


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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase	) as metadata_file:
						__lowercase			       = json.load(_UpperCamelCase	)
			__lowercase			       = LukeConfig(use_entity_aware_attention=_UpperCamelCase							,      **metadata['''model_config''']	)

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

			# Load the entity vocab file
			__lowercase			       = load_original_entity_vocab(_UpperCamelCase	)
			# 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=_UpperCamelCase							,      rstrip=_UpperCamelCase	)
			__lowercase			       = 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							,      '''tokenizer_config.json'''	)							,      '''r'''	) as f:
						__lowercase			       = json.load(_UpperCamelCase	)
			__lowercase			       = '''MLukeTokenizer'''
			with open(os.path.join(_UpperCamelCase							,      '''tokenizer_config.json'''	)							,      '''w'''	) as f:
						json.dump(_UpperCamelCase							,      _UpperCamelCase	)

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

			__lowercase			       = MLukeTokenizer.from_pretrained(_UpperCamelCase	)

			# 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=_UpperCamelCase	).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(_UpperCamelCase							,      strict=_UpperCamelCase	)

			if set(_UpperCamelCase	) != {"luke.embeddings.position_ids"}:
						raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'	)
			if set(_UpperCamelCase	) != {
			    "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(_UpperCamelCase							,      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(_UpperCamelCase							,      entity_spans=[span]							,      return_tensors='''pt'''	)

			__lowercase			       = model(**_UpperCamelCase	)

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

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

			__lowercase			       = model(**_UpperCamelCase	)

			__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(_UpperCamelCase	)

			__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(_UpperCamelCase	)	)
			model.save_pretrained(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = ['''[MASK]''', '''[PAD]''', '''[UNK]''']

			__lowercase			       = [json.loads(_UpperCamelCase	) for line in open(_UpperCamelCase	)]

			__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__":
	a			:			Optional[int]												=					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.'''
	)
	a			:			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,
	)






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=True							,      _UpperCamelCase="pt"	):


			'''simple docstring'''







			__lowercase			       = {'''add_prefix_space''': True} if isinstance(_UpperCamelCase							,      _UpperCamelCase	) and not line.startswith(''' '''	) else {}
			__lowercase			       = padding_side
			return tokenizer(
			    [line]							,      max_length=_UpperCamelCase							,      padding='''max_length''' if pad_to_max_length else None							,      truncation=_UpperCamelCase							,      return_tensors=_UpperCamelCase							,      add_special_tokens=_UpperCamelCase							,      **_UpperCamelCase							,      )


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      ):


			'''simple docstring'''







			__lowercase			       = input_ids.ne(_UpperCamelCase	).any(dim=0	)
			if attention_mask is None:
						return input_ids[:, keep_column_mask]
			else:
						return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_="train"			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=""			,							) ->      Tuple:







								'''simple docstring'''

								super().__init__()
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.source'''    )
								__lowercase			       = Path(snake_case_    ).joinpath(type_path + '''.target'''    )
								__lowercase			       = self.get_char_lens(self.src_file    )
								__lowercase			       = max_source_length
								__lowercase			       = max_target_length
								assert min(self.src_lens    ) > 0, F'found empty line in {self.src_file}'
								__lowercase			       = tokenizer
								__lowercase			       = prefix
								if n_obs is not None:
											__lowercase			       = self.src_lens[:n_obs]
								__lowercase			       = src_lang
								__lowercase			       = tgt_lang






					def __len__(					self    ) ->      List[Any]:







								'''simple docstring'''

								return len(self.src_lens    )






					def __getitem__(					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = index + 1  # linecache starts at 1
								__lowercase			       = self.prefix + linecache.getline(str(self.src_file    )			,							snake_case_    ).rstrip('''\n'''    )
								__lowercase			       = linecache.getline(str(self.tgt_file    )			,							snake_case_    ).rstrip('''\n'''    )
								assert source_line, F'empty source line for index {index}'
								assert tgt_line, F'empty tgt line for index {index}'

								# Need to add eos token manually for T5
								if isinstance(self.tokenizer			,							snake_case_    ):
											source_line += self.tokenizer.eos_token
											tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								__lowercase			       = (
								    self.tokenizer.question_encoder if isinstance(self.tokenizer			,							snake_case_    ) else self.tokenizer
								)
								__lowercase			       = self.tokenizer.generator if isinstance(self.tokenizer			,							snake_case_    ) else self.tokenizer

								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_source_length			,							'''right'''    )
								__lowercase			       = encode_line(snake_case_			,							snake_case_			,							self.max_target_length			,							'''right'''    )

								__lowercase			       = source_inputs['''input_ids'''].squeeze()
								__lowercase			       = target_inputs['''input_ids'''].squeeze()
								__lowercase			       = source_inputs['''attention_mask'''].squeeze()
								return {
								    "input_ids": source_ids,
								    "attention_mask": src_mask,
								    "decoder_input_ids": target_ids,
								}






					@staticmethod
					def A   (					snake_case_    ) ->      Any:







								'''simple docstring'''

								return [len(snake_case_    ) for x in Path(snake_case_    ).open().readlines()]






					def A   (					self			,							snake_case_    ) ->      Dict[str, torch.Tensor]:







								'''simple docstring'''

								__lowercase			       = torch.stack([x['''input_ids'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''attention_mask'''] for x in batch]    )
								__lowercase			       = torch.stack([x['''decoder_input_ids'''] for x in batch]    )
								__lowercase			       = (
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = (
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer			,							snake_case_    )
								    else self.tokenizer.pad_token_id
								)
								__lowercase			       = trim_batch(snake_case_			,							snake_case_    )
								__lowercase       ,			__lowercase			       = trim_batch(snake_case_			,							snake_case_			,							attention_mask=snake_case_    )
								__lowercase			       = {
								    '''input_ids''': source_ids,
								    '''attention_mask''': source_mask,
								    '''decoder_input_ids''': y,
								}
								return batch




a			:			int												=					getLogger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return list(itertools.chain.from_iterable(_UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = get_git_info()
			save_json(_UpperCamelCase							,      os.path.join(_UpperCamelCase							,      '''git_log.json'''	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=4							,      **_UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''w'''	) as f:
						json.dump(_UpperCamelCase							,      _UpperCamelCase							,      indent=_UpperCamelCase							,      **_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase	) as f:
						return json.load(_UpperCamelCase	)


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = git.Repo(search_parent_directories=_UpperCamelCase	)
			__lowercase			       = {
			    '''repo_id''': str(_UpperCamelCase	),
			    '''repo_sha''': str(repo.head.object.hexsha	),
			    '''repo_branch''': str(repo.active_branch	),
			    '''hostname''': str(socket.gethostname()	),
			}
			return repo_infos


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return list(map(_UpperCamelCase							,      _UpperCamelCase	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''wb'''	) as f:
						return pickle.dump(_UpperCamelCase							,      _UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			def remove_articles(_UpperCamelCase	):
						return re.sub(r'''\b(a|an|the)\b'''							,      ''' '''							,      _UpperCamelCase	)

			def white_space_fix(_UpperCamelCase	):
						return " ".join(text.split()	)

			def remove_punc(_UpperCamelCase	):
						__lowercase			       = set(string.punctuation	)
						return "".join(ch for ch in text if ch not in exclude	)

			def lower(_UpperCamelCase	):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase	)	)	)	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = normalize_answer(_UpperCamelCase	).split()
			__lowercase			       = Counter(_UpperCamelCase	) & Counter(_UpperCamelCase	)
			__lowercase			       = sum(common.values()	)
			if num_same == 0:
						return 0
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = 1.0 * num_same / len(_UpperCamelCase	)
			__lowercase			       = (2 * precision * recall) / (precision + recall)
			return fa


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			return normalize_answer(_UpperCamelCase	) == normalize_answer(_UpperCamelCase	)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			assert len(_UpperCamelCase	) == len(_UpperCamelCase	)
			__lowercase			       = 0
			for hypo, pred in zip(_UpperCamelCase							,      _UpperCamelCase	):
						em += exact_match_score(_UpperCamelCase							,      _UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
						em /= len(_UpperCamelCase	)
			return {"em": em}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			return model_prefix.startswith('''rag'''	)





def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			__lowercase			       = '''dropout_rate'''
			for p in extra_params:
						if getattr(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):
									if not hasattr(_UpperCamelCase							,      _UpperCamelCase	) and not hasattr(_UpperCamelCase							,      equivalent_param[p]	):
												logger.info('''config doesn\'t have a `{}` attribute'''.format(_UpperCamelCase	)	)
												delattr(_UpperCamelCase							,      _UpperCamelCase	)
												continue
									__lowercase			       = p if hasattr(_UpperCamelCase							,      _UpperCamelCase	) else equivalent_param[p]
									setattr(_UpperCamelCase							,      _UpperCamelCase							,      getattr(_UpperCamelCase							,      _UpperCamelCase	)	)
									delattr(_UpperCamelCase							,      _UpperCamelCase	)
			return hparams, config


