




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_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      Optional[int]      )     ->       int:





				"""simple docstring"""


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

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

				# Load the entity vocab file
				__lowerCamelCase		       =    load_original_entity_vocab(_lowerCamelCase      )
				# 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=_lowerCamelCase ,     rstrip=_lowerCamelCase      )
				__lowerCamelCase		       =    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:
								__lowerCamelCase		       =    json.load(_lowerCamelCase      )
				__lowerCamelCase		       =    "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      )

				__lowerCamelCase		       =    MLukeTokenizer.from_pretrained(_lowerCamelCase      )

				# 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=_lowerCamelCase      ).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		       =    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
				__lowerCamelCase		       =    MLukeTokenizer.from_pretrained(_lowerCamelCase ,     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(_lowerCamelCase ,     entity_spans=[span] ,     return_tensors='pt'      )

				__lowerCamelCase		       =    model(**_lowerCamelCase      )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								__lowerCamelCase		       =    torch.Size((1, 33, 768)      )
								__lowerCamelCase		       =    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] ,     _lowerCamelCase ,     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([[-0.14_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] ,     _lowerCamelCase ,     atol=1E-4      ):
								raise ValueError

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

				__lowerCamelCase		       =    model(**_lowerCamelCase      )

				__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(_lowerCamelCase      )

				__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(_lowerCamelCase      )      )
				model.save_pretrained(_lowerCamelCase      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple      )     ->       Tuple:





				"""simple docstring"""


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

				__lowerCamelCase		       =    [json.loads(_lowerCamelCase      ) for line in open(_lowerCamelCase      )]

				__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__":
					__A        =    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        =    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 __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

														if not self.training:
																					_lowerCAmelCase     :							int    =    entropy(__a)
																					_lowerCAmelCase     :							List[Any]    =    []
																					_lowerCAmelCase     :							str    =    []
														if labels is not None:
																					if self.num_labels == 1:
																												#  We are doing regression
																												_lowerCAmelCase     :							Optional[Any]    =    MSELoss()
																												_lowerCAmelCase     :							int    =    loss_fct(logits.view(-1),			labels.view(-1))
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    CrossEntropyLoss()
																												_lowerCAmelCase     :							Optional[Any]    =    loss_fct(logits.view(-1,			self.num_labels),			labels.view(-1))

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    highway_exit[0]
																												if not self.training:
																																			highway_logits_all.append(__a)
																																			highway_entropy.append(highway_exit[2])
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																												)  # use the highway of the last layer

														return outputs  # (loss), logits, (hidden_states), (attentions), entropy
