
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__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# Load configuration defined in the metadata file
					with open(lowerCAmelCase__		) as metadata_file:
										lowercase							=						json.load(lowerCAmelCase__		)
					lowercase							=						LukeConfig(use_entity_aware_attention=lowerCAmelCase__ ,			**metadata['''model_config''']		)

					# Load in the weights from the checkpoint_path
					lowercase							=						torch.load(lowerCAmelCase__ ,			map_location='''cpu'''		)['''module''']

					# Load the entity vocab file
					lowercase							=						load_original_entity_vocab(lowerCAmelCase__		)
					# 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=lowerCAmelCase__ ,			rstrip=lowerCAmelCase__		)
					lowercase							=						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							=						json.load(lowerCAmelCase__		)
					lowercase							=						'''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							=						MLukeTokenizer.from_pretrained(lowerCAmelCase__		)

					# 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=lowerCAmelCase__		).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(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							=						MLukeTokenizer.from_pretrained(lowerCAmelCase__ ,			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(lowerCAmelCase__ ,			entity_spans=[span] ,			return_tensors='''pt'''		)

					lowercase							=						model(**lowerCAmelCase__		)

					# Verify word hidden states
					if model_size == "large":
										raise NotImplementedError
					else:  # base
										lowercase							=						torch.Size((1, 33, 768)		)
										lowercase							=						torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]		)

					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							=						torch.Size((1, 1, 768)		)
										lowercase							=						torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]		)

					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							=						MLukeTokenizer.from_pretrained(lowerCAmelCase__		)
					lowercase							=						'''Tokyo is the capital of <mask>.'''
					lowercase							=						(24, 30)
					lowercase							=						tokenizer(lowerCAmelCase__ ,			entity_spans=[span] ,			return_tensors='''pt'''		)

					lowercase							=						model(**lowerCAmelCase__		)

					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(lowerCAmelCase__		)

					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(lowerCAmelCase__		)		)
					model.save_pretrained(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						['''[MASK]''', '''[PAD]''', '''[UNK]''']

					lowercase							=						[json.loads(lowerCAmelCase__		) for line in open(lowerCAmelCase__		)]

					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__":
							lowercase__		: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."
							)
							lowercase__		:int   =    parser.parse_args()
							convert_luke_checkpoint(
							    args.checkpoint_path,
							    args.metadata_path,
							    args.entity_vocab_path,
							    args.pytorch_dump_folder_path,
							    args.model_size,
							)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


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

lowercase__		:Optional[Any]   =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase__		:Union[str, Any]   =    {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__		:str   =    {
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

lowercase__		:Dict   =    {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       str       					=VOCAB_FILES_NAMES
							lowercase_       :       Dict       					=PRETRAINED_VOCAB_FILES_MAP
							lowercase_       :       Optional[int]       					=PRETRAINED_INIT_CONFIGURATION
							lowercase_       :       Tuple       					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_       :       Any       					=BertTokenizer




							def __init__(				self   ,A__=None   ,A__=None   ,A__=True   ,A__="[UNK]"   ,A__="[SEP]"   ,A__="[PAD]"   ,A__="[CLS]"   ,A__="[MASK]"   ,A__=True   ,A__=None   ,**A__   ,):
												super().__init__(
												    A__   ,tokenizer_file=A__   ,do_lower_case=A__   ,unk_token=A__   ,sep_token=A__   ,pad_token=A__   ,cls_token=A__   ,mask_token=A__   ,tokenize_chinese_chars=A__   ,strip_accents=A__   ,**A__   ,)

												lowercase							=						json.loads(self.backend_tokenizer.normalizer.__getstate__())
												if (
												    normalizer_state.get('''lowercase'''   ,A__) != do_lower_case
												    or normalizer_state.get('''strip_accents'''   ,A__) != strip_accents
												    or normalizer_state.get('''handle_chinese_chars'''   ,A__) != tokenize_chinese_chars
												):
																	lowercase							=						getattr(A__   ,normalizer_state.pop('''type'''))
																	lowercase							=						do_lower_case
																	lowercase							=						strip_accents
																	lowercase							=						tokenize_chinese_chars
																	lowercase							=						normalizer_class(**A__)

												lowercase							=						do_lower_case




							def  A__      (				self   ,A__   ,A__=None):
												lowercase							=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						[self.sep_token_id]
												lowercase							=						[self.cls_token_id]
												if token_ids_a is None:
																	return len(cls + token_ids_a + sep) * [0]
												return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]





							def  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						self._tokenizer.model.save(A__   ,name=A__)
												return tuple(A__)




