

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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		List[str]:







						"""simple docstring"""

						with open(_lowerCamelCase       ) as metadata_file:
												A__	:							Union[str, Any]						    = json.load(_lowerCamelCase       )
						A__	:							List[str]						    = LukeConfig(use_entity_aware_attention=_lowerCamelCase							,     **metadata["model_config"]       )

						# Load in the weights from the checkpoint_path
						A__	:							List[str]						    = torch.load(_lowerCamelCase							,     map_location="cpu"       )

						# Load the entity vocab file
						A__	:							Dict						    = load_entity_vocab(_lowerCamelCase       )

						A__	:							Tuple						    = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]       )

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

						A__	:							Optional[Any]						    = LukeTokenizer.from_pretrained(_lowerCamelCase       )

						# Initialize the embeddings of the special tokens
						A__	:							Union[str, Any]						    = state_dict['embeddings.word_embeddings.weight']
						A__	:							Optional[int]						    = word_emb[tokenizer.convert_tokens_to_ids(["@"]       )[0]].unsqueeze(0       )
						A__	:							Dict						    = word_emb[tokenizer.convert_tokens_to_ids(["#"]       )[0]].unsqueeze(0       )
						A__	:							Tuple						    = 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"]:
																		A__	:							Union[str, Any]						    = F'encoder.layer.{layer_index}.attention.self.'
																		A__	:							Dict						    = state_dict[prefix + matrix_name]
																		A__	:							Union[str, Any]						    = state_dict[prefix + matrix_name]
																		A__	:							Union[str, Any]						    = state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						A__	:							List[Any]						    = state_dict['entity_embeddings.entity_embeddings.weight']
						A__	:							Tuple						    = entity_emb[entity_vocab['[MASK]']]

						A__	:							Union[str, Any]						    = LukeModel(config=_lowerCamelCase       ).eval()

						A__	:							Any						    = model.load_state_dict(_lowerCamelCase							,     strict=_lowerCamelCase       )
						if not (len(_lowerCamelCase       ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(F'Missing keys {", ".join(_lowerCamelCase       )}. 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
						A__	:							Any						    = LukeTokenizer.from_pretrained(_lowerCamelCase							,     task="entity_classification"       )

						A__	:							Tuple						    = (
						    '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 .'
						)
						A__	:							Optional[Any]						    = (3_9, 4_2)
						A__	:							Union[str, Any]						    = tokenizer(_lowerCamelCase							,     entity_spans=[span]							,     add_prefix_space=_lowerCamelCase							,     return_tensors="pt"       )

						A__	:							int						    = model(**_lowerCamelCase       )

						# Verify word hidden states
						if model_size == "large":
												A__	:							Optional[Any]						    = torch.Size((1, 4_2, 1_0_2_4)       )
												A__	:							Tuple						    = torch.tensor(
												    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]       )
						else:  # base
												A__	:							str						    = torch.Size((1, 4_2, 7_6_8)       )
												A__	:							Optional[Any]						    = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]       )

						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":
												A__	:							Optional[Any]						    = torch.Size((1, 1, 1_0_2_4)       )
												A__	:							List[Any]						    = torch.tensor([[0.0466, -0.0106, -0.0179]]       )
						else:  # base
												A__	:							Tuple						    = torch.Size((1, 1, 7_6_8)       )
												A__	:							Optional[Any]						    = torch.tensor([[0.1457, 0.1044, 0.0174]]       )

						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

						# Finally, save our PyTorch model and tokenizer
						print("Saving PyTorch model to {}".format(_lowerCamelCase       )       )
						model.save_pretrained(_lowerCamelCase       )
def 	snake_case__       (					__lowercase       )		->		List[Any]:







						"""simple docstring"""

						A__	:							str						    = {}
						with open(_lowerCamelCase							,     "r"							,     encoding="utf-8"       ) as f:
												for index, line in enumerate(_lowerCamelCase       ):
																		A__	:							List[str]						    = line.rstrip().split("\t"       )
																		A__	:							Any						    = index

						return entity_vocab


if __name__ == "__main__":
		snake_case    :						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.'
		)
		snake_case    :						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 multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Dict			,		_A      :     NestedDataStructureLike[PathLike]			,		_A      :     Optional[NamedSplit] = None			,		_A      :     Optional[Features] = None			,		_A      :     str = None			,		_A      :     bool = False			,		_A      :     bool = False			,		_A      :     Optional[str] = None			,		_A      :     Optional[int] = None			,		**_A      :     Dict			,		):
											super().__init__(
											    _A			,		split=_A			,		features=_A			,		cache_dir=_A			,		keep_in_memory=_A			,		streaming=_A			,		num_proc=_A			,		**_A			,		)
											A__	:							Optional[int]						    = field
											A__	:							Dict						    = path_or_paths if isinstance(_A			,		_A) else {self.split: path_or_paths}
											A__	:							int						    = Json(
											    cache_dir=_A			,		data_files=_A			,		features=_A			,		field=_A			,		**_A			,		)







					def  _lowercase			(       self      :     Dict):
											# Build iterable dataset
											if self.streaming:
																	A__	:							Union[str, Any]						    = self.builder.as_streaming_dataset(split=self.split)
											# Build regular (map-style) dataset
											else:
																	A__	:							Optional[Any]						    = None
																	A__	:							Any						    = None
																	A__	:							Tuple						    = None
																	A__	:							Tuple						    = None

																	self.builder.download_and_prepare(
																	    download_config=_A			,		download_mode=_A			,		verification_mode=_A			,		base_path=_A			,		num_proc=self.num_proc			,		)
																	A__	:							Any						    = self.builder.as_dataset(
																	    split=self.split			,		verification_mode=_A			,		in_memory=self.keep_in_memory)
											return dataset




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dataset			,		_A      :     Union[PathLike, BinaryIO]			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		**_A      :     Any			,		):
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

											A__	:							Tuple						    = dataset
											A__	:							Tuple						    = path_or_buf
											A__	:							int						    = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											A__	:							Tuple						    = num_proc
											A__	:							Any						    = "utf-8"
											A__	:							Optional[int]						    = to_json_kwargs







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Any						    = self.to_json_kwargs.pop("path_or_buf"			,		_A)
											A__	:							Union[str, Any]						    = self.to_json_kwargs.pop("orient"			,		"records")
											A__	:							List[Any]						    = self.to_json_kwargs.pop("lines"			,		True if orient == "records" else False)
											A__	:							List[Any]						    = self.to_json_kwargs.pop("index"			,		False if orient in ["split", "table"] else True)
											A__	:							int						    = self.to_json_kwargs.pop("compression"			,		_A)

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

											if isinstance(self.path_or_buf			,		(str, bytes, os.PathLike)):
																	with fsspec.open(self.path_or_buf			,		"wb"			,		compression=_A) as buffer:
																							A__	:							int						    = self._write(file_obj=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											else:
																	if compression:
																							raise NotImplementedError(
																							    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																							    " was passed. Please provide a local path instead.")
																	A__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

											A__	:							Dict						    = query_table(
											    table=self.dataset.data			,		key=slice(_A			,		offset + self.batch_size)			,		indices=self.dataset._indices			,		)
											A__	:							Dict						    = batch.to_pandas().to_json(
											    path_or_buf=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**_A)
											if not json_str.endswith("\n"):
																	json_str += "\n"
											return json_str.encode(self.encoding)







					def  _lowercase			(       self      :     List[str]			,		_A      :     BinaryIO			,		_A      :     str			,		_A      :     List[Any]			,		_A      :     Optional[Any]			,		**_A      :     Optional[Any]			,		):
											A__	:							Any						    = 0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0			,		len(self.dataset)			,		self.batch_size)			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																							A__	:							List[str]						    = self._batch_json((offset, orient, lines, index, to_json_kwargs))
																							written += file_obj.write(_A)
											else:
																	A__    ,       A__	:							Union[str, Any]						    = len(self.dataset), self.batch_size
																	with multiprocessing.Pool(self.num_proc) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json			,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,		_A			,		_A)]			,		)			,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																													written += file_obj.write(_A)

											return written