



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						Dict:







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

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

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

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

	# Add special tokens to the token vocabulary for downstream tasks
	lowerCAmelCase   =     AddedToken("""<ent>"""							,		lstrip=_SCREAMING_SNAKE_CASE							,		rstrip=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     AddedToken("""<ent2>"""							,		lstrip=_SCREAMING_SNAKE_CASE							,		rstrip=_SCREAMING_SNAKE_CASE							)
	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(_SCREAMING_SNAKE_CASE							)
	with open(os.path.join(_SCREAMING_SNAKE_CASE							,		"""tokenizer_config.json"""							)							,		"""r"""							) as f:
		lowerCAmelCase   =     json.load(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     """MLukeTokenizer"""
	with open(os.path.join(_SCREAMING_SNAKE_CASE							,		"""tokenizer_config.json"""							)							,		"""w"""							) as f:
		json.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase   =     MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE							)

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

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

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

	lowerCAmelCase   =     LukeForMaskedLM(config=_SCREAMING_SNAKE_CASE							).eval()

	state_dict.pop("""entity_predictions.decoder.weight"""							)
	state_dict.pop("""lm_head.decoder.weight"""							)
	state_dict.pop("""lm_head.decoder.bias"""							)
	lowerCAmelCase   =     OrderedDict()
	for key, value in state_dict.items():
		if not (key.startswith("""lm_head"""							) or key.startswith("""entity_predictions"""							)):
			lowerCAmelCase   =     state_dict[key]
		else:
			lowerCAmelCase   =     state_dict[key]

	lowerCAmelCase,       lowerCAmelCase   =     model.load_state_dict(_SCREAMING_SNAKE_CASE							,		strict=_SCREAMING_SNAKE_CASE							)

	if set(_SCREAMING_SNAKE_CASE							) != {"luke.embeddings.position_ids"}:
		raise ValueError(f'Unexpected unexpected_keys: {unexpected_keys}'							)
	if set(_SCREAMING_SNAKE_CASE							) != {
	    "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
	lowerCAmelCase   =     MLukeTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE							,		task="""entity_classification"""							)

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

	lowerCAmelCase   =     model(**_SCREAMING_SNAKE_CASE							)

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

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

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

	lowerCAmelCase   =     model(**_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase   =     outputs.entity_logits[0][0].argmax().item()
	lowerCAmelCase   =     [
	    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(_SCREAMING_SNAKE_CASE							)							)
	model.save_pretrained(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						List[str]:







	"""simple docstring"""
	lowerCAmelCase   =     ["""[MASK]""", """[PAD]""", """[UNK]"""]

	lowerCAmelCase   =     [json.loads(_SCREAMING_SNAKE_CASE							) for line in open(_SCREAMING_SNAKE_CASE							)]

	lowerCAmelCase   =     {}
	for entry in data:
		lowerCAmelCase   =     entry["""id"""]
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				lowerCAmelCase   =     entity_id
				break
			lowerCAmelCase   =     f'{language}:{entity_name}'
			lowerCAmelCase   =     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,
				)




'''simple docstring'''

import tensorflow as tf

from ...tf_utils import shape_list

class __snake_case(				tf.keras.layers.Layer ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_=1     ,	A_=False     ,	**A_				)     ->     Optional[int]:
							super().__init__(**A_				)

							lowerCAmelCase   =     vocab_size
							lowerCAmelCase   =     d_embed
							lowerCAmelCase   =     d_proj

							lowerCAmelCase   =     cutoffs + [vocab_size]
							lowerCAmelCase   =     [0] + self.cutoffs
							lowerCAmelCase   =     div_val

							lowerCAmelCase   =     self.cutoffs[0]
							lowerCAmelCase   =     len(self.cutoffs				) - 1
							lowerCAmelCase   =     self.shortlist_size + self.n_clusters
							lowerCAmelCase   =     keep_order

							lowerCAmelCase   =     []
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     int:
							if self.n_clusters > 0:
								lowerCAmelCase   =     self.add_weight(
								    shape=(self.n_clusters, self.d_embed)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name="""cluster_weight"""				)
								lowerCAmelCase   =     self.add_weight(
								    shape=(self.n_clusters,)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name="""cluster_bias"""				)

							if self.div_val == 1:
								for i in range(len(self.cutoffs				)				):
									if self.d_proj != self.d_embed:
										lowerCAmelCase   =     self.add_weight(
										    shape=(self.d_embed, self.d_proj)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_projs_._{i}'     ,	)
										self.out_projs.append(A_				)
									else:
										self.out_projs.append(A_				)
									lowerCAmelCase   =     self.add_weight(
									    shape=(self.vocab_size, self.d_embed)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_layers_._{i}_._weight'     ,	)
									lowerCAmelCase   =     self.add_weight(
									    shape=(self.vocab_size,)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_layers_._{i}_._bias'     ,	)
									self.out_layers.append((weight, bias)				)
							else:
								for i in range(len(self.cutoffs				)				):
									lowerCAmelCase,       lowerCAmelCase   =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
									lowerCAmelCase   =     self.d_embed // (self.div_val**i)

									lowerCAmelCase   =     self.add_weight(
									    shape=(d_emb_i, self.d_proj)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_projs_._{i}'				)
									self.out_projs.append(A_				)
									lowerCAmelCase   =     self.add_weight(
									    shape=(r_idx - l_idx, d_emb_i)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_layers_._{i}_._weight'     ,	)
									lowerCAmelCase   =     self.add_weight(
									    shape=(r_idx - l_idx,)     ,	initializer="""zeros"""     ,	trainable=A_     ,	name=f'out_layers_._{i}_._bias'     ,	)
									self.out_layers.append((weight, bias)				)
							super().build(A_				)


						@staticmethod
						def     __snake_case   ( A_     ,	A_     ,	A_     ,	A_=None				)     ->     List[Any]:
							lowerCAmelCase   =     x
							if proj is not None:
								lowerCAmelCase   =     tf.einsum("""ibd,ed->ibe"""     ,	A_     ,	A_				)
							return tf.einsum("""ibd,nd->ibn"""     ,	A_     ,	A_				) + b


						@staticmethod
						def     __snake_case   ( A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     shape_list(A_				)
							lowerCAmelCase   =     tf.range(lp_size[0]     ,	dtype=target.dtype				)
							lowerCAmelCase   =     tf.stack([r, target]     ,	1				)
							return tf.gather_nd(A_     ,	A_				)




						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_=True     ,	A_=False				)     ->     Tuple:
							lowerCAmelCase   =     0
							if self.n_clusters == 0:
								lowerCAmelCase   =     self._logit(A_     ,	self.out_layers[0][0]     ,	self.out_layers[0][1]     ,	self.out_projs[0]				)
								if target is not None:
									lowerCAmelCase   =     tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A_     ,	logits=A_				)
								lowerCAmelCase   =     tf.nn.log_softmax(A_     ,	axis=-1				)
							else:
								lowerCAmelCase   =     shape_list(A_				)
								lowerCAmelCase   =     []
								lowerCAmelCase   =     tf.zeros(hidden_sizes[:2]				)
								for i in range(len(self.cutoffs				)				):
									lowerCAmelCase,       lowerCAmelCase   =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
									if target is not None:
										lowerCAmelCase   =     (target >= l_idx) & (target < r_idx)
										lowerCAmelCase   =     tf.where(A_				)
										lowerCAmelCase   =     tf.boolean_mask(A_     ,	A_				) - l_idx

									if self.div_val == 1:
										lowerCAmelCase   =     self.out_layers[0][0][l_idx:r_idx]
										lowerCAmelCase   =     self.out_layers[0][1][l_idx:r_idx]
									else:
										lowerCAmelCase   =     self.out_layers[i][0]
										lowerCAmelCase   =     self.out_layers[i][1]

									if i == 0:
										lowerCAmelCase   =     tf.concat([cur_W, self.cluster_weight]     ,	0				)
										lowerCAmelCase   =     tf.concat([cur_b, self.cluster_bias]     ,	0				)

										lowerCAmelCase   =     self._logit(A_     ,	A_     ,	A_     ,	self.out_projs[0]				)
										lowerCAmelCase   =     tf.nn.log_softmax(A_				)
										out.append(head_logprob[..., : self.cutoffs[0]]				)
										if target is not None:
											lowerCAmelCase   =     tf.boolean_mask(A_     ,	A_				)
											lowerCAmelCase   =     self._gather_logprob(A_     ,	A_				)
									else:
										lowerCAmelCase   =     self._logit(A_     ,	A_     ,	A_     ,	self.out_projs[i]				)
										lowerCAmelCase   =     tf.nn.log_softmax(A_				)
										lowerCAmelCase   =     self.cutoffs[0] + i - 1  # No probability for the head cluster
										lowerCAmelCase   =     head_logprob[..., cluster_prob_idx, None] + tail_logprob
										out.append(A_				)
										if target is not None:
											lowerCAmelCase   =     tf.boolean_mask(A_     ,	A_				)
											lowerCAmelCase   =     tf.boolean_mask(A_     ,	A_				)
											lowerCAmelCase   =     self._gather_logprob(A_     ,	A_				)
											cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
									if target is not None:
										loss += tf.scatter_nd(A_     ,	-cur_logprob     ,	shape_list(A_				)				)
								lowerCAmelCase   =     tf.concat(A_     ,	axis=-1				)

							if target is not None:
								if return_mean:
									lowerCAmelCase   =     tf.reduce_mean(A_				)
								# Add the training-time loss value to the layer using `self.add_loss()`.
								self.add_loss(A_				)

								# Log the loss as a metric (we could log arbitrary metrics,
								# including different metrics for training and inference.
								self.add_metric(A_     ,	name=self.name     ,	aggregation="""mean""" if return_mean else """"""				)

							return out