

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  (					A__   ):






 """simple docstring"""



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

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

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

 return config


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]

 # 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  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				dct.pop(_snake_case   )
 __lowercase													=				val


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				{
     '''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''',
 }
 __lowercase													=				model_name_to_url[model_name]
 __lowercase													=				torch.hub.load_state_dict_from_url(_snake_case  ,       map_location='''cpu'''   )['''state_dict''']

 __lowercase													=				get_upernet_config(_snake_case   )
 __lowercase													=				UperNetForSemanticSegmentation(_snake_case   )
 model.eval()

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

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

 __lowercase													=				SegformerImageProcessor()
 __lowercase													=				processor(_snake_case  ,       return_tensors='''pt'''   ).pixel_values

 with torch.no_grad():
  __lowercase													=				model(_snake_case   )

 if model_name == "upernet-convnext-tiny":
  __lowercase													=				torch.tensor(
      [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]   )
 elif model_name == "upernet-convnext-small":
  __lowercase													=				torch.tensor(
      [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]   )
 elif model_name == "upernet-convnext-base":
  __lowercase													=				torch.tensor(
      [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]   )
 elif model_name == "upernet-convnext-large":
  __lowercase													=				torch.tensor(
      [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]   )
 elif model_name == "upernet-convnext-xlarge":
  __lowercase													=				torch.tensor(
      [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]   )
 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__      		=			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__      		=			parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__      		=			numpy.array([0, 0])
lowerCAmelCase__      		=			numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__      		=			numpy.array([1, 0])
lowerCAmelCase__      		=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				initial_vectors
 for _ in range(A__   ):
  __lowercase													=				iteration_step(A__   )
 return vectors


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for i, start_vector in enumerate(vectors[:-1]   ):
  __lowercase													=				vectors[i + 1]
  new_vectors.append(A__   )
  __lowercase													=				end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3   )
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3  ,       60   )   )
  new_vectors.append(start_vector + difference_vector * 2 / 3   )
 new_vectors.append(vectors[-1]   )
 return new_vectors


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				numpy.radians(A__   )
 __lowercase						,			__lowercase													=				numpy.cos(A__   ), numpy.sin(A__   )
 __lowercase													=				numpy.array(((c, -s), (s, c))   )
 return numpy.dot(A__  ,       A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				plt.gca()
 axes.set_aspect('''equal'''   )

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 __lowercase						,			__lowercase													=				zip(*A__   )
 plt.plot(A__  ,       A__   )
 plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__      		=			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)

