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







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

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

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

       return config







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					Tuple	     =				[]
       # 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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					List[Any]	     =				dct.pop(SCREAMING_SNAKE_CASE__    )
       lowercase							:					str	     =				val







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







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







def 						_snake_case(  )			->   torch.Tensor:
       lowercase							:					Union[str, Any]	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:					Optional[int]	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = False    )			->   Tuple:
       lowercase							:					int	     =				get_maskformer_config(SCREAMING_SNAKE_CASE__    )

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

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

       # rename keys
       lowercase							:					Union[str, Any]	     =				create_rename_keys(SCREAMING_SNAKE_CASE__    )
       for src, dest in rename_keys:
              rename_key(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__ ,     config.backbone_config    )
       read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       # update to torch tensors
       for key, value in state_dict.items():
              lowercase							:					str	     =				torch.from_numpy(SCREAMING_SNAKE_CASE__    )

       # load 🤗 model
       lowercase							:					Union[str, Any]	     =				MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__    )
       model.eval()

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

       lowercase							:					Optional[int]	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(SCREAMING_SNAKE_CASE__    ) == 0, f"Unexpected keys: {unexpected_keys}"

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

       lowercase							:					Union[str, Any]	     =				image_processor(SCREAMING_SNAKE_CASE__ ,     return_tensors="""pt"""    )

       lowercase							:					List[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )

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

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

       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__":
  lowercase  : 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."""
  )

  lowercase  : Dict     =							parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

       # Load in the weights from the checkpoint_path
       lowercase							:					Tuple	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )["""module"""]

       # Load the entity vocab file
       lowercase							:					str	     =				load_original_entity_vocab(SCREAMING_SNAKE_CASE__    )
       # add an entry for [MASK2]
       lowercase							:					Any	     =				max(entity_vocab.values()    ) + 1
       config.entity_vocab_size += 1

       lowercase							:					Dict	     =				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase							:					List[Any]	     =				AddedToken("""<ent>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				AddedToken("""<ent2>""" ,     lstrip=SCREAMING_SNAKE_CASE__ ,     rstrip=SCREAMING_SNAKE_CASE__    )
       tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}    )
       config.vocab_size += 2

       print(f"Saving tokenizer to {pytorch_dump_folder_path}"    )
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__    )
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """r"""    ) as f:
              lowercase							:					List[str]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				"""MLukeTokenizer"""
       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     """tokenizer_config.json"""    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       with open(os.path.join(SCREAMING_SNAKE_CASE__ ,     MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]    ) ,     """w"""    ) as f:
              json.dump(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

       # Initialize the embeddings of the special tokens
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""@"""]    )[0]
       lowercase							:					Dict	     =				tokenizer.convert_tokens_to_ids(["""#"""]    )[0]

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					str	     =				torch.cat([word_emb, ent_emb, enta_emb]    )
       # add special tokens for 'entity_predictions.bias'
       for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					int	     =				torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]    )

       # Initialize the query layers of the entity-aware self-attention mechanism
       for layer_index in range(config.num_hidden_layers    ):
              for matrix_name in ["query.weight", "query.bias"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       lowercase							:					Any	     =				state_dict["""entity_embeddings.entity_embeddings.weight"""]
       lowercase							:					Tuple	     =				entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					Optional[Any]	     =				torch.cat([entity_emb, entity_mask_emb]    )
       # add [MASK2] for 'entity_predictions.bias'
       lowercase							:					Optional[Any]	     =				state_dict["""entity_predictions.bias"""]
       lowercase							:					str	     =				entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0    )
       lowercase							:					List[str]	     =				torch.cat([entity_prediction_bias, entity_mask_bias]    )

       lowercase							:					List[str]	     =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__    ).eval()

       state_dict.pop("""entity_predictions.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.weight"""    )
       state_dict.pop("""lm_head.decoder.bias"""    )
       lowercase							:					List[str]	     =				OrderedDict()
       for key, value in state_dict.items():
              if not (key.startswith("""lm_head"""    ) or key.startswith("""entity_predictions"""    )):
                     lowercase							:					List[Any]	     =				state_dict[key]
              else:
                     lowercase							:					Union[str, Any]	     =				state_dict[key]

       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )

       if set(SCREAMING_SNAKE_CASE__    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"    )
       if set(SCREAMING_SNAKE_CASE__    ) != {
           "lm_head.decoder.weight",
           "lm_head.decoder.bias",
           "entity_predictions.decoder.weight",
       }:
              raise ValueError(f"Unexpected missing_keys: {missing_keys}"    )

       model.tie_weights()
       assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
       assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

       # Check outputs
       lowercase							:					str	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,     task="""entity_classification"""    )

       lowercase							:					str	     =				"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
       lowercase							:					str	     =				(0, 9)
       lowercase							:					Dict	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]    )

       if not (outputs.last_hidden_state.shape == expected_shape):
              raise ValueError(
                  f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"    )
       if not torch.allclose(outputs.last_hidden_state[0, :3, :3] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[Any]	     =				torch.tensor([[-0.1482, 0.0609, 0.0322]]    )

       if not (outputs.entity_last_hidden_state.shape == expected_shape):
              raise ValueError(
                  f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
                  f" {expected_shape}"    )
       if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify masked word/entity prediction
       lowercase							:					Any	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[Any]	     =				"""Tokyo is the capital of <mask>."""
       lowercase							:					List[Any]	     =				(24, 30)
       lowercase							:					int	     =				tokenizer(SCREAMING_SNAKE_CASE__ ,     entity_spans=[span] ,     return_tensors="""pt"""    )

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
       ]
       assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""    )][0] == "en:Japan"

       # Finally, save our PyTorch model and tokenizer
       print("""Saving PyTorch model to {}""".format(SCREAMING_SNAKE_CASE__    )    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

       lowercase							:					int	     =				{}
       for entry in data:
              lowercase							:					Optional[Any]	     =				entry["""id"""]
              for entity_name, language in entry["entities"]:
                     if entity_name in SPECIAL_TOKENS:
                            lowercase							:					Optional[Any]	     =				entity_id
                            break
                     lowercase							:					List[Any]	     =				f"{language}:{entity_name}"
                     lowercase							:					Union[str, Any]	     =				entity_id
       return new_mapping


if __name__ == "__main__":
  lowercase  : Any						   =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
  parser.add_argument(
      """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
  )
  parser.add_argument(
      """--entity_vocab_path""",
      default=None,
      type=str,
      help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
  )
  parser.add_argument(
      """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
  )
  lowercase  : str						   =							parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )
