


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      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   3_8_4
   if "tiny" in model_name:
      UpperCAmelCase_							:      Any						=   [3, 3, 9, 3]
      UpperCAmelCase_							:      Union[str, Any]						=   [9_6, 1_9_2, 3_8_4, 7_6_8]
   if "small" in model_name:
      UpperCAmelCase_							:      Optional[int]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Dict						=   [9_6, 1_9_2, 3_8_4, 7_6_8]
   if "base" in model_name:
      UpperCAmelCase_							:      List[str]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Optional[Any]						=   [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
      UpperCAmelCase_							:      Any						=   5_1_2
   if "large" in model_name:
      UpperCAmelCase_							:      Optional[int]						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      Dict						=   [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
      UpperCAmelCase_							:      int						=   7_6_8
   if "xlarge" in model_name:
      UpperCAmelCase_							:      Any						=   [3, 3, 2_7, 3]
      UpperCAmelCase_							:      int						=   [2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]
      UpperCAmelCase_							:      Optional[int]						=   1_0_2_4

   # set label information
   UpperCAmelCase_							:      Dict						=   1_5_0
   UpperCAmelCase_							:      Any						=   '''huggingface/label-files'''
   UpperCAmelCase_							:      Tuple						=   '''ade20k-id2label.json'''
   UpperCAmelCase_							:      Optional[int]						=   json.load(open(hf_hub_download(__lowercase			,       __lowercase			,       repo_type='''dataset'''       )			,       '''r'''       )       )
   UpperCAmelCase_							:      Union[str, Any]						=   {int(__lowercase       ): v for k, v in idalabel.items()}
   UpperCAmelCase_							:      List[str]						=   {v: k for k, v in idalabel.items()}

   UpperCAmelCase_							:      int						=   ConvNextConfig(
       depths=__lowercase			,       hidden_sizes=__lowercase			,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
   UpperCAmelCase_							:      List[Any]						=   UperNetConfig(
       backbone_config=__lowercase			,       auxiliary_in_channels=__lowercase			,       num_labels=__lowercase			,       idalabel=__lowercase			,       labelaid=__lowercase			,       )

   return config
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   []

   # 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      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   dct.pop(__lowercase       )
   UpperCAmelCase_							:      List[Any]						=   val
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      str						=   {
       '''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''',
   }
   UpperCAmelCase_							:      List[str]						=   model_name_to_url[model_name]
   UpperCAmelCase_							:      List[Any]						=   torch.hub.load_state_dict_from_url(__lowercase			,       map_location='''cpu'''       )['''state_dict''']

   UpperCAmelCase_							:      Optional[Any]						=   get_upernet_config(__lowercase       )
   UpperCAmelCase_							:      List[Any]						=   UperNetForSemanticSegmentation(__lowercase       )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      UpperCAmelCase_							:      str						=   state_dict.pop(__lowercase       )
      if "bn" in key:
         UpperCAmelCase_							:      Tuple						=   key.replace('''bn'''			,       '''batch_norm'''       )
      UpperCAmelCase_							:      Optional[int]						=   val

   # rename keys
   UpperCAmelCase_							:      List[Any]						=   create_rename_keys(__lowercase       )
   for src, dest in rename_keys:
      rename_key(__lowercase			,       __lowercase			,       __lowercase       )

   model.load_state_dict(__lowercase       )

   # verify on image
   UpperCAmelCase_							:      Optional[int]						=   '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
   UpperCAmelCase_							:      Union[str, Any]						=   Image.open(requests.get(__lowercase			,       stream=__lowercase       ).raw       ).convert('''RGB'''       )

   UpperCAmelCase_							:      List[Any]						=   SegformerImageProcessor()
   UpperCAmelCase_							:      int						=   processor(__lowercase			,       return_tensors='''pt'''       ).pixel_values

   with torch.no_grad():
      UpperCAmelCase_							:      Tuple						=   model(__lowercase       )

   if model_name == "upernet-convnext-tiny":
      UpperCAmelCase_							:      Optional[Any]						=   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":
      UpperCAmelCase_							:      Union[str, Any]						=   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":
      UpperCAmelCase_							:      List[Any]						=   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":
      UpperCAmelCase_							:      Dict						=   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":
      UpperCAmelCase_							:      Any						=   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]			,       __lowercase			,       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(__lowercase       )
      print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
      processor.save_pretrained(__lowercase       )

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



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)