
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      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			with open(__lowerCAmelCase       ) as metadata_file:
						lowerCAmelCase_      			=			json.load(__lowerCAmelCase       )
			lowerCAmelCase_      			=			LukeConfig(use_entity_aware_attention=__lowerCAmelCase					,							**metadata["model_config"]       )

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

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

			lowerCAmelCase_      			=			XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]       )

			# Add special tokens to the token vocabulary for downstream tasks
			lowerCAmelCase_      			=			AddedToken("<ent>"					,							lstrip=__lowerCAmelCase					,							rstrip=__lowerCAmelCase       )
			lowerCAmelCase_      			=			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					,							"tokenizer_config.json"       )					,							"r"       ) as f:
						lowerCAmelCase_      			=			json.load(__lowerCAmelCase       )
			lowerCAmelCase_      			=			"MLukeTokenizer"
			with open(os.path.join(__lowerCAmelCase					,							"tokenizer_config.json"       )					,							"w"       ) as f:
						json.dump(__lowerCAmelCase					,							__lowerCAmelCase       )

			with open(os.path.join(__lowerCAmelCase					,							MLukeTokenizer.vocab_files_names["entity_vocab_file"]       )					,							"w"       ) as f:
						json.dump(__lowerCAmelCase					,							__lowerCAmelCase       )

			lowerCAmelCase_      			=			MLukeTokenizer.from_pretrained(__lowerCAmelCase       )

			# Initialize the embeddings of the special tokens
			lowerCAmelCase_      			=			tokenizer.convert_tokens_to_ids(["@"]       )[0]
			lowerCAmelCase_      			=			tokenizer.convert_tokens_to_ids(["#"]       )[0]

			lowerCAmelCase_      			=			state_dict["embeddings.word_embeddings.weight"]
			lowerCAmelCase_      			=			word_emb[ent_init_index].unsqueeze(0       )
			lowerCAmelCase_      			=			word_emb[enta_init_index].unsqueeze(0       )
			lowerCAmelCase_      			=			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_      			=			state_dict[bias_name]
						lowerCAmelCase_      			=			decoder_bias[ent_init_index].unsqueeze(0       )
						lowerCAmelCase_      			=			decoder_bias[enta_init_index].unsqueeze(0       )
						lowerCAmelCase_      			=			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_      			=			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]"]].unsqueeze(0       )
			lowerCAmelCase_      			=			torch.cat([entity_emb, entity_mask_emb]       )
			# add [MASK2] for 'entity_predictions.bias'
			lowerCAmelCase_      			=			state_dict["entity_predictions.bias"]
			lowerCAmelCase_      			=			entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0       )
			lowerCAmelCase_      			=			torch.cat([entity_prediction_bias, entity_mask_bias]       )

			lowerCAmelCase_      			=			LukeForMaskedLM(config=__lowerCAmelCase       ).eval()

			state_dict.pop("entity_predictions.decoder.weight"       )
			state_dict.pop("lm_head.decoder.weight"       )
			state_dict.pop("lm_head.decoder.bias"       )
			lowerCAmelCase_      			=			OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith("lm_head"       ) or key.startswith("entity_predictions"       )):
									lowerCAmelCase_      			=			state_dict[key]
						else:
									lowerCAmelCase_      			=			state_dict[key]

			lowerCAmelCase_			,						lowerCAmelCase_      			=			model.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase       )

			if set(__lowerCAmelCase       ) != {"luke.embeddings.position_ids"}:
						raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""       )
			if set(__lowerCAmelCase       ) != {
			    "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_      			=			MLukeTokenizer.from_pretrained(__lowerCAmelCase					,							task="entity_classification"       )

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

			lowerCAmelCase_      			=			model(**__lowerCAmelCase       )

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						lowerCAmelCase_      			=			torch.Size((1, 33, 768)       )
						lowerCAmelCase_      			=			torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]       )

			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":
						raise NotImplementedError
			else:  # base
						lowerCAmelCase_      			=			torch.Size((1, 1, 768)       )
						lowerCAmelCase_      			=			torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]       )

			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

			# Verify masked word/entity prediction
			lowerCAmelCase_      			=			MLukeTokenizer.from_pretrained(__lowerCAmelCase       )
			lowerCAmelCase_      			=			"Tokyo is the capital of <mask>."
			lowerCAmelCase_      			=			(24, 30)
			lowerCAmelCase_      			=			tokenizer(__lowerCAmelCase					,							entity_spans=[span]					,							return_tensors="pt"       )

			lowerCAmelCase_      			=			model(**__lowerCAmelCase       )

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

			lowerCAmelCase_      			=			outputs.entity_logits[0][0].argmax().item()
			lowerCAmelCase_      			=			[
			    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(__lowerCAmelCase       )       )
			model.save_pretrained(__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			["[MASK]", "[PAD]", "[UNK]"]

			lowerCAmelCase_      			=			[json.loads(__lowerCAmelCase       ) for line in open(__lowerCAmelCase       )]

			lowerCAmelCase_      			=			{}
			for entry in data:
						lowerCAmelCase_      			=			entry["id"]
						for entity_name, language in entry["entities"]:
									if entity_name in SPECIAL_TOKENS:
												lowerCAmelCase_      			=			entity_id
												break
									lowerCAmelCase_      			=			F"""{language}:{entity_name}"""
									lowerCAmelCase_      			=			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,
			)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     =  {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



