




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		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   List[Any]	):




	"""simple docstring"""







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

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

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

	UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase	)

	# 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=_lowerCAmelCase	).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(_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
	UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase						,							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(_lowerCAmelCase						,							entity_spans=[span]						,							return_tensors="pt"	)

	UpperCAmelCase__							 =						model(**_lowerCAmelCase	)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		UpperCAmelCase__							 =						torch.Size((1, 33, 768)	)
		UpperCAmelCase__							 =						torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

	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
		UpperCAmelCase__							 =						torch.Size((1, 1, 768)	)
		UpperCAmelCase__							 =						torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

	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
	UpperCAmelCase__							 =						MLukeTokenizer.from_pretrained(_lowerCAmelCase	)
	UpperCAmelCase__							 =						"Tokyo is the capital of <mask>."
	UpperCAmelCase__							 =						(24, 30)
	UpperCAmelCase__							 =						tokenizer(_lowerCAmelCase						,							entity_spans=[span]						,							return_tensors="pt"	)

	UpperCAmelCase__							 =						model(**_lowerCAmelCase	)

	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(_lowerCAmelCase	)

	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(_lowerCAmelCase	)	)
	model.save_pretrained(_lowerCAmelCase	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]	):




	"""simple docstring"""







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

	UpperCAmelCase__							 =						[json.loads(_lowerCAmelCase	) for line in open(_lowerCAmelCase	)]

	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__":
		_lowerCAmelCase						:   List[str]		=   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."
		)
		_lowerCAmelCase						:   Optional[Any]		=   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 argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Union[str, Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						BigBirdConfig.from_json_file(_lowerCAmelCase	)
 print(F'''Building PyTorch model from configuration: {config}'''	)

 if is_trivia_qa:
  UpperCAmelCase__							 =						BigBirdForQuestionAnswering(_lowerCAmelCase	)
 else:
  UpperCAmelCase__							 =						BigBirdForPreTraining(_lowerCAmelCase	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_lowerCAmelCase						,							_lowerCAmelCase						,							is_trivia_qa=_lowerCAmelCase	)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   List[Any]		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--big_bird_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained BERT model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
  )
  _lowerCAmelCase						:   Dict		=   parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )





