




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       Any  ):




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

					# Load in the weights from the checkpoint_path
					__lowerCAmelCase            = torch.load(__a   ,   map_location="cpu"  )

					# Load the entity vocab file
					__lowerCAmelCase            = load_entity_vocab(__a  )

					__lowerCAmelCase            = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]  )

					# Add special tokens to the token vocabulary for downstream tasks
					__lowerCAmelCase            = AddedToken("<ent>"   ,   lstrip=__a   ,   rstrip=__a  )
					__lowerCAmelCase            = 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   ,   LukeTokenizer.vocab_files_names["entity_vocab_file"]  )   ,   "w"  ) as f:
										json.dump(__a   ,   __a  )

					__lowerCAmelCase            = LukeTokenizer.from_pretrained(__a  )

					# Initialize the embeddings of the special tokens
					__lowerCAmelCase            = state_dict["embeddings.word_embeddings.weight"]
					__lowerCAmelCase            = word_emb[tokenizer.convert_tokens_to_ids(["@"]  )[0]].unsqueeze(0  )
					__lowerCAmelCase            = word_emb[tokenizer.convert_tokens_to_ids(["#"]  )[0]].unsqueeze(0  )
					__lowerCAmelCase            = torch.cat([word_emb, ent_emb, enta_emb]  )

					# 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]"]]

					__lowerCAmelCase            = LukeModel(config=__a  ).eval()

					__lowerCAmelCase     ,							__lowerCAmelCase            = model.load_state_dict(__a   ,   strict=__a  )
					if not (len(__a  ) == 1 and missing_keys[0] == "embeddings.position_ids"):
										raise ValueError(F"""Missing keys {', '.join(__a  )}. Expected only missing embeddings.position_ids"""  )
					if not (all(key.startswith("entity_predictions"  ) or key.startswith("lm_head"  ) for key in unexpected_keys  )):
										raise ValueError(
										    "Unexpected keys"
										    F""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'  ) or key.startswith('lm_head'  ))]  )}"""  )

					# Check outputs
					__lowerCAmelCase            = LukeTokenizer.from_pretrained(__a   ,   task="entity_classification"  )

					__lowerCAmelCase            = (
					    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
					    " new world number one avoid a humiliating second- round exit at Wimbledon ."
					)
					__lowerCAmelCase            = (39, 42)
					__lowerCAmelCase            = tokenizer(__a   ,   entity_spans=[span]   ,   add_prefix_space=__a   ,   return_tensors="pt"  )

					__lowerCAmelCase            = model(**__a  )

					# Verify word hidden states
					if model_size == "large":
										__lowerCAmelCase            = torch.Size((1, 42, 10_24)  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]  )
					else:  # base
										__lowerCAmelCase            = torch.Size((1, 42, 7_68)  )
										__lowerCAmelCase            = torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]  )

					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":
										__lowerCAmelCase            = torch.Size((1, 1, 10_24)  )
										__lowerCAmelCase            = torch.tensor([[0.04_66, -0.01_06, -0.01_79]]  )
					else:  # base
										__lowerCAmelCase            = torch.Size((1, 1, 7_68)  )
										__lowerCAmelCase            = torch.tensor([[0.14_57, 0.10_44, 0.01_74]]  )

					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

					# Finally, save our PyTorch model and tokenizer
					print("Saving PyTorch model to {}".format(__a  )  )
					model.save_pretrained(__a  )







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = {}
					with open(__a   ,   "r"   ,   encoding="utf-8"  ) as f:
										for index, line in enumerate(__a  ):
															__lowerCAmelCase     ,							__lowerCAmelCase            = line.rstrip().split("\t"  )
															__lowerCAmelCase            = index

					return entity_vocab


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,
					)










from typing import TYPE_CHECKING

from ....utils import _LazyModule


UpperCamelCase__					    =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_tapex import TapexTokenizer


else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)




