


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

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

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

   return config
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      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      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   dct.pop(_A       )
   UpperCAmelCase_							:      Dict						=   val
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      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",
   }
   UpperCAmelCase_							:      Union[str, Any]						=   model_name_to_url[model_name]
   UpperCAmelCase_							:      Tuple						=   torch.hub.load_state_dict_from_url(_A			,       map_location='''cpu'''       )["state_dict"]

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

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

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

   model.load_state_dict(_A       )

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

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

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

   if model_name == "upernet-convnext-tiny":
      UpperCAmelCase_							:      Optional[int]						=   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_							:      Tuple						=   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_							:      str						=   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_							:      Union[str, Any]						=   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_							:      Tuple						=   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]			,       _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__":
       __UpperCamelCase				:  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.'
       )

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase				:  str      					=							{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Any      					=							[
           'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'XCLIPModel',
           'XCLIPPreTrainedModel',
           'XCLIPTextModel',
           'XCLIPVisionModel',
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       __UpperCamelCase				:  List[str]      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)