
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 						__magic_name__			(   __a      :	int	,		__a      :	str	,		__a      :	Dict	,		__a      :	str	,		__a      :	int						):



     '''simple docstring'''




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

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

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

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

     # Add special tokens to the token vocabulary for downstream tasks
     UpperCamelCase__				=    AddedToken("""<ent>"""	,		lstrip=__a	,		rstrip=__a						)
     UpperCamelCase__				=    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:
          UpperCamelCase__				=    json.load(__a						)
     UpperCamelCase__				=    """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						)

     UpperCamelCase__				=    MLukeTokenizer.from_pretrained(__a						)

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

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

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

     UpperCamelCase__				=    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"""						)
     UpperCamelCase__				=    OrderedDict()
     for key, value in state_dict.items():
          if not (key.startswith("""lm_head"""						) or key.startswith("""entity_predictions"""						)):
               UpperCamelCase__				=    state_dict[key]
          else:
               UpperCamelCase__				=    state_dict[key]

     UpperCamelCase__		,	UpperCamelCase__				=    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
     UpperCamelCase__				=    MLukeTokenizer.from_pretrained(__a	,		task="""entity_classification"""						)

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

     UpperCamelCase__				=    model(**__a						)

     # Verify word hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          UpperCamelCase__				=    torch.Size((1, 33, 768)						)
          UpperCamelCase__				=    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]	,		__a	,		atol=1E-4						):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          raise NotImplementedError
     else:  # base
          UpperCamelCase__				=    torch.Size((1, 1, 768)						)
          UpperCamelCase__				=    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]	,		__a	,		atol=1E-4						):
          raise ValueError

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

     UpperCamelCase__				=    model(**__a						)

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

     UpperCamelCase__				=    outputs.entity_logits[0][0].argmax().item()
     UpperCamelCase__				=    [
         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 						__magic_name__			(   __a      :	Dict						):



     '''simple docstring'''




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

     UpperCamelCase__				=    [json.loads(__a						) for line in open(__a						)]

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


if __name__ == "__main__":
    lowerCamelCase_						     =					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_						     =					parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )







from __future__ import annotations






def 						__magic_name__			(   __a      :	list[list[int]]						):



     '''simple docstring'''




     for i in range(1	,		len(matrix[0]						)						):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1	,		len(__a						)						):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1	,		len(__a						)						):
          for j in range(1	,		len(matrix[0]						)						):
               matrix[i][j] += min(matrix[i - 1][j]	,		matrix[i][j - 1]						)

     return matrix[-1][-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()





