"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Any	)       ->      int:
  # Load configuration defined in the metadata file
  with open(_lowerCamelCase	) as metadata_file:
    _lowerCAmelCase						:       Tuple  			=     json.load(_lowerCamelCase	)
  _lowerCAmelCase						:       Dict  			=     LukeConfig(use_entity_aware_attention=_lowerCamelCase			,**metadata["""model_config"""]	)

  # Load in the weights from the checkpoint_path
  _lowerCAmelCase						:       List[Any]  			=     torch.load(_lowerCamelCase			,map_location="""cpu"""	)

  # Load the entity vocab file
  _lowerCAmelCase						:       Optional[int]  			=     load_entity_vocab(_lowerCamelCase	)

  _lowerCAmelCase						:       List[Any]  			=     RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]	)

  # Add special tokens to the token vocabulary for downstream tasks
  _lowerCAmelCase						:       Optional[Any]  			=     AddedToken("""<ent>"""			,lstrip=_lowerCamelCase			,rstrip=_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     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	)

  _lowerCAmelCase						:       List[Any]  			=     LukeTokenizer.from_pretrained(_lowerCamelCase	)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  _lowerCAmelCase						:       Union[str, Any]  			=     state_dict["""entity_embeddings.entity_embeddings.weight"""]
  _lowerCAmelCase						:       Dict  			=     entity_emb[entity_vocab["""[MASK]"""]]

  _lowerCAmelCase						:       int  			=     LukeModel(config=_lowerCamelCase	).eval()

  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     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
  _lowerCAmelCase						:       List[Any]  			=     LukeTokenizer.from_pretrained(_lowerCamelCase			,task="""entity_classification"""	)

  _lowerCAmelCase						:       int  			=     (
      """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						:       Dict  			=     (39, 42)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(_lowerCamelCase			,entity_spans=[span]			,add_prefix_space=_lowerCamelCase			,return_tensors="""pt"""	)

  _lowerCAmelCase						:       List[str]  			=     model(**_lowerCamelCase	)

  # Verify word hidden states
  if model_size == "large":
    _lowerCAmelCase						:       List[str]  			=     torch.Size((1, 42, 1024)	)
    _lowerCAmelCase						:       List[str]  			=     torch.tensor(
        [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]	)
  else:  # base
    _lowerCAmelCase						:       int  			=     torch.Size((1, 42, 768)	)
    _lowerCAmelCase						:       Tuple  			=     torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]	)

  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":
    _lowerCAmelCase						:       Optional[int]  			=     torch.Size((1, 1, 1024)	)
    _lowerCAmelCase						:       Tuple  			=     torch.tensor([[0.04_66, -0.01_06, -0.01_79]]	)
  else:  # base
    _lowerCAmelCase						:       Optional[int]  			=     torch.Size((1, 1, 768)	)
    _lowerCAmelCase						:       Dict  			=     torch.tensor([[0.14_57, 0.10_44, 0.01_74]]	)

  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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Dict:
  _lowerCAmelCase						:       List[str]  			=     {}
  with open(_lowerCamelCase			,"""r"""			,encoding="""utf-8"""	) as f:
    for index, line in enumerate(_lowerCamelCase	):
      _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     line.rstrip().split("""\t"""	)
      _lowerCAmelCase						:       Any  			=     index

  return entity_vocab


if __name__ == "__main__":
     _a      :		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.'
     )
     _a      :		List[str]           =   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 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					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




