowercase__ )

     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__		:					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."
      )

      SCREAMING_SNAKE_CASE__		:					int						=					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       snake_case					:
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42  # [batch_size x 3]
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     42




  def 	__lowercase(      self     :	List[Any]				)->       Union[str, Any]:




       """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 	__lowercase(      self     :	Dict				)->       Tuple:




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




  def 	__lowercase(      self     :	Dict				)->       Union[str, Any]:




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




  def 	__lowercase(      self     :	Tuple				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		torch.arange(self.height * self.width				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.stack(
           [
               pixel_indices % self.width,
               torch.div(a_     , self.width     , rounding_mode='trunc'				),
           ]     , axis=1     , )
       return coords




  @property
  def 	__lowercase(      self     :	Any				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.shape
       SCREAMING_SNAKE_CASE__				:				Tuple			=		int(np.prod(a_				)				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_image_coords()
       SCREAMING_SNAKE_CASE__				:				Dict			=		torch.broadcast_to(coords.unsqueeze(0				)     , [batch_size * inner_batch_size, *coords.shape]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_camera_rays(a_				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		rays.view(a_     , inner_batch_size * self.height * self.width     , 2     , 3				)

       return rays




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	torch.Tensor				)->       torch.Tensor:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							*SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		coords.shape
       assert n_coords == 2
       assert batch_size == self.origin.shape[0]

       SCREAMING_SNAKE_CASE__				:				str			=		coords.view(a_     , -1     , 2				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.resolution()
       SCREAMING_SNAKE_CASE__				:				str			=		self.fov()

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

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




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	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=a_     , height=a_     , x_fov=self.x_fov     , y_fov=self.y_fov     , )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		[]
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				str			=		[]
     for theta in np.linspace(0     ,  2 * np.pi     ,  num=20 ):
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.sin(lowercase__ ), np.cos(lowercase__ ), -0.5] )
          z /= np.sqrt(np.sum(z**2 ) )
          SCREAMING_SNAKE_CASE__				:				Tuple			=		-z * 4
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		np.array([np.cos(lowercase__ ), -np.sin(lowercase__ ), 0.0] )
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		np.cross(lowercase__     ,  lowercase__ )
          origins.append(lowercase__ )
          xs.append(lowercase__ )
          ys.append(lowercase__ )
          zs.append(lowercase__ )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  x=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  y=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  z=torch.from_numpy(np.stack(lowercase__     ,  axis=0 ) ).float()     ,  width=lowercase__     ,  height=lowercase__     ,  x_fov=0.7     ,  y_fov=0.7     ,  shape=(1, len(lowercase__ ))     ,  )



