'''simple docstring'''







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       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  List[Any]				,						UpperCAmelCase__				:  List[Any]		)			->	Dict:


							'''simple docstring'''






							with open(UpperCAmelCase__		) as metadata_file:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = json.load(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = LukeConfig(use_entity_aware_attention=UpperCAmelCase__				,						**metadata['model_config']		)

							# Load in the weights from the checkpoint_path
							SCREAMING_SNAKE_CASE__     :List[str]						    = torch.load(UpperCAmelCase__				,						map_location='cpu'		)['module']

							# Load the entity vocab file
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = load_original_entity_vocab(UpperCAmelCase__		)
							# add an entry for [MASK2]
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(entity_vocab.values()		) + 1
							config.entity_vocab_size += 1

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']		)

							# Add special tokens to the token vocabulary for downstream tasks
							SCREAMING_SNAKE_CASE__     :List[Any]						    = AddedToken('<ent>'				,						lstrip=UpperCAmelCase__				,						rstrip=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = AddedToken('<ent2>'				,						lstrip=UpperCAmelCase__				,						rstrip=UpperCAmelCase__		)
							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(UpperCAmelCase__		)
							with open(os.path.join(UpperCAmelCase__				,						'tokenizer_config.json'		)				,						'r'		) as f:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = json.load(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Any						    = 'MLukeTokenizer'
							with open(os.path.join(UpperCAmelCase__				,						'tokenizer_config.json'		)				,						'w'		) as f:
														json.dump(UpperCAmelCase__				,						UpperCAmelCase__		)

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

							SCREAMING_SNAKE_CASE__     :Dict						    = MLukeTokenizer.from_pretrained(UpperCAmelCase__		)

							# Initialize the embeddings of the special tokens
							SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer.convert_tokens_to_ids(['@']		)[0]
							SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer.convert_tokens_to_ids(['#']		)[0]

							SCREAMING_SNAKE_CASE__     :List[str]						    = state_dict['embeddings.word_embeddings.weight']
							SCREAMING_SNAKE_CASE__     :int						    = word_emb[ent_init_index].unsqueeze(0		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = word_emb[enta_init_index].unsqueeze(0		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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"]:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = state_dict[bias_name]
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = decoder_bias[ent_init_index].unsqueeze(0		)
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = decoder_bias[enta_init_index].unsqueeze(0		)
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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"]:
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = F'''encoder.layer.{layer_index}.attention.self.'''
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__     :Dict						    = state_dict[prefix + matrix_name]
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = state_dict['entity_embeddings.entity_embeddings.weight']
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = entity_emb[entity_vocab['[MASK]']].unsqueeze(0		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = torch.cat([entity_emb, entity_mask_emb]		)
							# add [MASK2] for 'entity_predictions.bias'
							SCREAMING_SNAKE_CASE__     :str						    = state_dict['entity_predictions.bias']
							SCREAMING_SNAKE_CASE__     :Any						    = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0		)
							SCREAMING_SNAKE_CASE__     :Any						    = torch.cat([entity_prediction_bias, entity_mask_bias]		)

							SCREAMING_SNAKE_CASE__     :List[Any]						    = LukeForMaskedLM(config=UpperCAmelCase__		).eval()

							state_dict.pop('entity_predictions.decoder.weight'		)
							state_dict.pop('lm_head.decoder.weight'		)
							state_dict.pop('lm_head.decoder.bias'		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = OrderedDict()
							for key, value in state_dict.items():
														if not (key.startswith('lm_head'		) or key.startswith('entity_predictions'		)):
																					SCREAMING_SNAKE_CASE__     :int						    = state_dict[key]
														else:
																					SCREAMING_SNAKE_CASE__     :Dict						    = state_dict[key]

							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Tuple						    = model.load_state_dict(UpperCAmelCase__				,						strict=UpperCAmelCase__		)

							if set(UpperCAmelCase__		) != {"luke.embeddings.position_ids"}:
														raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}'''		)
							if set(UpperCAmelCase__		) != {
							    "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
							SCREAMING_SNAKE_CASE__     :List[str]						    = MLukeTokenizer.from_pretrained(UpperCAmelCase__				,						task='entity_classification'		)

							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
							SCREAMING_SNAKE_CASE__     :Any						    = (0, 9)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer(UpperCAmelCase__				,						entity_spans=[span]				,						return_tensors='pt'		)

							SCREAMING_SNAKE_CASE__     :int						    = model(**UpperCAmelCase__		)

							# Verify word hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														SCREAMING_SNAKE_CASE__     :List[str]						    = torch.Size((1, 3_3, 7_6_8)		)
														SCREAMING_SNAKE_CASE__     :Dict						    = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]		)

							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]				,						UpperCAmelCase__				,						atol=1e-4		):
														raise ValueError

							# Verify entity hidden states
							if model_size == "large":
														raise NotImplementedError
							else:  # base
														SCREAMING_SNAKE_CASE__     :Dict						    = torch.Size((1, 1, 7_6_8)		)
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.tensor([[-0.1482, 0.0609, 0.0322]]		)

							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]				,						UpperCAmelCase__				,						atol=1e-4		):
														raise ValueError

							# Verify masked word/entity prediction
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = MLukeTokenizer.from_pretrained(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 'Tokyo is the capital of <mask>.'
							SCREAMING_SNAKE_CASE__     :List[Any]						    = (2_4, 3_0)
							SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer(UpperCAmelCase__				,						entity_spans=[span]				,						return_tensors='pt'		)

							SCREAMING_SNAKE_CASE__     :List[Any]						    = model(**UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Tuple						    = encoding['input_ids'][0].tolist()
							SCREAMING_SNAKE_CASE__     :Dict						    = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'		)		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = outputs.logits[0][mask_position_id].argmax(dim=-1		)
							assert "Japan" == tokenizer.decode(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :str						    = outputs.entity_logits[0][0].argmax().item()
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = [
							    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(UpperCAmelCase__		)		)
							model.save_pretrained(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple		)			->	Optional[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = ['[MASK]', '[PAD]', '[UNK]']

							SCREAMING_SNAKE_CASE__     :str						    = [json.loads(UpperCAmelCase__		) for line in open(UpperCAmelCase__		)]

							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {}
							for entry in data:
														SCREAMING_SNAKE_CASE__     :Dict						    = entry['id']
														for entity_name, language in entry["entities"]:
																					if entity_name in SPECIAL_TOKENS:
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = entity_id
																												break
																					SCREAMING_SNAKE_CASE__     :Dict						    = F'''{language}:{entity_name}'''
																					SCREAMING_SNAKE_CASE__     :Tuple						    = entity_id
							return new_mapping


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'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
    '''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''M2M100ForConditionalGeneration''',
		    '''M2M100Model''',
		    '''M2M100PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






