



"""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        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase__				) as metadata_file:
            UpperCAmelCase_      			=							json.load(lowerCAmelCase__				)
      UpperCAmelCase_      			=							LukeConfig(use_entity_aware_attention=lowerCAmelCase__     ,      **metadata["model_config"]				)

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

      # Load the entity vocab file
      UpperCAmelCase_      			=							load_entity_vocab(lowerCAmelCase__				)

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

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCAmelCase_      			=							AddedToken("<ent>"     ,      lstrip=lowerCAmelCase__     ,      rstrip=lowerCAmelCase__				)
      UpperCAmelCase_      			=							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__     ,      LukeTokenizer.vocab_files_names["entity_vocab_file"]				)     ,      "w"				) as f:
            json.dump(lowerCAmelCase__     ,      lowerCAmelCase__				)

      UpperCAmelCase_      			=							LukeTokenizer.from_pretrained(lowerCAmelCase__				)

      # Initialize the embeddings of the special tokens
      UpperCAmelCase_      			=							state_dict["embeddings.word_embeddings.weight"]
      UpperCAmelCase_      			=							word_emb[tokenizer.convert_tokens_to_ids(["@"]				)[0]].unsqueeze(0				)
      UpperCAmelCase_      			=							word_emb[tokenizer.convert_tokens_to_ids(["#"]				)[0]].unsqueeze(0				)
      UpperCAmelCase_      			=							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"]:
                  UpperCAmelCase_      			=							f"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase_      			=							state_dict[prefix + matrix_name]
                  UpperCAmelCase_      			=							state_dict[prefix + matrix_name]
                  UpperCAmelCase_      			=							state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase_      			=							state_dict["entity_embeddings.entity_embeddings.weight"]
      UpperCAmelCase_      			=							entity_emb[entity_vocab["[MASK]"]]

      UpperCAmelCase_      			=							LukeModel(config=lowerCAmelCase__				).eval()

      UpperCAmelCase_			,   UpperCAmelCase_      			=							model.load_state_dict(lowerCAmelCase__     ,      strict=lowerCAmelCase__				)
      if not (len(lowerCAmelCase__				) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(f"""Missing keys {', '.join(lowerCAmelCase__				)}. 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
      UpperCAmelCase_      			=							LukeTokenizer.from_pretrained(lowerCAmelCase__     ,      task="entity_classification"				)

      UpperCAmelCase_      			=							(
          "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 ."
      )
      UpperCAmelCase_      			=							(39, 42)
      UpperCAmelCase_      			=							tokenizer(lowerCAmelCase__     ,      entity_spans=[span]     ,      add_prefix_space=lowerCAmelCase__     ,      return_tensors="pt"				)

      UpperCAmelCase_      			=							model(**lowerCAmelCase__				)

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase_      			=							torch.Size((1, 42, 1024)				)
            UpperCAmelCase_      			=							torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]				)
      else:  # base
            UpperCAmelCase_      			=							torch.Size((1, 42, 768)				)
            UpperCAmelCase_      			=							torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]				)

      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":
            UpperCAmelCase_      			=							torch.Size((1, 1, 1024)				)
            UpperCAmelCase_      			=							torch.tensor([[0.0466, -0.0106, -0.0179]]				)
      else:  # base
            UpperCAmelCase_      			=							torch.Size((1, 1, 768)				)
            UpperCAmelCase_      			=							torch.tensor([[0.1457, 0.1044, 0.0174]]				)

      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

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(lowerCAmelCase__				)				)
      model.save_pretrained(lowerCAmelCase__				)
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							{}
      with open(lowerCAmelCase__     ,      "r"     ,      encoding="utf-8"				) as f:
            for index, line in enumerate(lowerCAmelCase__				):
                  UpperCAmelCase_			,   UpperCAmelCase_      			=							line.rstrip().split("\t"				)
                  UpperCAmelCase_      			=							index

      return entity_vocab


if __name__ == "__main__":
  lowerCamelCase       	=						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."""
  )
  lowerCamelCase       	=						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''roberta'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]=50265		,    _UpperCAmelCase      :  str=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  int=12		,    _UpperCAmelCase      :  Tuple=3072		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  Optional[int]=512		,    _UpperCAmelCase      :  Optional[Any]=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Optional[Any]=1e-12		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  int=2		,    _UpperCAmelCase      :  Tuple="absolute"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  int=None		,    **_UpperCAmelCase      :  List[str]		,    )			->						Any:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )

             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							position_embedding_type
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  List[Any]   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_      			=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_      			=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]   )




