

"""simple docstring"""


import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast





@dataclass
class    snake_case     (    datasets.BuilderConfig  ):
				SCREAMING_SNAKE_CASE_       :				Optional[datasets.Features]       				=  None





class    snake_case     (    datasets.ArrowBasedBuilder  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  PandasConfig

				def 							lowercase_					(     self							:    List[Any])->       int:



						'''simple docstring'''


						return datasets.DatasetInfo(features=self.config.features)

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int)->       List[str]:



						'''simple docstring'''


						if not self.config.data_files:
								raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}")
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(self.config.data_files)
						if isinstance(UpperCamelCase__					,  (str, list, tuple)):
								__lowerCAmelCase:	Optional[Any]   				=      data_files
								if isinstance(UpperCamelCase__					,  UpperCamelCase__):
										__lowerCAmelCase:	int   				=      [files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								__lowerCAmelCase:	int   				=      [dl_manager.iter_files(UpperCamelCase__) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,  gen_kwargs={"files": files})]
						__lowerCAmelCase:	Tuple   				=      []
						for split_name, files in data_files.items():
								if isinstance(UpperCamelCase__					,  UpperCamelCase__):
										__lowerCAmelCase:	List[str]   				=      [files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								__lowerCAmelCase:	int   				=      [dl_manager.iter_files(UpperCamelCase__) for file in files]
								splits.append(datasets.SplitGenerator(name=UpperCamelCase__					,  gen_kwargs={"files": files}))
						return splits

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    pa.Table)->       pa.Table:



						'''simple docstring'''


						if self.config.features is not None:
								# more expensive cast to support nested features with keys in a different order
								# allows str <-> int/float or str to Audio for example
								__lowerCAmelCase:	Dict   				=      table_cast(UpperCamelCase__					,  self.config.features.arrow_schema)
						return pa_table




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple)->       List[str]:



						'''simple docstring'''


						for i, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__)):
								with open(UpperCamelCase__					,  "rb") as f:
										__lowerCAmelCase:	str   				=      pa.Table.from_pandas(pd.read_pickle(UpperCamelCase__))
										yield i, self._cast_table(UpperCamelCase__)





"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		# Load configuration defined in the metadata file
		with open(__SCREAMING_SNAKE_CASE			) as metadata_file:
				__lowerCAmelCase:	List[Any]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE ,			**metadata["model_config"]			)

		# Load in the weights from the checkpoint_path
		__lowerCAmelCase:	Optional[Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["module"]

		# Load the entity vocab file
		__lowerCAmelCase:	List[Any]   				=      load_original_entity_vocab(__SCREAMING_SNAKE_CASE			)
		# add an entry for [MASK2]
		__lowerCAmelCase:	Any   				=      max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

		__lowerCAmelCase:	Union[str, Any]   				=      XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

		# Add special tokens to the token vocabulary for downstream tasks
		__lowerCAmelCase:	str   				=      AddedToken("<ent>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      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:	Optional[int]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      "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:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# Initialize the embeddings of the special tokens
		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_tokens_to_ids(["@"]			)[0]
		__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(["#"]			)[0]

		__lowerCAmelCase:	Dict   				=      state_dict["embeddings.word_embeddings.weight"]
		__lowerCAmelCase:	Optional[int]   				=      word_emb[ent_init_index].unsqueeze(0			)
		__lowerCAmelCase:	int   				=      word_emb[enta_init_index].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      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:	Dict   				=      state_dict[bias_name]
				__lowerCAmelCase:	Union[str, Any]   				=      decoder_bias[ent_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      decoder_bias[enta_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      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:	Optional[int]   				=      F"encoder.layer.{layer_index}.attention.self."
						__lowerCAmelCase:	Tuple   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Dict   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Optional[Any]   				=      state_dict[prefix + matrix_name]

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

		__lowerCAmelCase:	Optional[int]   				=      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:	Tuple   				=      OrderedDict()
		for key, value in state_dict.items():
				if not (key.startswith("lm_head"			) or key.startswith("entity_predictions"			)):
						__lowerCAmelCase:	Any   				=      state_dict[key]
				else:
						__lowerCAmelCase:	Tuple   				=      state_dict[key]

		__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      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:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,			task="entity_classification"			)

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

		__lowerCAmelCase:	int   				=      model(**__SCREAMING_SNAKE_CASE			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Dict   				=      torch.Size((1, 3_3, 7_6_8)			)
				__lowerCAmelCase:	Optional[int]   				=      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:	Union[str, Any]   				=      torch.Size((1, 1, 7_6_8)			)
				__lowerCAmelCase:	Any   				=      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:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      "Tokyo is the capital of <mask>."
		__lowerCAmelCase:	List[str]   				=      (2_4, 3_0)
		__lowerCAmelCase:	int   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	Union[str, Any]   				=      model(**__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Optional[int]   				=      encoding["input_ids"][0].tolist()
		__lowerCAmelCase:	int   				=      input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"			)			)
		__lowerCAmelCase:	Optional[Any]   				=      outputs.logits[0][mask_position_id].argmax(dim=-1			)
		assert "Japan" == tokenizer.decode(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	Any   				=      outputs.entity_logits[0][0].argmax().item()
		__lowerCAmelCase:	Union[str, Any]   				=      [
		    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    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Tuple   				=      ["[MASK]", "[PAD]", "[UNK]"]

		__lowerCAmelCase:	Optional[Any]   				=      [json.loads(__SCREAMING_SNAKE_CASE			) for line in open(__SCREAMING_SNAKE_CASE			)]

		__lowerCAmelCase:	str   				=      {}
		for entry in data:
				__lowerCAmelCase:	Tuple   				=      entry["id"]
				for entity_name, language in entry["entities"]:
						if entity_name in SPECIAL_TOKENS:
								__lowerCAmelCase:	Optional[int]   				=      entity_id
								break
						__lowerCAmelCase:	Optional[Any]   				=      F"{language}:{entity_name}"
						__lowerCAmelCase:	Optional[int]   				=      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,
			)


