
"""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()
_a      :			Optional[int]					=							logging.get_logger(__name__)
def 				a__   (   a   :				str		):

  """simple docstring"""







  _snake_case     :						Any						=   SwinConfig.from_pretrained(
      "microsoft/swin-tiny-patch4-window7-224"       ,     out_features=["stage1", "stage2", "stage3", "stage4"]		)
  _snake_case     :						str						=   MaskFormerConfig(backbone_config=a		)

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

  _snake_case     :						Optional[Any]						=   json.load(open(hf_hub_download(a       ,     a       ,     repo_type="dataset"		)       ,     "r"		)		)
  _snake_case     :						List[str]						=   {int(a		): v for k, v in idalabel.items()}

  return config
def 				a__   (   a   :				Dict		):

  """simple docstring"""







  _snake_case     :						Any						=   []
  # 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 				a__   (   a   :				List[str]       ,     a   :				int       ,     a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						Tuple						=   dct.pop(a		)
  _snake_case     :						int						=   val
def 				a__   (   a   :				int       ,     a   :				Optional[int]		):

  """simple docstring"""







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

  """simple docstring"""







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

  """simple docstring"""







  _snake_case     :						str						=   "http://images.cocodataset.org/val2017/000000039769.jpg"
  _snake_case     :						Tuple						=   Image.open(requests.get(a       ,     stream=a		).raw		)
  return im
@torch.no_grad()
def 				a__   (   a   :				Dict       ,     a   :				Any       ,     a   :				Optional[Any]       ,     a   :				Any = False		):

  """simple docstring"""







  _snake_case     :						List[str]						=   get_maskformer_config(a		)

  # load original state_dict
  with open(a       ,     "rb"		) as f:
    _snake_case     :						Optional[int]						=   pickle.load(a		)
  _snake_case     :						List[Any]						=   data["model"]

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

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

  # update to torch tensors
  for key, value in state_dict.items():
    _snake_case     :						Optional[int]						=   torch.from_numpy(a		)

  # load 🤗 model
  _snake_case     :						Tuple						=   MaskFormerForInstanceSegmentation(a		)
  model.eval()

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

  _snake_case			,					_snake_case     :						Any						=   model.load_state_dict(a       ,     strict=a		)
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(a		) == 0, f'Unexpected keys: {unexpected_keys}'

  # verify results
  _snake_case     :						str						=   prepare_img()
  if "vistas" in model_name:
    _snake_case     :						Dict						=   65
  elif "cityscapes" in model_name:
    _snake_case     :						int						=   65_535
  else:
    _snake_case     :						List[str]						=   255
  _snake_case     :						List[str]						=   True if "ade" in model_name else False
  _snake_case     :						Tuple						=   MaskFormerImageProcessor(ignore_index=a       ,     reduce_labels=a		)

  _snake_case     :						Union[str, Any]						=   image_processor(a       ,     return_tensors="pt"		)

  _snake_case     :						List[str]						=   model(**a		)

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

  if model_name == "maskformer-swin-tiny-ade":
    _snake_case     :						str						=   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]       ,     a       ,     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(a		).mkdir(exist_ok=a		)
    model.save_pretrained(a		)
    image_processor.save_pretrained(a		)

  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__":
    _a      :			Tuple					=							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."""
    )

    _a      :			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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			str					=							{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Optional[Any]  =					"""openai-gpt"""
    __lowercase							: Dict  =					{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }
    def __init__(    self						,    snake_case_=4_04_78						,    snake_case_=5_12						,    snake_case_=7_68						,    snake_case_=12						,    snake_case_=12						,    snake_case_="gelu"						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=1E-5						,    snake_case_=0.02						,    snake_case_="cls_index"						,    snake_case_=True						,    snake_case_=None						,    snake_case_=True						,    snake_case_=0.1						,    **snake_case_						,    ):
      _snake_case     :						Tuple						=   vocab_size
      _snake_case     :						Dict						=   n_positions
      _snake_case     :						Any						=   n_embd
      _snake_case     :						Any						=   n_layer
      _snake_case     :						Optional[int]						=   n_head
      _snake_case     :						Union[str, Any]						=   afn
      _snake_case     :						Dict						=   resid_pdrop
      _snake_case     :						str						=   embd_pdrop
      _snake_case     :						Union[str, Any]						=   attn_pdrop
      _snake_case     :						str						=   layer_norm_epsilon
      _snake_case     :						Union[str, Any]						=   initializer_range
      _snake_case     :						Any						=   summary_type
      _snake_case     :						List[str]						=   summary_use_proj
      _snake_case     :						Optional[int]						=   summary_activation
      _snake_case     :						Union[str, Any]						=   summary_first_dropout
      _snake_case     :						Optional[int]						=   summary_proj_to_labels
      super().__init__(**snake_case_     )

