





'''simple docstring'''


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 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : List[Any]     ,__lowerCamelCase : Tuple     ,__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Union[str, Any]			):
   # Load configuration defined in the metadata file
   with open(__lowerCamelCase			) as metadata_file:
      lowercase_      :Any         =			json.load(__lowerCamelCase			)
   lowercase_      :str         =			LukeConfig(use_entity_aware_attention=__lowerCamelCase     ,**metadata["model_config"]			)

   # Load in the weights from the checkpoint_path
   lowercase_      :int         =			torch.load(__lowerCamelCase     ,map_location="cpu"			)["module"]

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

   lowercase_      :Tuple         =			XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

   # Add special tokens to the token vocabulary for downstream tasks
   lowercase_      :Tuple         =			AddedToken("<ent>"     ,lstrip=__lowerCamelCase     ,rstrip=__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			AddedToken("<ent2>"     ,lstrip=__lowerCamelCase     ,rstrip=__lowerCamelCase			)
   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(__lowerCamelCase			)
   with open(os.path.join(__lowerCamelCase     ,"tokenizer_config.json"			)     ,"r"			) as f:
      lowercase_      :Optional[int]         =			json.load(__lowerCamelCase			)
   lowercase_      :Dict         =			"MLukeTokenizer"
   with open(os.path.join(__lowerCamelCase     ,"tokenizer_config.json"			)     ,"w"			) as f:
      json.dump(__lowerCamelCase     ,__lowerCamelCase			)

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

   lowercase_      :Any         =			MLukeTokenizer.from_pretrained(__lowerCamelCase			)

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

   lowercase_      :Dict         =			state_dict["embeddings.word_embeddings.weight"]
   lowercase_      :Any         =			word_emb[ent_init_index].unsqueeze(0			)
   lowercase_      :Union[str, Any]         =			word_emb[enta_init_index].unsqueeze(0			)
   lowercase_      :Optional[int]         =			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_      :Any         =			state_dict[bias_name]
      lowercase_      :Tuple         =			decoder_bias[ent_init_index].unsqueeze(0			)
      lowercase_      :Optional[Any]         =			decoder_bias[enta_init_index].unsqueeze(0			)
      lowercase_      :Union[str, Any]         =			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_      :int         =			state_dict[prefix + matrix_name]
         lowercase_      :int         =			state_dict[prefix + matrix_name]
         lowercase_      :List[str]         =			state_dict[prefix + matrix_name]

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

   lowercase_      :str         =			LukeForMaskedLM(config=__lowerCamelCase			).eval()

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

   lowercase_      :Optional[Any]         =			model.load_state_dict(__lowerCamelCase     ,strict=__lowerCamelCase			)

   if set(__lowerCamelCase			) != {"luke.embeddings.position_ids"}:
      raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'			)
   if set(__lowerCamelCase			) != {
       "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_      :Optional[Any]         =			MLukeTokenizer.from_pretrained(__lowerCamelCase     ,task="entity_classification"			)

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

   lowercase_      :Tuple         =			model(**__lowerCamelCase			)

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowercase_      :Optional[Any]         =			torch.Size((1, 33, 7_68)			)
      lowercase_      :Optional[Any]         =			torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]			)

   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]     ,__lowerCamelCase     ,atol=1e-4			):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowercase_      :List[Any]         =			torch.Size((1, 1, 7_68)			)
      lowercase_      :Union[str, Any]         =			torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]			)

   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]     ,__lowerCamelCase     ,atol=1e-4			):
      raise ValueError

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

   lowercase_      :str         =			model(**__lowerCamelCase			)

   lowercase_      :List[Any]         =			encoding["input_ids"][0].tolist()
   lowercase_      :Any         =			input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
   lowercase_      :int         =			outputs.logits[0][mask_position_id].argmax(dim=-1			)
   assert "Japan" == tokenizer.decode(__lowerCamelCase			)

   lowercase_      :int         =			outputs.entity_logits[0][0].argmax().item()
   lowercase_      :Tuple         =			[
       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(__lowerCamelCase			)			)
   model.save_pretrained(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : Dict			):
   lowercase_      :List[str]         =			["[MASK]", "[PAD]", "[UNK]"]

   lowercase_      :Optional[int]         =			[json.loads(__lowerCamelCase			) for line in open(__lowerCamelCase			)]

   lowercase_      :List[str]         =			{}
   for entry in data:
      lowercase_      :str         =			entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            lowercase_      :Tuple         =			entity_id
            break
         lowercase_      :Any         =			F'{language}:{entity_name}'
         lowercase_      :Tuple         =			entity_id
   return new_mapping


if __name__ == "__main__":
 lowerCAmelCase :					Union[str, 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.'''
 )
 lowerCAmelCase :					Dict 							=parser.parse_args()
 convert_luke_checkpoint(
     args.checkpoint_path,
     args.metadata_path,
     args.entity_vocab_path,
     args.pytorch_dump_folder_path,
     args.model_size,
 )









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


