



import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Union[str, Any] ,       UpperCamelCase_				:       str ,       UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       int	)->  Dict:
    # Load configuration defined in the metadata file
    with open(UpperCamelCase_	) as metadata_file:
        A__		 =					json.load(UpperCamelCase_	)
    A__		 =					LukeConfig(use_entity_aware_attention=UpperCamelCase_ ,       **metadata['''model_config''']	)

    # Load in the weights from the checkpoint_path
    A__		 =					torch.load(UpperCamelCase_ ,       map_location='''cpu'''	)

    # Load the entity vocab file
    A__		 =					load_entity_vocab(UpperCamelCase_	)

    A__		 =					RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

    # Add special tokens to the token vocabulary for downstream tasks
    A__		 =					AddedToken('''<ent>''' ,       lstrip=UpperCamelCase_ ,       rstrip=UpperCamelCase_	)
    A__		 =					AddedToken('''<ent2>''' ,       lstrip=UpperCamelCase_ ,       rstrip=UpperCamelCase_	)
    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(UpperCamelCase_	)
    with open(os.path.join(UpperCamelCase_ ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']	) ,       '''w'''	) as f:
        json.dump(UpperCamelCase_ ,       UpperCamelCase_	)

    A__		 =					LukeTokenizer.from_pretrained(UpperCamelCase_	)

    # Initialize the embeddings of the special tokens
    A__		 =					state_dict['''embeddings.word_embeddings.weight''']
    A__		 =					word_emb[tokenizer.convert_tokens_to_ids(['''@''']	)[0]].unsqueeze(0	)
    A__		 =					word_emb[tokenizer.convert_tokens_to_ids(['''#''']	)[0]].unsqueeze(0	)
    A__		 =					torch.cat([word_emb, ent_emb, enta_emb]	)

    # 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"]:
            A__		 =					f"encoder.layer.{layer_index}.attention.self."
            A__		 =					state_dict[prefix + matrix_name]
            A__		 =					state_dict[prefix + matrix_name]
            A__		 =					state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    A__		 =					state_dict['''entity_embeddings.entity_embeddings.weight''']
    A__		 =					entity_emb[entity_vocab['''[MASK]''']]

    A__		 =					LukeModel(config=UpperCamelCase_	).eval()

    A__ ,	A__		 =					model.load_state_dict(UpperCamelCase_ ,       strict=UpperCamelCase_	)
    if not (len(UpperCamelCase_	) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(f"Missing keys {', '.join(UpperCamelCase_	)}. Expected only missing embeddings.position_ids"	)
    if not (all(key.startswith('''entity_predictions'''	) or key.startswith('''lm_head'''	) for key in unexpected_keys	)):
        raise ValueError(
            '''Unexpected keys'''
            f" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'	) or key.startswith('lm_head'	))]	)}"	)

    # Check outputs
    A__		 =					LukeTokenizer.from_pretrained(UpperCamelCase_ ,       task='''entity_classification'''	)

    A__		 =					(
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    A__		 =					(3_9, 4_2)
    A__		 =					tokenizer(UpperCamelCase_ ,       entity_spans=[span] ,       add_prefix_space=UpperCamelCase_ ,       return_tensors='''pt'''	)

    A__		 =					model(**UpperCamelCase_	)

    # Verify word hidden states
    if model_size == "large":
        A__		 =					torch.Size((1, 4_2, 1_0_2_4)	)
        A__		 =					torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]	)
    else:  # base
        A__		 =					torch.Size((1, 4_2, 7_6_8)	)
        A__		 =					torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]	)

    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] ,       UpperCamelCase_ ,       atol=1E-4	):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        A__		 =					torch.Size((1, 1, 1_0_2_4)	)
        A__		 =					torch.tensor([[0.0466, -0.0106, -0.0179]]	)
    else:  # base
        A__		 =					torch.Size((1, 1, 7_6_8)	)
        A__		 =					torch.tensor([[0.1457, 0.1044, 0.0174]]	)

    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] ,       UpperCamelCase_ ,       atol=1E-4	):
        raise ValueError

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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Any:
    A__		 =					{}
    with open(UpperCamelCase_ ,       '''r''' ,       encoding='''utf-8'''	) as f:
        for index, line in enumerate(UpperCamelCase_	):
            A__ ,	A__		 =					line.rstrip().split('''\t'''	)
            A__		 =					index

    return entity_vocab


if __name__ == "__main__":
   _lowercase						  =		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						  =		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 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       list[str] | None = None	)->  list[list[str]]:
    A__		 =					word_bank or []
    # create a table
    A__		 =					len(UpperCamelCase_	) + 1

    A__		 =					[]
    for _ in range(UpperCamelCase_	):
        table.append([]	)
    # seed value
    A__		 =					[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase_	):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase_	)] == word:
                    A__		 =					[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase_	)] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase_	)]:
        combination.reverse()

    return table[len(UpperCamelCase_	)]


if __name__ == "__main__":
   print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
   print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
   print(
       all_construct(
           "hexagonosaurus",
           ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
       )
   )
