






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 :int	,							a :List[Any]	,							a :Optional[Any]	,							a :Tuple	,							a :Dict )							->			List[str]:
 # 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  ,     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_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] )
 else:  # base
  a		  =      torch.Size((1, 42, 768) )
  a		  =      torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] )

 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_466, -0.0_106, -0.0_179]] )
 else:  # base
  a		  =      torch.Size((1, 1, 768) )
  a		  =      torch.tensor([[0.1_457, 0.1_044, 0.0_174]] )

 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 )							->			List[Any]:
 a		  =      {}
 with open(a	,							'''r'''	,							encoding='''utf-8''' ) as f:
  for index, line in enumerate(a ):
   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,
     )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NllbMoeForConditionalGeneration",
         "NllbMoeModel",
         "NllbMoePreTrainedModel",
         "NllbMoeTop2Router",
         "NllbMoeSparseMLP",
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

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


