

'''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    _A						(      snake_case						,				snake_case						,				snake_case						,				snake_case						,				snake_case	)     ->      Tuple:




   with open(_UpperCamelCase	) as metadata_file:
      _lowercase     : Optional[int]     	=  json.load(_UpperCamelCase	)
   _lowercase     : List[Any]     	=  LukeConfig(use_entity_aware_attention=_UpperCamelCase						,				**metadata["model_config"]	)

   # Load in the weights from the checkpoint_path
   _lowercase     : Optional[int]     	=  torch.load(_UpperCamelCase						,				map_location="cpu"	)["module"]

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

   _lowercase     : Optional[int]     	=  XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]	)

   # Add special tokens to the token vocabulary for downstream tasks
   _lowercase     : Optional[int]     	=  AddedToken("<ent>"						,				lstrip=_UpperCamelCase						,				rstrip=_UpperCamelCase	)
   _lowercase     : Any     	=  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:
      _lowercase     : Any     	=  json.load(_UpperCamelCase	)
   _lowercase     : List[str]     	=  "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	)

   _lowercase     : Any     	=  MLukeTokenizer.from_pretrained(_UpperCamelCase	)

   # Initialize the embeddings of the special tokens
   _lowercase     : Optional[int]     	=  tokenizer.convert_tokens_to_ids(["@"]	)[0]
   _lowercase     : Any     	=  tokenizer.convert_tokens_to_ids(["#"]	)[0]

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

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

   _lowercase     : List[str]     	=  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"	)
   _lowercase     : Optional[int]     	=  OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith("lm_head"	) or key.startswith("entity_predictions"	)):
         _lowercase     : List[str]     	=  state_dict[key]
      else:
         _lowercase     : Dict     	=  state_dict[key]

   _lowercase	, _lowercase     : Optional[Any]     	=  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
   _lowercase     : Any     	=  MLukeTokenizer.from_pretrained(_UpperCamelCase						,				task="entity_classification"	)

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

   _lowercase     : Optional[int]     	=  model(**_UpperCamelCase	)

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      _lowercase     : Dict     	=  torch.Size((1, 33, 7_68)	)
      _lowercase     : str     	=  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
      _lowercase     : Tuple     	=  torch.Size((1, 1, 7_68)	)
      _lowercase     : Optional[int]     	=  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
   _lowercase     : Union[str, Any]     	=  MLukeTokenizer.from_pretrained(_UpperCamelCase	)
   _lowercase     : str     	=  "Tokyo is the capital of <mask>."
   _lowercase     : str     	=  (24, 30)
   _lowercase     : Union[str, Any]     	=  tokenizer(_UpperCamelCase						,				entity_spans=[span]						,				return_tensors="pt"	)

   _lowercase     : Union[str, Any]     	=  model(**_UpperCamelCase	)

   _lowercase     : int     	=  encoding["input_ids"][0].tolist()
   _lowercase     : Any     	=  input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"	)	)
   _lowercase     : Optional[Any]     	=  outputs.logits[0][mask_position_id].argmax(dim=-1	)
   assert "Japan" == tokenizer.decode(_UpperCamelCase	)

   _lowercase     : Dict     	=  outputs.entity_logits[0][0].argmax().item()
   _lowercase     : str     	=  [
       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    _A						(      snake_case	)     ->      Tuple:
   _lowercase     : Union[str, Any]     	=  ["[MASK]", "[PAD]", "[UNK]"]

   _lowercase     : Optional[Any]     	=  [json.loads(_UpperCamelCase	) for line in open(_UpperCamelCase	)]

   _lowercase     : int     	=  {}
   for entry in data:
      _lowercase     : List[str]     	=  entry["id"]
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            _lowercase     : Dict     	=  entity_id
            break
         _lowercase     : str     	=  F'''{language}:{entity_name}'''
         _lowercase     : Optional[int]     	=  entity_id
   return new_mapping


if __name__ == "__main__":
    _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.'
    )
    _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'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_		:     int          =		logging.get_logger(__name__)

UpperCamelCase_		:     Any          =		{
    '''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
    '''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""falcon"""
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		["""past_key_values"""]






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=65_024   ,_SCREAMING_SNAKE_CASE=4_544   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=71   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=11   ,_SCREAMING_SNAKE_CASE=11   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          _snake_case				= vocab_size
          # Backward compatibility with n_embed kwarg
          _snake_case				= kwargs.pop("n_embed"   ,_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size if n_embed is None else n_embed
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= layer_norm_epsilon
          _snake_case				= initializer_range
          _snake_case				= use_cache
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout

          _snake_case				= bos_token_id
          _snake_case				= eos_token_id
          _snake_case				= num_attention_heads if num_kv_heads is None else num_kv_heads
          _snake_case				= alibi
          _snake_case				= new_decoder_architecture
          _snake_case				= multi_query  # Ignored when new_decoder_architecture is True
          _snake_case				= parallel_attn
          _snake_case				= bias

          super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     Union[str, Any]:
          return self.hidden_size // self.num_attention_heads






     @property
     def 							_lowercase      (					self       )				->     Optional[int]:
          return not self.alibi


