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

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

  # Load the entity vocab file
  _lowerCAmelCase		: Tuple						    =  load_entity_vocab(__snake_case			)

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

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

  _lowerCAmelCase		: Dict						    =  LukeTokenizer.from_pretrained(__snake_case			)

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

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

  _lowerCAmelCase		: Union[str, Any]						    =  LukeModel(config=__snake_case			).eval()

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

  _lowerCAmelCase		: Tuple						    =  (
      """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		: Optional[Any]						    =  (39, 42)
  _lowerCAmelCase		: List[str]						    =  tokenizer(__snake_case    ,entity_spans=[span]    ,add_prefix_space=__snake_case    ,return_tensors="""pt"""			)

  _lowerCAmelCase		: Tuple						    =  model(**__snake_case			)

  # Verify word hidden states
  if model_size == "large":
    _lowerCAmelCase		: 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		: Union[str, Any]						    =  torch.Size((1, 42, 768)			)
    _lowerCAmelCase		: List[Any]						    =  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]    ,__snake_case    ,atol=1e-4			):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    _lowerCAmelCase		: Optional[int]						    =  torch.Size((1, 1, 1024)			)
    _lowerCAmelCase		: List[Any]						    =  torch.tensor([[0.04_66, -0.01_06, -0.01_79]]			)
  else:  # base
    _lowerCAmelCase		: Union[str, Any]						    =  torch.Size((1, 1, 768)			)
    _lowerCAmelCase		: Optional[int]						    =  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]    ,__snake_case    ,atol=1e-4			):
    raise ValueError

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





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

  return entity_vocab


if __name__ == "__main__":
     _a   :	Optional[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   :	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__						)




