"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[str]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: Union[str, Any]			) ->   Union[str, Any]:
  # Load configuration defined in the metadata file
  with open(_lowerCamelCase			) as metadata_file:
    _lowerCAmelCase		: List[str]						    =  json.load(_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  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"""			)["""module"""]

  # Load the entity vocab file
  _lowerCAmelCase		: Any						    =  load_original_entity_vocab(_lowerCamelCase			)
  # add an entry for [MASK2]
  _lowerCAmelCase		: Optional[Any]						    =  max(entity_vocab.values()			) + 1
  config.entity_vocab_size += 1

  _lowerCAmelCase		: Dict						    =  XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]			)

  # Add special tokens to the token vocabulary for downstream tasks
  _lowerCAmelCase		: Optional[int]						    =  AddedToken("""<ent>"""    ,lstrip=_lowerCamelCase    ,rstrip=_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  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    ,"""tokenizer_config.json"""			)    ,"""r"""			) as f:
    _lowerCAmelCase		: int						    =  json.load(_lowerCamelCase			)
  _lowerCAmelCase		: Dict						    =  """MLukeTokenizer"""
  with open(os.path.join(_lowerCamelCase    ,"""tokenizer_config.json"""			)    ,"""w"""			) as f:
    json.dump(_lowerCamelCase    ,_lowerCamelCase			)

  with open(os.path.join(_lowerCamelCase    ,MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]			)    ,"""w"""			) as f:
    json.dump(_lowerCamelCase    ,_lowerCamelCase			)

  _lowerCAmelCase		: Dict						    =  MLukeTokenizer.from_pretrained(_lowerCamelCase			)

  # Initialize the embeddings of the special tokens
  _lowerCAmelCase		: int						    =  tokenizer.convert_tokens_to_ids(["""@"""]			)[0]
  _lowerCAmelCase		: int						    =  tokenizer.convert_tokens_to_ids(["""#"""]			)[0]

  _lowerCAmelCase		: Any						    =  state_dict["""embeddings.word_embeddings.weight"""]
  _lowerCAmelCase		: str						    =  word_emb[ent_init_index].unsqueeze(0			)
  _lowerCAmelCase		: Optional[Any]						    =  word_emb[enta_init_index].unsqueeze(0			)
  _lowerCAmelCase		: Dict						    =  torch.cat([word_emb, ent_emb, enta_emb]			)
  # add special tokens for 'entity_predictions.bias'
  for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
    _lowerCAmelCase		: Tuple						    =  state_dict[bias_name]
    _lowerCAmelCase		: Tuple						    =  decoder_bias[ent_init_index].unsqueeze(0			)
    _lowerCAmelCase		: int						    =  decoder_bias[enta_init_index].unsqueeze(0			)
    _lowerCAmelCase		: Optional[Any]						    =  torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

  # 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		: Dict						    =  state_dict[prefix + matrix_name]
      _lowerCAmelCase		: int						    =  state_dict[prefix + matrix_name]
      _lowerCAmelCase		: int						    =  state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  _lowerCAmelCase		: str						    =  state_dict["""entity_embeddings.entity_embeddings.weight"""]
  _lowerCAmelCase		: Optional[int]						    =  entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0			)
  _lowerCAmelCase		: Any						    =  torch.cat([entity_emb, entity_mask_emb]			)
  # add [MASK2] for 'entity_predictions.bias'
  _lowerCAmelCase		: List[str]						    =  state_dict["""entity_predictions.bias"""]
  _lowerCAmelCase		: List[str]						    =  entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0			)
  _lowerCAmelCase		: Dict						    =  torch.cat([entity_prediction_bias, entity_mask_bias]			)

  _lowerCAmelCase		: Optional[int]						    =  LukeForMaskedLM(config=_lowerCamelCase			).eval()

  state_dict.pop("""entity_predictions.decoder.weight"""			)
  state_dict.pop("""lm_head.decoder.weight"""			)
  state_dict.pop("""lm_head.decoder.bias"""			)
  _lowerCAmelCase		: List[Any]						    =  OrderedDict()
  for key, value in state_dict.items():
    if not (key.startswith("""lm_head"""			) or key.startswith("""entity_predictions"""			)):
      _lowerCAmelCase		: Optional[Any]						    =  state_dict[key]
    else:
      _lowerCAmelCase		: Dict						    =  state_dict[key]

  _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model.load_state_dict(_lowerCamelCase    ,strict=_lowerCamelCase			)

  if set(_lowerCamelCase			) != {"luke.embeddings.position_ids"}:
    raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}"			)
  if set(_lowerCamelCase			) != {
      "lm_head.decoder.weight",
      "lm_head.decoder.bias",
      "entity_predictions.decoder.weight",
  }:
    raise ValueError(f"Unexpected missing_keys: {missing_keys}"			)

  model.tie_weights()
  assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
  assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

  # Check outputs
  _lowerCAmelCase		: Union[str, Any]						    =  MLukeTokenizer.from_pretrained(_lowerCamelCase    ,task="""entity_classification"""			)

  _lowerCAmelCase		: Union[str, Any]						    =  """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
  _lowerCAmelCase		: int						    =  (0, 9)
  _lowerCAmelCase		: Union[str, Any]						    =  tokenizer(_lowerCamelCase    ,entity_spans=[span]    ,return_tensors="""pt"""			)

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

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    _lowerCAmelCase		: Tuple						    =  torch.Size((1, 33, 768)			)
    _lowerCAmelCase		: List[str]						    =  torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]]			)

  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":
    raise NotImplementedError
  else:  # base
    _lowerCAmelCase		: List[Any]						    =  torch.Size((1, 1, 768)			)
    _lowerCAmelCase		: str						    =  torch.tensor([[-0.14_82, 0.06_09, 0.03_22]]			)

  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

  # Verify masked word/entity prediction
  _lowerCAmelCase		: int						    =  MLukeTokenizer.from_pretrained(_lowerCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  """Tokyo is the capital of <mask>."""
  _lowerCAmelCase		: Tuple						    =  (24, 30)
  _lowerCAmelCase		: Any						    =  tokenizer(_lowerCamelCase    ,entity_spans=[span]    ,return_tensors="""pt"""			)

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

  _lowerCAmelCase		: Tuple						    =  encoding["""input_ids"""][0].tolist()
  _lowerCAmelCase		: Any						    =  input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""			)			)
  _lowerCAmelCase		: str						    =  outputs.logits[0][mask_position_id].argmax(dim=-1			)
  assert "Japan" == tokenizer.decode(_lowerCamelCase			)

  _lowerCAmelCase		: str						    =  outputs.entity_logits[0][0].argmax().item()
  _lowerCAmelCase		: Tuple						    =  [
      entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
  ]
  assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""			)][0] == "en:Japan"

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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Tuple:
  _lowerCAmelCase		: Optional[Any]						    =  ["""[MASK]""", """[PAD]""", """[UNK]"""]

  _lowerCAmelCase		: Any						    =  [json.loads(_lowerCamelCase			) for line in open(_lowerCamelCase			)]

  _lowerCAmelCase		: List[Any]						    =  {}
  for entry in data:
    _lowerCAmelCase		: Dict						    =  entry["""id"""]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        _lowerCAmelCase		: Optional[int]						    =  entity_id
        break
      _lowerCAmelCase		: Optional[Any]						    =  f"{language}:{entity_name}"
      _lowerCAmelCase		: Optional[Any]						    =  entity_id
  return new_mapping


if __name__ == "__main__":
     _a   :	Union[str, Any] 					=     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   :	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,
     )







"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    str	      =	CpmAntTokenizer
     _UpperCamelCase							:    List[Any]	      =	False






     def 					__A						(	self						):
       super().setUp()

       _lowerCAmelCase		: Dict						    =  [
           """<d>""",
           """</d>""",
           """<s>""",
           """</s>""",
           """</_>""",
           """<unk>""",
           """<pad>""",
           """</n>""",
           """我""",
           """是""",
           """C""",
           """P""",
           """M""",
           """A""",
           """n""",
           """t""",
       ]
       _lowerCAmelCase		: int						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as vocab_writer:
         vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)






     @tooslow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""						)
       _lowerCAmelCase		: Optional[Any]						    =  """今天天气真好！"""
       _lowerCAmelCase		: Any						    =  ["""今天""", """天气""", """真""", """好""", """！"""]
       _lowerCAmelCase		: str						    =  tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  """今天天气真好！"""
       _lowerCAmelCase		: Optional[Any]						    =  [tokenizer.bos_token] + tokens

       _lowerCAmelCase		: Optional[int]						    =  [6, 9802, 14962, 2082, 831, 244]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokenizer.decode(a__						)
       self.assertEqual(a__       ,  a__						)




