


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_(				_A   :List[Any]				,     _A   :List[str]				,     _A   :Any				,     _A   :List[Any]				,     _A   :int  )->		List[str]:
			# Load configuration defined in the metadata file
			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, 7_68)  )
						UpperCamelCase__					=       torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]  )

			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, 7_68)  )
						UpperCamelCase__					=       torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]  )

			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        UpperCamelCase_(				_A   :List[Any]  )->		Union[str, Any]:
			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__":
						__UpperCamelCase    		=       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.'
						)
						__UpperCamelCase    		=       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 collections import deque
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		List[Any]:
			UpperCamelCase__					=       len(_A  )
			UpperCamelCase__					=       deque()
			UpperCamelCase__					=       [False for _ in range(_A  )]
			UpperCamelCase__					=       [-1 for _ in range(_A  )]
			UpperCamelCase__					=       index_of[:]

			def strong_connect(_A   :Optional[int]				,     _A   :Optional[int]				,     _A   :List[Any]  ):
						UpperCamelCase__					=       index  # the number when this node is seen
						UpperCamelCase__					=       index  # lowest rank node reachable from here
						index += 1
						stack.append(_A  )
						UpperCamelCase__					=       True

						for w in g[v]:
									if index_of[w] == -1:
												UpperCamelCase__					=       strong_connect(_A				,     _A				,     _A  )
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									UpperCamelCase__					=       []
									UpperCamelCase__					=       stack.pop()
									UpperCamelCase__					=       False
									component.append(_A  )
									while w != v:
												UpperCamelCase__					=       stack.pop()
												UpperCamelCase__					=       False
												component.append(_A  )
									components.append(_A  )
						return index

			UpperCamelCase__					=       []
			for v in range(_A  ):
						if index_of[v] == -1:
									strong_connect(_A				,     0				,     _A  )

			return components
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Any  )->		Union[str, Any]:
			UpperCamelCase__					=       [[] for _ in range(_A  )]
			for u, v in edges:
						g[u].append(_A  )
			return g


if __name__ == "__main__":
						# Test
						__UpperCamelCase    		=       7
						__UpperCamelCase    		=       [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__UpperCamelCase    		=       [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__UpperCamelCase    		=       [(u, v) for u, v in zip(source, target)]
						__UpperCamelCase    		=       create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


