

'''simple docstring'''





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 							_lowerCAmelCase (      __magic_name__	:    Dict      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    str      ,   __magic_name__	:    Tuple      ,   __magic_name__	:    List[Any]	)							->     Any:
						# Load configuration defined in the metadata file
						with open(__magic_name__	) as metadata_file:
												lowercase   :       Union[str, Any]								=json.load(__magic_name__	)
						lowercase   :       int								=LukeConfig(use_entity_aware_attention=__magic_name__      ,   **metadata['''model_config''']	)

						# Load in the weights from the checkpoint_path
						lowercase   :       Optional[int]								=torch.load(__magic_name__      ,   map_location='''cpu'''	)

						# Load the entity vocab file
						lowercase   :       Optional[Any]								=load_entity_vocab(__magic_name__	)

						lowercase   :       Optional[Any]								=RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']	)

						# Add special tokens to the token vocabulary for downstream tasks
						lowercase   :       int								=AddedToken('''<ent>'''      ,   lstrip=__magic_name__      ,   rstrip=__magic_name__	)
						lowercase   :       Optional[Any]								=AddedToken('''<ent2>'''      ,   lstrip=__magic_name__      ,   rstrip=__magic_name__	)
						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(__magic_name__	)
						with open(os.path.join(__magic_name__      ,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']	)      ,   '''w'''	) as f:
												json.dump(__magic_name__      ,   __magic_name__	)

						lowercase   :       str								=LukeTokenizer.from_pretrained(__magic_name__	)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						lowercase   :       Optional[int]								=state_dict['''entity_embeddings.entity_embeddings.weight''']
						lowercase   :       Dict								=entity_emb[entity_vocab['''[MASK]''']]

						lowercase   :       Optional[Any]								=LukeModel(config=__magic_name__	).eval()

						lowercase		,  lowercase   :       List[Any]								=model.load_state_dict(__magic_name__      ,   strict=__magic_name__	)
						if not (len(__magic_name__	) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(f'''Missing keys {", ".join(__magic_name__	)}. 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
						lowercase   :       Optional[int]								=LukeTokenizer.from_pretrained(__magic_name__      ,   task='''entity_classification'''	)

						lowercase   :       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 .'''
						)
						lowercase   :       Optional[int]								=(39, 42)
						lowercase   :       int								=tokenizer(__magic_name__      ,   entity_spans=[span]      ,   add_prefix_space=__magic_name__      ,   return_tensors='''pt'''	)

						lowercase   :       Any								=model(**__magic_name__	)

						# Verify word hidden states
						if model_size == "large":
												lowercase   :       str								=torch.Size((1, 42, 1024)	)
												lowercase   :       List[Any]								=torch.tensor(
												    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]	)
						else:  # base
												lowercase   :       List[Any]								=torch.Size((1, 42, 768)	)
												lowercase   :       List[str]								=torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]	)

						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]      ,   __magic_name__      ,   atol=1E-4	):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												lowercase   :       Dict								=torch.Size((1, 1, 1024)	)
												lowercase   :       int								=torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]	)
						else:  # base
												lowercase   :       Dict								=torch.Size((1, 1, 768)	)
												lowercase   :       Union[str, Any]								=torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]	)

						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]      ,   __magic_name__      ,   atol=1E-4	):
												raise ValueError

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






def 							_lowerCAmelCase (      __magic_name__	:    str	)							->     Any:
						lowercase   :       Dict								={}
						with open(__magic_name__      ,   '''r'''      ,   encoding='''utf-8'''	) as f:
												for index, line in enumerate(__magic_name__	):
																		lowercase		,  lowercase   :       str								=line.rstrip().split('''\t'''	)
																		lowercase   :       int								=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,
							)








'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					"""▁"""

UpperCamelCase_							=					{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_							=					{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCamelCase_							=					{
    """facebook/xglm-564M""": 2048,
}





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						VOCAB_FILES_NAMES
				lowerCamelCase_							=						PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_							=						['input_ids', 'attention_mask']







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]="<s>"		,				UpperCAmelCase__    :		int="</s>"		,				UpperCAmelCase__    :		Optional[Any]="</s>"		,				UpperCAmelCase__    :		Optional[Any]="<s>"		,				UpperCAmelCase__    :		Any="<unk>"		,				UpperCAmelCase__    :		Any="<pad>"		,				UpperCAmelCase__    :		Optional[Dict[str, Any]] = None		,				**UpperCAmelCase__    :		Optional[int]		,				):
										'''simple docstring'''
										lowercase   :       int								={} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										lowercase   :       Optional[Any]								=7
										lowercase   :       Optional[int]								=[F'''<madeupword{i}>''' for i in range(self.num_madeup_words      )]

										lowercase   :       List[Any]								=kwargs.get('''additional_special_tokens'''		,				[]      )
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    bos_token=UpperCAmelCase__		,				eos_token=UpperCAmelCase__		,				unk_token=UpperCAmelCase__		,				sep_token=UpperCAmelCase__		,				cls_token=UpperCAmelCase__		,				pad_token=UpperCAmelCase__		,				sp_model_kwargs=self.sp_model_kwargs		,				**UpperCAmelCase__		,				)

										lowercase   :       List[str]								=spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(str(UpperCAmelCase__      )      )
										lowercase   :       List[Any]								=vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										lowercase   :       Union[str, Any]								=1

										# Mimic fairseq token-to-id alignment for the first 4 token
										lowercase   :       List[str]								={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										lowercase   :       str								=len(self.sp_model      )
										lowercase   :       List[Any]								={F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
										self.fairseq_tokens_to_ids.update(UpperCAmelCase__      )

										lowercase   :       int								={v: k for k, v in self.fairseq_tokens_to_ids.items()}







				def __getstate__(					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.__dict__.copy()
										lowercase   :       List[Any]								=None
										lowercase   :       Tuple								=self.sp_model.serialized_model_proto()
										return state







				def __setstate__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       int								=d

										# for backward compatibility
										if not hasattr(self		,				'''sp_model_kwargs'''      ):
																lowercase   :       Optional[int]								={}

										lowercase   :       List[Any]								=spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
										'''simple docstring'''

										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										lowercase   :       List[Any]								=[self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None		,				UpperCAmelCase__    :		bool = False      ):
										'''simple docstring'''

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=UpperCAmelCase__		,				token_ids_a=UpperCAmelCase__		,				already_has_special_tokens=UpperCAmelCase__      )

										if token_ids_a is None:
																return [1] + ([0] * len(UpperCAmelCase__      ))
										return [1] + ([0] * len(UpperCAmelCase__      )) + [1, 1] + ([0] * len(UpperCAmelCase__      ))







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
										'''simple docstring'''
										lowercase   :       int								=[self.sep_token_id]

										if token_ids_a is None:
																return len(sep + token_ids_a      ) * [0]
										return len(sep + token_ids_a + sep + sep + token_ids_a      ) * [0]







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								={self.convert_ids_to_tokens(UpperCAmelCase__      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										return self.sp_model.encode(UpperCAmelCase__		,				out_type=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										lowercase   :       List[str]								=self.sp_model.PieceToId(UpperCAmelCase__      )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset      )







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=''''''.join(UpperCAmelCase__      ).replace(UpperCAmelCase__		,				''' '''      ).strip()
										return out_string







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Optional[str] = None      ):
										'''simple docstring'''
										if not os.path.isdir(UpperCAmelCase__      ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																return
										lowercase   :       Dict								=os.path.join(
										    UpperCAmelCase__		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase__      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file		,				UpperCAmelCase__      )
										elif not os.path.isfile(self.vocab_file      ):
																with open(UpperCAmelCase__		,				'''wb'''      ) as fi:
																						lowercase   :       Optional[int]								=self.sp_model.serialized_model_proto()
																						fi.write(UpperCAmelCase__      )

										return (out_vocab_file,)





