






'''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__          =						logging.get_logger(__name__)







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Optional[int]:
  UpperCamelCase__				:					Union[str, Any]					=					SwinConfig.from_pretrained(
      'microsoft/swin-tiny-patch4-window7-224'     ,      out_features=['stage1', 'stage2', 'stage3', 'stage4'])
  UpperCamelCase__				:					str					=					MaskFormerConfig(backbone_config=lowerCamelCase_)

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

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

  return config







def       __UpperCAmelCase						(  lowerCamelCase_)							->     List[str]:
  UpperCamelCase__				:					str					=					[]
  # 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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Optional[Any]:
  UpperCamelCase__				:					str					=					dct.pop(lowerCamelCase_)
  UpperCamelCase__				:					Tuple					=					val







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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     Any:
  UpperCamelCase__				:					Union[str, Any]					=					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__				:					Tuple					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight')
    UpperCamelCase__				:					Optional[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
    UpperCamelCase__				:					int					=					in_proj_weight[: hidden_size, :]
    UpperCamelCase__				:					Any					=					in_proj_bias[:config.hidden_size]
    UpperCamelCase__				:					Any					=					in_proj_weight[hidden_size : hidden_size * 2, :]
    UpperCamelCase__				:					Any					=					in_proj_bias[hidden_size : hidden_size * 2]
    UpperCamelCase__				:					int					=					in_proj_weight[-hidden_size :, :]
    UpperCamelCase__				:					str					=					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__				:					Dict					=					state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight')
    UpperCamelCase__				:					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
    UpperCamelCase__				:					Dict					=					in_proj_weight[: hidden_size, :]
    UpperCamelCase__				:					Tuple					=					in_proj_bias[:config.hidden_size]
    UpperCamelCase__				:					Optional[Any]					=					in_proj_weight[hidden_size : hidden_size * 2, :]
    UpperCamelCase__				:					Tuple					=					in_proj_bias[hidden_size : hidden_size * 2]
    UpperCamelCase__				:					Union[str, Any]					=					in_proj_weight[-hidden_size :, :]
    UpperCamelCase__				:					int					=					in_proj_bias[-hidden_size :]
  # fmt: on







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







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = False)							->     int:
  UpperCamelCase__				:					Union[str, Any]					=					get_maskformer_config(lowerCamelCase_)

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

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

  # rename keys
  UpperCamelCase__				:					Optional[int]					=					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__				:					int					=					torch.from_numpy(lowerCamelCase_)

  # load 🤗 model
  UpperCamelCase__				:					Dict					=					MaskFormerForInstanceSegmentation(lowerCamelCase_)
  model.eval()

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

  UpperCamelCase__				:					Any					=					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__				:					List[Any]					=					prepare_img()
  if "vistas" in model_name:
    UpperCamelCase__				:					Optional[Any]					=					65
  elif "cityscapes" in model_name:
    UpperCamelCase__				:					int					=					65_535
  else:
    UpperCamelCase__				:					str					=					255
  UpperCamelCase__				:					Optional[Any]					=					True if '''ade''' in model_name else False
  UpperCamelCase__				:					Tuple					=					MaskFormerImageProcessor(ignore_index=lowerCamelCase_     ,      reduce_labels=lowerCamelCase_)

  UpperCamelCase__				:					Tuple					=					image_processor(lowerCamelCase_     ,      return_tensors='pt')

  UpperCamelCase__				:					Any					=					model(**lowerCamelCase_)

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

  if model_name == "maskformer-swin-tiny-ade":
    UpperCamelCase__				:					Dict					=					torch.tensor(
        [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]])
  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__          =						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__          =						parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )












'''simple docstring'''




import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor







@require_torch
@require_vision
class       __lowercase					(unittest.TestCase    ):

	@slow
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			UpperCamelCase__				:					List[str]					=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			model.to(UpperCAmelCase_)

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)
					UpperCamelCase__				:					Tuple					=					outputs.logits

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					torch.tensor(
			    [-0.41_58, -0.40_92, -0.43_47]  ,					device=UpperCAmelCase_  ,					dtype=torch.float  ,					)
			self.assertTrue(torch.allclose(logits[0, :3]  ,					UpperCAmelCase_  ,					atol=1e-4))




