"""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    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							3_84
     if "tiny" in model_name:
          __snake_case				=							[3, 3, 9, 3]
          __snake_case				=							[96, 1_92, 3_84, 7_68]
     if "small" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[96, 1_92, 3_84, 7_68]
     if "base" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[1_28, 2_56, 5_12, 10_24]
          __snake_case				=							5_12
     if "large" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[1_92, 3_84, 7_68, 15_36]
          __snake_case				=							7_68
     if "xlarge" in model_name:
          __snake_case				=							[3, 3, 27, 3]
          __snake_case				=							[2_56, 5_12, 10_24, 20_48]
          __snake_case				=							10_24

     # set label information
     __snake_case				=							1_50
     __snake_case				=							'''huggingface/label-files'''
     __snake_case				=							'''ade20k-id2label.json'''
     __snake_case				=							json.load(open(hf_hub_download(snake_case,      snake_case,      repo_type='''dataset'''),      '''r'''))
     __snake_case				=							{int(snake_case): v for k, v in idalabel.items()}
     __snake_case				=							{v: k for k, v in idalabel.items()}

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

     return config





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							dct.pop(snake_case)
     __snake_case				=							val





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __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(snake_case,      map_location='''cpu''')['''state_dict''']

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

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

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

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

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

     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],      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__":
       __lowercase		:		Dict														=							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."
       )

       __lowercase		:		int														=							parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))