





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	:					Tuple			  =					logging.get_logger(__name__)



def 		A_    (					_lowerCAmelCase					)						->			List[str]:
 UpperCamelCase     : str	     =       SwinConfig.from_pretrained(
     "microsoft/swin-tiny-patch4-window7-224"							,       out_features=["stage1", "stage2", "stage3", "stage4"]					)
 UpperCamelCase     : int	     =       MaskFormerConfig(backbone_config=__lowerCAmelCase					)

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

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

 return config



def 		A_    (					_lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Optional[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_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Any	     =       dct.pop(__lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       val



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 UpperCamelCase     : List[Any]	     =       [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[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)
   UpperCamelCase     : List[str]	     =       state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
   UpperCamelCase     : 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
   UpperCamelCase     : Optional[Any]	     =       in_proj_weight[:dim, :]
   UpperCamelCase     : Dict	     =       in_proj_bias[: dim]
   UpperCamelCase     : Optional[int]	     =       in_proj_weight[
       dim : dim * 2, :
   ]
   UpperCamelCase     : str	     =       in_proj_bias[
       dim : dim * 2
   ]
   UpperCamelCase     : Union[str, Any]	     =       in_proj_weight[
       -dim :, :
   ]
   UpperCamelCase     : Dict	     =       in_proj_bias[-dim :]
   # fmt: on



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



def 		A_    (					)						->			torch.Tensor:
 UpperCamelCase     : Dict	     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCamelCase     : List[str]	     =       Image.open(requests.get(__lowerCAmelCase							,       stream=__lowerCAmelCase					).raw					)
 return im



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = False					)						->			str:
 UpperCamelCase     : Tuple	     =       get_maskformer_config(__lowerCAmelCase					)

 # load original state_dict
 with open(__lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : Any	     =       pickle.load(__lowerCAmelCase					)
 UpperCamelCase     : Tuple	     =       data["""model"""]

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

 # rename keys
 UpperCamelCase     : Dict	     =       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     : Dict	     =       torch.from_numpy(__lowerCAmelCase					)

 # load 🤗 model
 UpperCamelCase     : Optional[int]	     =       MaskFormerForInstanceSegmentation(__lowerCAmelCase					)
 model.eval()

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

 UpperCamelCase     : int	     =       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     : Any	     =       prepare_img()
 if "vistas" in model_name:
  UpperCamelCase     : Tuple	     =       65
 elif "cityscapes" in model_name:
  UpperCamelCase     : List[Any]	     =       6_5535
 else:
  UpperCamelCase     : Optional[Any]	     =       255
 UpperCamelCase     : str	     =       True if """ade""" in model_name else False
 UpperCamelCase     : List[str]	     =       MaskFormerImageProcessor(ignore_index=__lowerCAmelCase							,       reduce_labels=__lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       image_processor(__lowerCAmelCase							,       return_tensors="pt"					)

 UpperCamelCase     : int	     =       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	:					Optional[int]			  =					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	:					Dict			  =					parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )







def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
