






"""simple docstring"""







import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			SwinConfig.from_pretrained(
           """microsoft/swin-tiny-patch4-window7-224"""	,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
       UpperCAmelCase	    =			MaskFormerConfig(backbone_config=lowerCAmelCase		)

       UpperCAmelCase	    =			"""huggingface/label-files"""
       if "ade20k-full" in model_name:
              # this should be ok
              UpperCAmelCase	    =			847
              UpperCAmelCase	    =			"""maskformer-ade20k-full-id2label.json"""
       elif "ade" in model_name:
              # this should be ok
              UpperCAmelCase	    =			150
              UpperCAmelCase	    =			"""ade20k-id2label.json"""
       elif "coco-stuff" in model_name:
              # this should be ok
              UpperCAmelCase	    =			171
              UpperCAmelCase	    =			"""maskformer-coco-stuff-id2label.json"""
       elif "coco" in model_name:
              # TODO
              UpperCAmelCase	    =			133
              UpperCAmelCase	    =			"""coco-panoptic-id2label.json"""
       elif "cityscapes" in model_name:
              # this should be ok
              UpperCAmelCase	    =			19
              UpperCAmelCase	    =			"""cityscapes-id2label.json"""
       elif "vistas" in model_name:
              # this should be ok
              UpperCAmelCase	    =			65
              UpperCAmelCase	    =			"""mapillary-vistas-id2label.json"""

       UpperCAmelCase	    =			json.load(open(hf_hub_download(lowerCAmelCase	,      lowerCAmelCase	,      repo_type="""dataset"""		)	,      """r"""		)		)
       UpperCAmelCase	    =			{int(lowerCAmelCase		): v for k, v in idalabel.items()}

       return config






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       # stem
       # fmt: off
       rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")		)
       rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")		)
       rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")		)
       rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

              if i < 3:
                     rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')		)
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')		)
              rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')		)
              rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')		)

       # FPN
       rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")		)
       rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")		)
       rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")		)
       for source_index, target_index in zip(range(3	,      0	,      -1		)	,      range(0	,      3		)		):
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')		)
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')		)
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')		)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')		)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')		)
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')		)
       rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")		)
       rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")		)

       # Transformer decoder
       for idx in range(config.decoder_config.decoder_layers		):
              # self-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')		)
              # cross-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')		)
              # MLP 1
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')		)
              # MLP 2
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')		)
              # layernorm 1 (self-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')		)
              # layernorm 2 (cross-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')		)
              # layernorm 3 (final layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')		)

       rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")		)
       rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")		)

       # heads on top
       rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")		)

       rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")		)
       rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")		)

       rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")		)
       rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")		)

       for i in range(3		):
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')		)
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')		)
       # fmt: on

       return rename_keys






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			dct.pop(lowerCAmelCase		)
       UpperCAmelCase	    =			val






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
       for i in range(len(backbone_config.depths		)		):
              UpperCAmelCase	    =			num_features[i]
              for j in range(backbone_config.depths[i]		):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     UpperCAmelCase	    =			state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
                     UpperCAmelCase	    =			state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
                     # next, add query, keys and values (in that order) to the state dict
                     UpperCAmelCase	    =			in_proj_weight[:dim, :]
                     UpperCAmelCase	    =			in_proj_bias[: dim]
                     UpperCAmelCase	    =			in_proj_weight[
                         dim : dim * 2, :
                     ]
                     UpperCAmelCase	    =			in_proj_bias[
                         dim : dim * 2
                     ]
                     UpperCAmelCase	    =			in_proj_weight[
                         -dim :, :
                     ]
                     UpperCAmelCase	    =			in_proj_bias[-dim :]
                     # fmt: on






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       # fmt: off
       UpperCAmelCase	    =			config.decoder_config.hidden_size
       for idx in range(config.decoder_config.decoder_layers		):
              # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
              UpperCAmelCase	    =			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''		)
              UpperCAmelCase	    =			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''		)
              # next, add query, keys and values (in that order) to the state dict
              UpperCAmelCase	    =			in_proj_weight[: hidden_size, :]
              UpperCAmelCase	    =			in_proj_bias[:config.hidden_size]
              UpperCAmelCase	    =			in_proj_weight[hidden_size : hidden_size * 2, :]
              UpperCAmelCase	    =			in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase	    =			in_proj_weight[-hidden_size :, :]
              UpperCAmelCase	    =			in_proj_bias[-hidden_size :]
              # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
              UpperCAmelCase	    =			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''		)
              UpperCAmelCase	    =			state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''		)
              # next, add query, keys and values (in that order) to the state dict
              UpperCAmelCase	    =			in_proj_weight[: hidden_size, :]
              UpperCAmelCase	    =			in_proj_bias[:config.hidden_size]
              UpperCAmelCase	    =			in_proj_weight[hidden_size : hidden_size * 2, :]
              UpperCAmelCase	    =			in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase	    =			in_proj_weight[-hidden_size :, :]
              UpperCAmelCase	    =			in_proj_bias[-hidden_size :]
       # fmt: on






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase	    =			Image.open(requests.get(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)
       return im






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase = False		):







       '''simple docstring'''
       UpperCAmelCase	    =			get_maskformer_config(lowerCAmelCase		)

       # load original state_dict
       with open(lowerCAmelCase	,      """rb"""		) as f:
              UpperCAmelCase	    =			pickle.load(lowerCAmelCase		)
       UpperCAmelCase	    =			data["""model"""]

       # for name, param in state_dict.items():
       #     print(name, param.shape)

       # rename keys
       UpperCAmelCase	    =			create_rename_keys(lowerCAmelCase		)
       for src, dest in rename_keys:
              rename_key(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       read_in_swin_q_k_v(lowerCAmelCase	,      config.backbone_config		)
       read_in_decoder_q_k_v(lowerCAmelCase	,      lowerCAmelCase		)

       # update to torch tensors
       for key, value in state_dict.items():
              UpperCAmelCase	    =			torch.from_numpy(lowerCAmelCase		)

       # load 🤗 model
       UpperCAmelCase	    =			MaskFormerForInstanceSegmentation(lowerCAmelCase		)
       model.eval()

       for name, param in model.named_parameters():
              print(lowerCAmelCase	,      param.shape		)

       UpperCAmelCase    ,       UpperCAmelCase	    =			model.load_state_dict(lowerCAmelCase	,      strict=lowerCAmelCase		)
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(lowerCAmelCase		) == 0, F'''Unexpected keys: {unexpected_keys}'''

       # verify results
       UpperCAmelCase	    =			prepare_img()
       if "vistas" in model_name:
              UpperCAmelCase	    =			65
       elif "cityscapes" in model_name:
              UpperCAmelCase	    =			65535
       else:
              UpperCAmelCase	    =			255
       UpperCAmelCase	    =			True if """ade""" in model_name else False
       UpperCAmelCase	    =			MaskFormerImageProcessor(ignore_index=lowerCAmelCase	,      reduce_labels=lowerCAmelCase		)

       UpperCAmelCase	    =			image_processor(lowerCAmelCase	,      return_tensors="""pt"""		)

       UpperCAmelCase	    =			model(**lowerCAmelCase		)

       print("""Logits:"""	,      outputs.class_queries_logits[0, :3, :3]		)

       if model_name == "maskformer-swin-tiny-ade":
              UpperCAmelCase	    =			torch.tensor(
                  [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]		)
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3]	,      lowerCAmelCase	,      atol=1e-4		)
       print("""Looks ok!"""		)

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''		)
              Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
              model.save_pretrained(lowerCAmelCase		)
              image_processor.save_pretrained(lowerCAmelCase		)

       if push_to_hub:
              print("""Pushing model and image processor to the hub..."""		)
              model.push_to_hub(F'''nielsr/{model_name}'''		)
              image_processor.push_to_hub(F'''nielsr/{model_name}'''		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Any            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''maskformer-swin-tiny-ade''',
     type=str,
     help=('''Name of the MaskFormer model you\'d like to convert''',),
 )
 parser.add_argument(
     '''--checkpoint_path''',
     default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
     type=str,
     help='''Path to the original state dict (.pth file).''',
 )
 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.'''
 )

 lowerCAmelCase_	:    List[Any]            =      parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_	:    Optional[int]            =      {
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_	:    str            =      [
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_	:    Optional[Any]            =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
