





'''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 		__a(SCREAMING_SNAKE_CASE_     :    Union[str, Any]						,				SCREAMING_SNAKE_CASE_     :    Union[str, Any]						,				SCREAMING_SNAKE_CASE_     :    Optional[int]						,				SCREAMING_SNAKE_CASE_     :    Tuple						,				SCREAMING_SNAKE_CASE_     :    int			):



    '''simple docstring'''





    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			=					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, 1024)			)
        _lowerCAmelCase			=					torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]			)
    else:  # base
        _lowerCAmelCase			=					torch.Size((1, 42, 768)			)
        _lowerCAmelCase			=					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]						,				A_						,				atol=1e-4			):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _lowerCAmelCase			=					torch.Size((1, 1, 1024)			)
        _lowerCAmelCase			=					torch.tensor([[0.0466, -0.0106, -0.0179]]			)
    else:  # base
        _lowerCAmelCase			=					torch.Size((1, 1, 768)			)
        _lowerCAmelCase			=					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]						,				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_     :    Optional[int]			):



    '''simple docstring'''





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

    return entity_vocab


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE		       =							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."
       )
       _SCREAMING_SNAKE_CASE		       =							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 numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







					def __len__(       self			:				Any		):
						return len(self.dataset		)







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Tuple=None		):
						lowerCAmelCase__	:						List[Any]  				=	loader
						lowerCAmelCase__	:						int  				=	infer
						lowerCAmelCase__	:						List[str]  				=	params
						if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							lowerCAmelCase__	:						int  				=	None
						lowerCAmelCase__	:						Dict  				=	loader_batch_size

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				Tuple		):
						if isinstance(self._loader_batch_data   ,torch.Tensor		):
							# Batch data is simple tensor, just fetch the slice
							lowerCAmelCase__	:						Tuple  				=	self._loader_batch_data[self._loader_batch_index]
						else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							lowerCAmelCase__	:						int  				=	{}
							for k, element in self._loader_batch_data.items():
								if isinstance(lowercase_   ,lowercase_		):
									# Convert ModelOutput to tuple first
									lowerCAmelCase__	:						List[Any]  				=	element.to_tuple()
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						List[Any]  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						str  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase_   ,lowercase_		):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]   ,torch.Tensor		):
										lowerCAmelCase__	:						Dict  				=	tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
									elif isinstance(element[0]   ,np.ndarray		):
										lowerCAmelCase__	:						Optional[int]  				=	tuple(np.expand_dims(el[self._loader_batch_index]   ,0		) for el in element		)
									continue
								if element is None:
									# This can happen for optional data that get passed around
									lowerCAmelCase__	:						Dict  				=	None
								elif isinstance(element[self._loader_batch_index]   ,torch.Tensor		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									lowerCAmelCase__	:						str  				=	element[self._loader_batch_index].unsqueeze(0		)
								elif isinstance(element[self._loader_batch_index]   ,np.ndarray		):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									lowerCAmelCase__	:						Tuple  				=	np.expand_dims(element[self._loader_batch_index]   ,0		)
								else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase__	:						int  				=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							lowerCAmelCase__	:						int  				=	self._loader_batch_data.__class__(lowercase_		)
						self._loader_batch_index += 1
						return result







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

						# We're out of items within a batch
						lowerCAmelCase__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**self.params		)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[str]  				=	first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	next(self.subiterator		)
						except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Extremely similar to PipelineIterator in its unpacking mechanism
						# BUT, we have an extra required item which is the presence of `is_last`
						# That is because everything is flattened by `PipelineChunkIterator` we
						# need to keep track of how to regroup here in the original `process`
						# boundaries so that `process` and `postprocess` see the same data.

						# This iterator accumulates items (possibly while unbatching) until it
						# its a `is_last` and then just passes it on to the caller.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
								lowerCAmelCase__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

						while not is_last:
							lowerCAmelCase__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							if self.loader_batch_size is not None:
								if isinstance(lowercase_   ,torch.Tensor		):
									lowerCAmelCase__	:						Tuple  				=	processed
								else:
									lowerCAmelCase__	:						List[Any]  				=	list(processed.keys()		)[0]
									lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
								if isinstance(lowercase_   ,lowercase_		):
									lowerCAmelCase__	:						Tuple  				=	len(lowercase_		)
								else:
									lowerCAmelCase__	:						str  				=	first_tensor.shape[0]
								if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									lowerCAmelCase__	:						Optional[int]  				=	observed_batch_size
								lowerCAmelCase__	:						Optional[int]  				=	processed
								lowerCAmelCase__	:						Optional[int]  				=	0
								while self._loader_batch_index < self.loader_batch_size:
									lowerCAmelCase__	:						Any  				=	self.loader_batch_item()
									lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
									accumulator.append(lowercase_		)
									if is_last:
										return accumulator
							else:
								lowerCAmelCase__	:						Dict  				=	processed
								lowerCAmelCase__	:						Tuple  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
						return accumulator







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







					def __len__(       self			:				List[Any]		):
						return len(self.dataset		)







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







					def __len__(       self			:				str		):
						return len(self.dataset		)







					def __getitem__(       self			:				Optional[int]   ,lowercase_			:				Union[str, Any]		):
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




