


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    lowercase_	(	_A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						384
      if "tiny" in model_name:
            lowerCamelCase__	:       str					=						[3, 3, 9, 3]
            lowerCamelCase__	:       Optional[int]					=						[96, 192, 384, 768]
      if "small" in model_name:
            lowerCamelCase__	:       Union[str, Any]					=						[3, 3, 27, 3]
            lowerCamelCase__	:       Any					=						[96, 192, 384, 768]
      if "base" in model_name:
            lowerCamelCase__	:       List[Any]					=						[3, 3, 27, 3]
            lowerCamelCase__	:       Optional[int]					=						[128, 256, 512, 1024]
            lowerCamelCase__	:       Optional[int]					=						512
      if "large" in model_name:
            lowerCamelCase__	:       Optional[int]					=						[3, 3, 27, 3]
            lowerCamelCase__	:       Optional[Any]					=						[192, 384, 768, 1536]
            lowerCamelCase__	:       Optional[Any]					=						768
      if "xlarge" in model_name:
            lowerCamelCase__	:       List[Any]					=						[3, 3, 27, 3]
            lowerCamelCase__	:       Tuple					=						[256, 512, 1024, 2048]
            lowerCamelCase__	:       int					=						1024

      # set label information
      lowerCamelCase__	:       Optional[Any]					=						150
      lowerCamelCase__	:       int					=						"huggingface/label-files"
      lowerCamelCase__	:       Dict					=						"ade20k-id2label.json"
      lowerCamelCase__	:       Optional[int]					=						json.load(open(hf_hub_download(_A					,      _A					,      repo_type="dataset"		)					,      "r"		)		)
      lowerCamelCase__	:       List[str]					=						{int(_A		): v for k, v in idalabel.items()}
      lowerCamelCase__	:       Optional[int]					=						{v: k for k, v in idalabel.items()}

      lowerCamelCase__	:       Dict					=						ConvNextConfig(
          depths=_A					,      hidden_sizes=_A					,      out_features=["stage1", "stage2", "stage3", "stage4"]		)
      lowerCamelCase__	:       Optional[Any]					=						UperNetConfig(
          backbone_config=_A					,      auxiliary_in_channels=_A					,      num_labels=_A					,      idalabel=_A					,      labelaid=_A					,      )

      return config

def    lowercase_	(	_A    :					Any		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						[]

      # 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    lowercase_	(	_A    :					Optional[int]					,      _A    :					List[str]					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						dct.pop(_A		)
      lowerCamelCase__	:       Dict					=						val

def    lowercase_	(	_A    :					Tuple					,      _A    :					Optional[int]					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       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",
      }
      lowerCamelCase__	:       Union[str, Any]					=						model_name_to_url[model_name]
      lowerCamelCase__	:       Tuple					=						torch.hub.load_state_dict_from_url(_A					,      map_location="cpu"		)["state_dict"]

      lowerCamelCase__	:       Dict					=						get_upernet_config(_A		)
      lowerCamelCase__	:       Dict					=						UperNetForSemanticSegmentation(_A		)
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            lowerCamelCase__	:       Optional[int]					=						state_dict.pop(_A		)
            if "bn" in key:
                  lowerCamelCase__	:       Optional[int]					=						key.replace("bn"					,      "batch_norm"		)
            lowerCamelCase__	:       List[str]					=						val

      # rename keys
      lowerCamelCase__	:       int					=						create_rename_keys(_A		)
      for src, dest in rename_keys:
            rename_key(_A					,      _A					,      _A		)

      model.load_state_dict(_A		)

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

      lowerCamelCase__	:       Tuple					=						SegformerImageProcessor()
      lowerCamelCase__	:       List[Any]					=						processor(_A					,      return_tensors="pt"		).pixel_values

      with torch.no_grad():
            lowerCamelCase__	:       Optional[int]					=						model(_A		)

      if model_name == "upernet-convnext-tiny":
            lowerCamelCase__	:       Optional[int]					=						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__	:       Tuple					=						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__	:       str					=						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__	:       Union[str, Any]					=						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__	:       Tuple					=						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]					,      _A					,      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(_A		)
            print(F"Saving processor to {pytorch_dump_folder_path}"		)
            processor.save_pretrained(_A		)

      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__":
    A   :					int	=      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."
    )

    A   :					Union[str, Any]	=      parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


