



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_      (	__a     :				Dict ,		__a     :				Tuple ,		__a     :				str ,		__a     :				Tuple ,		__a     :				List[Any]		):

				"""simple docstring"""
				# Load configuration defined in the metadata file
				with open(__a		) as metadata_file:
								a__				      =		json.load(__a		)
				a__				      =		LukeConfig(use_entity_aware_attention=__a ,		**metadata["""model_config"""]		)

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

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

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

				# Add special tokens to the token vocabulary for downstream tasks
				a__				      =		AddedToken("""<ent>""" ,		lstrip=__a ,		rstrip=__a		)
				a__				      =		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		)

				a__				      =		LukeTokenizer.from_pretrained(__a		)

				# Initialize the embeddings of the special tokens
				a__				      =		state_dict['''embeddings.word_embeddings.weight''']
				a__				      =		word_emb[tokenizer.convert_tokens_to_ids(["""@"""]		)[0]].unsqueeze(0		)
				a__				      =		word_emb[tokenizer.convert_tokens_to_ids(["""#"""]		)[0]].unsqueeze(0		)
				a__				      =		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__				      =		F'''encoder.layer.{layer_index}.attention.self.'''
												a__				      =		state_dict[prefix + matrix_name]
												a__				      =		state_dict[prefix + matrix_name]
												a__				      =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				a__				      =		state_dict['''entity_embeddings.entity_embeddings.weight''']
				a__				      =		entity_emb[entity_vocab['''[MASK]''']]

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

				a__				      =		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
				a__				      =		LukeTokenizer.from_pretrained(__a ,		task="""entity_classification"""		)

				a__				      =		(
				    '''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__				      =		(39, 42)
				a__				      =		tokenizer(__a ,		entity_spans=[span] ,		add_prefix_space=__a ,		return_tensors="""pt"""		)

				a__				      =		model(**__a		)

				# Verify word hidden states
				if model_size == "large":
								a__				      =		torch.Size((1, 42, 1_024)		)
								a__				      =		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
								a__				      =		torch.Size((1, 42, 768)		)
								a__				      =		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] ,		__a ,		atol=1e-4		):
								raise ValueError

				# Verify entity hidden states
				if model_size == "large":
								a__				      =		torch.Size((1, 1, 1_024)		)
								a__				      =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]		)
				else:  # base
								a__				      =		torch.Size((1, 1, 768)		)
								a__				      =		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] ,		__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_      (	__a     :				str		):

				"""simple docstring"""
				a__				      =		{}
				with open(__a ,		"""r""" ,		encoding="""utf-8"""		) as f:
								for index, line in enumerate(__a		):
												a__				      =		line.rstrip().split("""\t"""		)
												a__				      =		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,
	)







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	UpperCAmelCase							 =					None

UpperCAmelCase							 =					logging.get_logger(__name__)
UpperCAmelCase							 =					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase							 =					{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

UpperCAmelCase							 =					"""▁"""

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Tuple         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Union[str, Any]         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[str]         =					AlbertTokenizer


							def __init__(							self  ,  a_=None  ,  a_=None  ,  a_=True  ,  a_=True  ,  a_=False  ,  a_="[CLS]"  ,  a_="[SEP]"  ,  a_="<unk>"  ,  a_="[SEP]"  ,  a_="<pad>"  ,  a_="[CLS]"  ,  a_="[MASK]"  ,  **a_  ,  ):
											# Mask token behave like a normal word, i.e. include the space before it and
											# is included in the raw text, there should be a match in a non-normalized sentence.
											a__				      =		(
											    AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_  ,  normalized=a_		)
											    if isinstance(a_  ,  a_		)
											    else mask_token
											)

											super().__init__(
											    a_  ,  tokenizer_file=a_  ,  do_lower_case=a_  ,  remove_space=a_  ,  keep_accents=a_  ,  bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  sep_token=a_  ,  pad_token=a_  ,  cls_token=a_  ,  mask_token=a_  ,  **a_  ,  )

											a__				      =		do_lower_case
											a__				      =		remove_space
											a__				      =		keep_accents
											a__				      =		vocab_file
											a__				      =		False if not self.vocab_file else True


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[self.cls_token_id]
											if token_ids_a is None:
															return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
											return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""		)

											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(a_		):
															copyfile(self.vocab_file  ,  a_		)

											return (out_vocab_file,)

