






'''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    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
      # Load configuration defined in the metadata file
      with open(lowercase__) as metadata_file:
            lowerCamelCase__  =     json.load(lowercase__)
      lowerCamelCase__  =     LukeConfig(use_entity_aware_attention=lowercase__				,     **metadata["model_config"])

      # Load in the weights from the checkpoint_path
      lowerCamelCase__  =     torch.load(lowercase__				,     map_location="cpu")

      # Load the entity vocab file
      lowerCamelCase__  =     load_entity_vocab(lowercase__)

      lowerCamelCase__  =     RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase__  =     AddedToken("<ent>"				,     lstrip=lowercase__				,     rstrip=lowercase__)
      lowerCamelCase__  =     AddedToken("<ent2>"				,     lstrip=lowercase__				,     rstrip=lowercase__)
      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(lowercase__)
      with open(os.path.join(lowercase__				,     LukeTokenizer.vocab_files_names["entity_vocab_file"])				,     "w") as f:
            json.dump(lowercase__				,     lowercase__)

      lowerCamelCase__  =     LukeTokenizer.from_pretrained(lowercase__)

      # 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=lowercase__).eval()

      lowerCamelCase__  ,	lowerCamelCase__  =     model.load_state_dict(lowercase__				,     strict=lowercase__)
      if not (len(lowercase__) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F'''Missing keys {", ".join(lowercase__)}. 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(lowercase__				,     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(lowercase__				,     entity_spans=[span]				,     add_prefix_space=lowercase__				,     return_tensors="pt")

      lowerCamelCase__  =     model(**lowercase__)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase__  =     torch.Size((1, 42, 1024))
            lowerCamelCase__  =     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
            lowerCamelCase__  =     torch.Size((1, 42, 768))
            lowerCamelCase__  =     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]				,     lowercase__				,     atol=1E-4):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase__  =     torch.Size((1, 1, 1024))
            lowerCamelCase__  =     torch.tensor([[0.0_466, -0.0_106, -0.0_179]])
      else:  # base
            lowerCamelCase__  =     torch.Size((1, 1, 768))
            lowerCamelCase__  =     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]				,     lowercase__				,     atol=1E-4):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(lowercase__))
      model.save_pretrained(lowercase__)






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

      return entity_vocab


if __name__ == "__main__":
 __A							:							List[str]  							=		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							:							Optional[Any]  							=		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 ..utils import DummyObject, requires_backends
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     List[str]				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     str				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     str				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Dict				,     *__lowerCamelCase     :     Any				,     **__lowerCamelCase     :     str     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     Dict				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     int				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     List[Any]				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     List[Any]     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )

