"""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 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     with open(A      ) as metadata_file:
          lowercase__				 =							json.load(A      )
     lowercase__				 =							LukeConfig(use_entity_aware_attention=A    ,				**metadata["model_config"]      )

     # Load in the weights from the checkpoint_path
     lowercase__				 =							torch.load(A    ,				map_location="cpu"      )["module"]

     # Load the entity vocab file
     lowercase__				 =							load_original_entity_vocab(A      )
     # add an entry for [MASK2]
     lowercase__				 =							max(entity_vocab.values()      ) + 1
     config.entity_vocab_size += 1

     lowercase__				 =							XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

     # Add special tokens to the token vocabulary for downstream tasks
     lowercase__				 =							AddedToken("<ent>"    ,				lstrip=A    ,				rstrip=A      )
     lowercase__				 =							AddedToken("<ent2>"    ,				lstrip=A    ,				rstrip=A      )
     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(A      )
     with open(os.path.join(A    ,				"tokenizer_config.json"      )    ,				"r"      ) as f:
          lowercase__				 =							json.load(A      )
     lowercase__				 =							"MLukeTokenizer"
     with open(os.path.join(A    ,				"tokenizer_config.json"      )    ,				"w"      ) as f:
          json.dump(A    ,				A      )

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

     lowercase__				 =							MLukeTokenizer.from_pretrained(A      )

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

     lowercase__				 =							state_dict["embeddings.word_embeddings.weight"]
     lowercase__				 =							word_emb[ent_init_index].unsqueeze(0      )
     lowercase__				 =							word_emb[enta_init_index].unsqueeze(0      )
     lowercase__				 =							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__				 =							state_dict[bias_name]
          lowercase__				 =							decoder_bias[ent_init_index].unsqueeze(0      )
          lowercase__				 =							decoder_bias[enta_init_index].unsqueeze(0      )
          lowercase__				 =							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__				 =							f'''encoder.layer.{layer_index}.attention.self.'''
               lowercase__				 =							state_dict[prefix + matrix_name]
               lowercase__				 =							state_dict[prefix + matrix_name]
               lowercase__				 =							state_dict[prefix + matrix_name]

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

     lowercase__				 =							LukeForMaskedLM(config=A      ).eval()

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

     lowercase__ ,						lowercase__				 =							model.load_state_dict(A    ,				strict=A      )

     if set(A      ) != {"luke.embeddings.position_ids"}:
          raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''      )
     if set(A      ) != {
         "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__				 =							MLukeTokenizer.from_pretrained(A    ,				task="entity_classification"      )

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

     lowercase__				 =							model(**A      )

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          lowercase__				 =							torch.Size((1, 33, 7_68)      )
          lowercase__				 =							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]    ,				A    ,				atol=1e-4      ):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          lowercase__				 =							torch.Size((1, 1, 7_68)      )
          lowercase__				 =							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]    ,				A    ,				atol=1e-4      ):
          raise ValueError

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

     lowercase__				 =							model(**A      )

     lowercase__				 =							encoding["input_ids"][0].tolist()
     lowercase__				 =							input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
     lowercase__				 =							outputs.logits[0][mask_position_id].argmax(dim=-1      )
     assert "Japan" == tokenizer.decode(A      )

     lowercase__				 =							outputs.entity_logits[0][0].argmax().item()
     lowercase__				 =							[
         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(A      )      )
     model.save_pretrained(A      )







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							["[MASK]", "[PAD]", "[UNK]"]

     lowercase__				 =							[json.loads(A      ) for line in open(A      )]

     lowercase__				 =							{}
     for entry in data:
          lowercase__				 =							entry["id"]
          for entity_name, language in entry["entities"]:
               if entity_name in SPECIAL_TOKENS:
                    lowercase__				 =							entity_id
                    break
               lowercase__				 =							f'''{language}:{entity_name}'''
               lowercase__				 =							entity_id
     return new_mapping


if __name__ == "__main__":
 lowerCAmelCase_:					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_:					List[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,
 )





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size




