


'''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()
UpperCAmelCase_			:  Dict										=   logging.get_logger(__name__)


def       _lowercase							(					UpperCamelCase__							:       str      ):
 __A	:		List[str]	  =      SwinConfig.from_pretrained(
     'microsoft/swin-tiny-patch4-window7-224',					out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
 __A	:		int	  =      MaskFormerConfig(backbone_config=UpperCamelCase__      )

 __A	:		int	  =      'huggingface/label-files'
 if "ade20k-full" in model_name:
  # this should be ok
  __A	:		List[Any]	  =      847
  __A	:		Dict	  =      'maskformer-ade20k-full-id2label.json'
 elif "ade" in model_name:
  # this should be ok
  __A	:		Tuple	  =      150
  __A	:		Union[str, Any]	  =      'ade20k-id2label.json'
 elif "coco-stuff" in model_name:
  # this should be ok
  __A	:		List[Any]	  =      171
  __A	:		List[str]	  =      'maskformer-coco-stuff-id2label.json'
 elif "coco" in model_name:
  # TODO
  __A	:		Any	  =      133
  __A	:		Union[str, Any]	  =      'coco-panoptic-id2label.json'
 elif "cityscapes" in model_name:
  # this should be ok
  __A	:		Optional[Any]	  =      19
  __A	:		int	  =      'cityscapes-id2label.json'
 elif "vistas" in model_name:
  # this should be ok
  __A	:		List[Any]	  =      65
  __A	:		Dict	  =      'mapillary-vistas-id2label.json'

 __A	:		List[str]	  =      json.load(open(hf_hub_download(UpperCamelCase__,					UpperCamelCase__,					repo_type='dataset'      ),					'r'      )      )
 __A	:		int	  =      {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

 return config


def       _lowercase							(					UpperCamelCase__							:       List[Any]      ):
 __A	:		Dict	  =      []
 # 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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Tuple,					UpperCamelCase__							:       Any      ):
 __A	:		Union[str, Any]	  =      dct.pop(UpperCamelCase__      )
 __A	:		Dict	  =      val


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       int      ):
 __A	:		Optional[int]	  =      [int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
 for i in range(len(backbone_config.depths      )      ):
  __A	:		str	  =      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)
   __A	:		Optional[int]	  =      state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""      )
   __A	:		Optional[int]	  =      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
   __A	:		str	  =      in_proj_weight[:dim, :]
   __A	:		List[Any]	  =      in_proj_bias[: dim]
   __A	:		Optional[int]	  =      in_proj_weight[
       dim : dim * 2, :
   ]
   __A	:		Any	  =      in_proj_bias[
       dim : dim * 2
   ]
   __A	:		Optional[Any]	  =      in_proj_weight[
       -dim :, :
   ]
   __A	:		Tuple	  =      in_proj_bias[-dim :]
   # fmt: on


def       _lowercase							(					UpperCamelCase__							:       Optional[int],					UpperCamelCase__							:       str      ):
 # fmt: off
 __A	:		Optional[int]	  =      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)
  __A	:		Dict	  =      state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
  __A	:		List[Any]	  =      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
  __A	:		Optional[int]	  =      in_proj_weight[: hidden_size, :]
  __A	:		Optional[Any]	  =      in_proj_bias[:config.hidden_size]
  __A	:		str	  =      in_proj_weight[hidden_size : hidden_size * 2, :]
  __A	:		List[str]	  =      in_proj_bias[hidden_size : hidden_size * 2]
  __A	:		Optional[Any]	  =      in_proj_weight[-hidden_size :, :]
  __A	:		Union[str, Any]	  =      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)
  __A	:		int	  =      state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
  __A	:		Optional[Any]	  =      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
  __A	:		Any	  =      in_proj_weight[: hidden_size, :]
  __A	:		Union[str, Any]	  =      in_proj_bias[:config.hidden_size]
  __A	:		Optional[int]	  =      in_proj_weight[hidden_size : hidden_size * 2, :]
  __A	:		Optional[Any]	  =      in_proj_bias[hidden_size : hidden_size * 2]
  __A	:		str	  =      in_proj_weight[-hidden_size :, :]
  __A	:		Any	  =      in_proj_bias[-hidden_size :]
 # fmt: on


def       _lowercase							(					):
 __A	:		Optional[Any]	  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A	:		Dict	  =      Image.open(requests.get(UpperCamelCase__,					stream=UpperCamelCase__      ).raw      )
 return im


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       str,					UpperCamelCase__							:       str,					UpperCamelCase__							:       bool = False      ):
 __A	:		List[Any]	  =      get_maskformer_config(UpperCamelCase__      )

 # load original state_dict
 with open(UpperCamelCase__,					'rb'      ) as f:
  __A	:		Any	  =      pickle.load(UpperCamelCase__      )
 __A	:		Tuple	  =      data['model']

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

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

 # update to torch tensors
 for key, value in state_dict.items():
  __A	:		Dict	  =      torch.from_numpy(UpperCamelCase__      )

 # load 🤗 model
 __A	:		str	  =      MaskFormerForInstanceSegmentation(UpperCamelCase__      )
 model.eval()

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

 __A  ,__A	:		List[str]	  =      model.load_state_dict(UpperCamelCase__,					strict=UpperCamelCase__      )
 assert missing_keys == [
     "model.pixel_level_module.encoder.model.layernorm.weight",
     "model.pixel_level_module.encoder.model.layernorm.bias",
 ]
 assert len(UpperCamelCase__      ) == 0, f"""Unexpected keys: {unexpected_keys}"""

 # verify results
 __A	:		List[Any]	  =      prepare_img()
 if "vistas" in model_name:
  __A	:		Dict	  =      65
 elif "cityscapes" in model_name:
  __A	:		Optional[int]	  =      65535
 else:
  __A	:		List[Any]	  =      255
 __A	:		List[Any]	  =      True if 'ade' in model_name else False
 __A	:		Dict	  =      MaskFormerImageProcessor(ignore_index=UpperCamelCase__,					reduce_labels=UpperCamelCase__      )

 __A	:		Optional[Any]	  =      image_processor(UpperCamelCase__,					return_tensors='pt'      )

 __A	:		Dict	  =      model(**UpperCamelCase__      )

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

 if model_name == "maskformer-swin-tiny-ade":
  __A	:		Optional[Any]	  =      torch.tensor(
      [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]      )
 assert torch.allclose(outputs.class_queries_logits[0, :3, :3],					UpperCamelCase__,					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(UpperCamelCase__      ).mkdir(exist_ok=UpperCamelCase__      )
  model.save_pretrained(UpperCamelCase__      )
  image_processor.save_pretrained(UpperCamelCase__      )

 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__":
 UpperCAmelCase_			:  List[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.'
 )

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










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






