


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    (				lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Dict				)->List[str]:
   '''simple docstring'''






   with open(lowercase__				) as metadata_file:
      snake_case_            =       json.load(lowercase__				)
   snake_case_            =       LukeConfig(use_entity_aware_attention=lowercase__		,    **metadata["model_config"]				)

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

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

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

   # Add special tokens to the token vocabulary for downstream tasks
   snake_case_            =       AddedToken("<ent>"		,    lstrip=lowercase__		,    rstrip=lowercase__				)
   snake_case_            =       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__				)

   snake_case_            =       LukeTokenizer.from_pretrained(lowercase__				)

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

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

   snake_case_            =       LukeModel(config=lowercase__				).eval()

   snake_case_     ,     snake_case_            =       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
   snake_case_            =       LukeTokenizer.from_pretrained(lowercase__		,    task="entity_classification"				)

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

   snake_case_            =       model(**lowercase__				)

   # Verify word hidden states
   if model_size == "large":
      snake_case_            =       torch.Size((1, 42, 1_024)				)
      snake_case_            =       torch.tensor(
          [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]				)
   else:  # base
      snake_case_            =       torch.Size((1, 42, 768)				)
      snake_case_            =       torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]				)

   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":
      snake_case_            =       torch.Size((1, 1, 1_024)				)
      snake_case_            =       torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]				)
   else:  # base
      snake_case_            =       torch.Size((1, 1, 768)				)
      snake_case_            =       torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]				)

   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    (				lowerCAmelCase_				:List[str]				)->Union[str, Any]:
   '''simple docstring'''






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

   return entity_vocab


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:Optional[int]			=  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.'''
   )
   SCREAMING_SNAKE_CASE						:Union[str, 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,
   )







A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
