

"""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[int])     ->						List[str]:
   '''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(UpperCamelCase_,      UpperCamelCase_,      repo_type="dataset"),      "r"))
   __lowercase								=						{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase								=						{v: k for k, v in idalabel.items()}

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

   return config




def   _A  (  UpperCamelCase_					:	List[str])     ->						str:
   '''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  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Tuple)     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						dct.pop(UpperCamelCase_)
   __lowercase								=						val




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Tuple)     ->						Union[str, Any]:
   '''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(UpperCamelCase_,      map_location="cpu")["state_dict"]

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

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

   # rename keys
   __lowercase								=						create_rename_keys(UpperCamelCase_)
   for src, dest in rename_keys:
      rename_key(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)

   model.load_state_dict(UpperCamelCase_)

   # verify on image
   __lowercase								=						"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
   __lowercase								=						Image.open(requests.get(UpperCamelCase_,      stream=UpperCamelCase_).raw).convert("RGB")

   __lowercase								=						SegformerImageProcessor()
   __lowercase								=						processor(UpperCamelCase_,      return_tensors="pt").pixel_values

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

   if model_name == "upernet-convnext-tiny":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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],      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__":
   _a							=							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							=							parser.parse_args()
   convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
   # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
   # setup instructions, if using on-demand hardware

   # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
   # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
   # Throw an error if user passes both BYO and on-demand cluster args
   # Otherwise, use default values
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--user', type=str, default='ubuntu')
   parser.add_argument('--host', type=str, default='localhost')
   parser.add_argument('--key_path', type=str, default=None)
   parser.add_argument('--instance', type=str, default='V100:1')
   parser.add_argument('--provider', type=str, default='cheapest')
   parser.add_argument('--use_spot', type=bool, default=False)
   parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
   _a				,						_a							=							parser.parse_known_args()
   if args.host != "localhost":
      if args.instance != "V100:1" or args.provider != "cheapest":
         raise ValueError('Cannot specify both BYO and on-demand cluster args')
      _a							=							rh.cluster(
          name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
      )
   else:
      _a							=							rh.cluster(
          name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
      )
   _a							=							args.example.rsplit('/', 1)[0]

   # Set up remote environment
   cluster.install_packages(['pip:./'])  # Installs transformers from local source
   # Note transformers is copied into the home directory on the remote machine, so we can install from there
   cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
   cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

   # Run example. You can bypass the CLI wrapper and paste your own code here.
   cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

   # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
   # from my_script... import train
   # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
   # launch_train_gpu = rh.function(fn=train,
   #                                system=gpu,
   #                                reqs=reqs,
   #                                name='train_bert_glue')
   #
   # We can pass in arguments just like we would to a function:
   # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
   #                  stream_logs=True)




