




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 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

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

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

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

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

	snake_case_		    =     MLukeTokenizer.from_pretrained(_A							)

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

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

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

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

	snake_case_    ,      snake_case_		    =     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
	snake_case_		    =     MLukeTokenizer.from_pretrained(_A     ,	task="entity_classification"							)

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

	snake_case_		    =     model(**_A							)

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

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

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

	snake_case_		    =     model(**_A							)

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

	snake_case_		    =     outputs.entity_logits[0][0].argmax().item()
	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(_A							)							)
	model.save_pretrained(_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	snake_case_		    =     [json.loads(_A							) for line in open(_A							)]

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


if __name__ == "__main__":
		lowercase__				:     List[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__				:     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 numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							snake_case_		    =     numpy.random.rand(
							    self.input_array.shape[1]       ,	4						)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							snake_case_		    =     numpy.random.rand(
							    4       ,	3						)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							snake_case_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							snake_case_		    =     numpy.zeros(output_array.shape						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     sigmoid(
							    numpy.dot(self.input_array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return self.layer_between_second_hidden_layer_and_output






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,	2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T       ,	numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	)
							snake_case_		    =     numpy.dot(
							    self.input_array.T       ,	numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output						)       ,	self.second_hidden_layer_and_output_layer_weights.T       ,	)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer						)       ,	self.first_hidden_layer_and_second_hidden_layer_weights.T       ,	)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer						)       ,	)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






							for iteration in range(1       ,	iterations + 1						):
								snake_case_		    =     self.feedforward()
								self.back_propagation()
								if give_loss:
									snake_case_		    =     numpy.mean(numpy.square(output - self.feedforward()						)						)
									print(f"Iteration {iteration} Loss: {loss}"						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

							snake_case_		    =     sigmoid(
							    numpy.dot(self.array       ,	self.input_layer_and_first_hidden_layer_weights						)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer       ,	self.first_hidden_layer_and_second_hidden_layer_weights       ,	)						)

							snake_case_		    =     sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer       ,	self.second_hidden_layer_and_output_layer_weights       ,	)						)

							return int(self.layer_between_second_hidden_layer_and_output > 0.6						)







def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return 1 / (1 + numpy.exp(-value							))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return (value) * (1 - (value))


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )     ,	dtype=numpy.floataa     ,	)

	# True output values for the given input values.
	snake_case_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     TwoHiddenLayerNeuralNetwork(
	    input_array=_A     ,	output_array=_A							)

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=_A     ,	iterations=10     ,	give_loss=_A							)

	return neural_network.predict(numpy.array(([1, 1, 1])     ,	dtype=numpy.floataa							)							)


if __name__ == "__main__":
		example()
