"""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					: int        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			SwinConfig.from_pretrained(
          """microsoft/swin-tiny-patch4-window7-224"""      ,	out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	)
      lowercase														=			MaskFormerConfig(backbone_config=_A	)

      lowercase														=			"""huggingface/label-files"""
      if "ade20k-full" in model_name:
            # this should be ok
            lowercase														=			8_4_7
            lowercase														=			"""maskformer-ade20k-full-id2label.json"""
      elif "ade" in model_name:
            # this should be ok
            lowercase														=			1_5_0
            lowercase														=			"""ade20k-id2label.json"""
      elif "coco-stuff" in model_name:
            # this should be ok
            lowercase														=			1_7_1
            lowercase														=			"""maskformer-coco-stuff-id2label.json"""
      elif "coco" in model_name:
            # TODO
            lowercase														=			1_3_3
            lowercase														=			"""coco-panoptic-id2label.json"""
      elif "cityscapes" in model_name:
            # this should be ok
            lowercase														=			1_9
            lowercase														=			"""cityscapes-id2label.json"""
      elif "vistas" in model_name:
            # this should be ok
            lowercase														=			6_5
            lowercase														=			"""mapillary-vistas-id2label.json"""

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

      return config


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					str:
      '''simple docstring'''


      lowercase														=			[]
      # 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__  :List[Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			dct.pop(_A	)
      lowercase														=			val


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Any	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
      for i in range(len(backbone_config.depths	)	):
            lowercase														=			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)
                  lowercase														=			state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'	)
                  lowercase														=			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
                  lowercase														=			in_proj_weight[:dim, :]
                  lowercase														=			in_proj_bias[: dim]
                  lowercase														=			in_proj_weight[
                      dim : dim * 2, :
                  ]
                  lowercase														=			in_proj_bias[
                      dim : dim * 2
                  ]
                  lowercase														=			in_proj_weight[
                      -dim :, :
                  ]
                  lowercase														=			in_proj_bias[-dim :]
                  # fmt: on


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Any	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			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)
            lowercase														=			state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'	)
            lowercase														=			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
            lowercase														=			in_proj_weight[: hidden_size, :]
            lowercase														=			in_proj_bias[:config.hidden_size]
            lowercase														=			in_proj_weight[hidden_size : hidden_size * 2, :]
            lowercase														=			in_proj_bias[hidden_size : hidden_size * 2]
            lowercase														=			in_proj_weight[-hidden_size :, :]
            lowercase														=			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)
            lowercase														=			state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'	)
            lowercase														=			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
            lowercase														=			in_proj_weight[: hidden_size, :]
            lowercase														=			in_proj_bias[:config.hidden_size]
            lowercase														=			in_proj_weight[hidden_size : hidden_size * 2, :]
            lowercase														=			in_proj_bias[hidden_size : hidden_size * 2]
            lowercase														=			in_proj_weight[-hidden_size :, :]
            lowercase														=			in_proj_bias[-hidden_size :]
      # fmt: on


def     UpperCAmelCase__ (       )				->					torch.Tensor:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(_A      ,	stream=_A	).raw	)
      return im


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :bool = False	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			get_maskformer_config(_A	)

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

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

      # rename keys
      lowercase														=			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():
            lowercase														=			torch.from_numpy(_A	)

      # load 🤗 model
      lowercase														=			MaskFormerForInstanceSegmentation(_A	)
      model.eval()

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

      lowercase							,							lowercase														=			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
      lowercase														=			prepare_img()
      if "vistas" in model_name:
            lowercase														=			6_5
      elif "cityscapes" in model_name:
            lowercase														=			6_5_5_3_5
      else:
            lowercase														=			2_5_5
      lowercase														=			True if """ade""" in model_name else False
      lowercase														=			MaskFormerImageProcessor(ignore_index=_A      ,	reduce_labels=_A	)

      lowercase														=			image_processor(_A      ,	return_tensors="""pt"""	)

      lowercase														=			model(**_A	)

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

      if model_name == "maskformer-swin-tiny-ade":
            lowercase														=			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]      ,	_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__":
       __lowerCAmelCase					: str        =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[str]        =parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )


"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            warnings.warn(
                """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use YolosImageProcessor instead."""	, __lowerCAmelCase	, )
            super().__init__(*__lowerCAmelCase	, **__lowerCAmelCase		)



