


"""simple docstring"""







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'''


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

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

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

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

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

					__SCREAMING_SNAKE_CASE		     =       MLukeTokenizer.from_pretrained(lowerCAmelCase_     )

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

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

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

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

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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
					__SCREAMING_SNAKE_CASE		     =       MLukeTokenizer.from_pretrained(lowerCAmelCase_						,		task="entity_classification"     )

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

					__SCREAMING_SNAKE_CASE		     =       model(**lowerCAmelCase_     )

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

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

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

					__SCREAMING_SNAKE_CASE		     =       model(**lowerCAmelCase_     )

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

					__SCREAMING_SNAKE_CASE		     =       outputs.entity_logits[0][0].argmax().item()
					__SCREAMING_SNAKE_CASE		     =       [
					    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'''


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

					__SCREAMING_SNAKE_CASE		     =       [json.loads(lowerCAmelCase_     ) for line in open(lowerCAmelCase_     )]

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


if __name__ == "__main__":
		a__       :							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.'''
		)
		a__       :							List[str]        =  parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




"""simple docstring"""







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return (data["data"], data["target"])




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       XGBClassifier()
					classifier.fit(lowerCAmelCase_						,		lowerCAmelCase_     )
					return classifier




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       load_iris()
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       data_handling(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       train_test_split(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		test_size=0.25     )

					__SCREAMING_SNAKE_CASE		     =       iris["target_names"]

					# Create an XGBoost Classifier from the training data
					__SCREAMING_SNAKE_CASE		     =       xgboost(lowerCAmelCase_						,		lowerCAmelCase_     )

					# Display the confusion matrix of the classifier with both training and test sets
					ConfusionMatrixDisplay.from_estimator(
					    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		display_labels=lowerCAmelCase_						,		cmap="Blues"						,		normalize="true"						,		)
					plt.title("Normalized Confusion Matrix - IRIS Dataset"     )
					plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()
