




'''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      __a     (       _UpperCamelCase:   Optional[Any]   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				384
     if "tiny" in model_name:
          _snake_case							=				[3, 3, 9, 3]
          _snake_case							=				[96, 192, 384, 768]
     if "small" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[96, 192, 384, 768]
     if "base" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[128, 256, 512, 1_024]
          _snake_case							=				512
     if "large" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[192, 384, 768, 1_536]
          _snake_case							=				768
     if "xlarge" in model_name:
          _snake_case							=				[3, 3, 27, 3]
          _snake_case							=				[256, 512, 1_024, 2_048]
          _snake_case							=				1_024

     # set label information
     _snake_case							=				150
     _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      __a     (       _UpperCamelCase:   Dict   )						->			Optional[int]:

     """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      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Dict   )						->			Union[str, Any]:

     """simple docstring"""




     _snake_case							=				dct.pop(_UpperCamelCase   )
     _snake_case							=				val





def      __a     (       _UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Dict   )						->			Dict:

     """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(
              [[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]   )
     elif model_name == "upernet-convnext-small":
          _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":
          _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":
          _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":
          _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__":
   UpperCamelCase_						:      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.'''
   )

   UpperCamelCase_						:      Optional[Any]       =						parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





from manim import *

class        _a    (						__lowerCAmelCase							):






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				Rectangle(height=0.5 ,width=0.5   )
          _snake_case							=				Rectangle(height=0.4_6 ,width=0.4_6   ).set_stroke(width=0   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				VGroup(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("CPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          cpu.move_to([-2.5, -0.5, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(4   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )
          _snake_case							=				Text("GPU" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          gpu.move_to([-1, -1, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Model" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0.5 ,aligned_edge=_SCREAMING_SNAKE_CASE   )
          model.move_to([3, -1.0, 0]   )
          self.add(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               rect.set_stroke(_SCREAMING_SNAKE_CASE   )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               _snake_case							=				Rectangle(height=0.4_6 / 4 ,width=0.4_6 / 3   ).set_stroke(width=0.0   ).set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )

               if i == 0:
                    cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   ) ,buff=0.0_2 ,direction=_SCREAMING_SNAKE_CASE   )
                    cpu_target.set_x(cpu_target.get_x() + 0.1   )
               elif i == 3:
                    cpu_target.next_to(cpu_targs[0] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               else:
                    cpu_target.next_to(cpu_targs[i - 1] ,direction=_SCREAMING_SNAKE_CASE ,buff=0.0   )
               self.add(_SCREAMING_SNAKE_CASE   )
               cpu_targs.append(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				[mem.copy() for i in range(6   )]
          _snake_case							=				VGroup(*_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,buff=0   )

          _snake_case							=				Text("Loaded Checkpoint" ,font_size=24   )
          _snake_case							=				Group(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ).arrange(_SCREAMING_SNAKE_CASE ,aligned_edge=_SCREAMING_SNAKE_CASE ,buff=0.4   )
          checkpoint.move_to([3, 0.5, 0]   )

          _snake_case							=				Square(side_length=2.2   )
          key.move_to([-5, 2, 0]   )

          _snake_case							=				MarkupText(
              f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" ,font_size=18 ,)

          key_text.move_to([-5, 2.4, 0]   )

          self.add(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				MarkupText(
              f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,font_size=18 ,)

          blue_text.next_to(_SCREAMING_SNAKE_CASE ,DOWN * 2.4 ,aligned_edge=key_text.get_left()   )

          _snake_case							=				MarkupText(
              f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" ,font_size=24 ,)
          step_a.move_to([2, 2, 0]   )
          self.play(Write(_SCREAMING_SNAKE_CASE   ) ,Write(_SCREAMING_SNAKE_CASE   )   )

          self.play(Write(_SCREAMING_SNAKE_CASE ,run_time=1   ) ,Create(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

          _snake_case							=				[]
          _snake_case							=				[]
          for i, rect in enumerate(_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				fill.copy().set_fill(_SCREAMING_SNAKE_CASE ,opacity=0.7   )
               target.move_to(_SCREAMING_SNAKE_CASE   )
               first_animations.append(GrowFromCenter(_SCREAMING_SNAKE_CASE ,run_time=1   )   )

               _snake_case							=				target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
               second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE ,run_time=1.5   )   )

          self.play(*_SCREAMING_SNAKE_CASE   )
          self.play(*_SCREAMING_SNAKE_CASE   )
          self.wait()


