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  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	List[Any]			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	List[str]     )							-> Optional[Any]:







  """simple docstring"""





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

  # Load in the weights from the checkpoint_path
  SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase			,					map_location="""cpu"""     )["""module"""]

  # Load the entity vocab file
  SCREAMING_SNAKE_CASE__      							=				load_original_entity_vocab(__UpperCamelCase     )
  # add an entry for [MASK2]
  SCREAMING_SNAKE_CASE__      							=				max(entity_vocab.values()     ) + 1
  config.entity_vocab_size += 1

  SCREAMING_SNAKE_CASE__      							=				XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]     )

  # Add special tokens to the token vocabulary for downstream tasks
  SCREAMING_SNAKE_CASE__      							=				AddedToken("""<ent>"""			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				AddedToken("""<ent2>"""			,					lstrip=__UpperCamelCase			,					rstrip=__UpperCamelCase     )
  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(__UpperCamelCase     )
  with open(os.path.join(__UpperCamelCase			,					"""tokenizer_config.json"""     )			,					"""r"""     ) as f:
    SCREAMING_SNAKE_CASE__      							=				json.load(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				"""MLukeTokenizer"""
  with open(os.path.join(__UpperCamelCase			,					"""tokenizer_config.json"""     )			,					"""w"""     ) as f:
    json.dump(__UpperCamelCase			,					__UpperCamelCase     )

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

  SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase     )

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

  SCREAMING_SNAKE_CASE__      							=				state_dict["""embeddings.word_embeddings.weight"""]
  SCREAMING_SNAKE_CASE__      							=				word_emb[ent_init_index].unsqueeze(0     )
  SCREAMING_SNAKE_CASE__      							=				word_emb[enta_init_index].unsqueeze(0     )
  SCREAMING_SNAKE_CASE__      							=				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"]:
    SCREAMING_SNAKE_CASE__      							=				state_dict[bias_name]
    SCREAMING_SNAKE_CASE__      							=				decoder_bias[ent_init_index].unsqueeze(0     )
    SCREAMING_SNAKE_CASE__      							=				decoder_bias[enta_init_index].unsqueeze(0     )
    SCREAMING_SNAKE_CASE__      							=				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"]:
      SCREAMING_SNAKE_CASE__      							=				f"""encoder.layer.{layer_index}.attention.self."""
      SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]
      SCREAMING_SNAKE_CASE__      							=				state_dict[prefix + matrix_name]

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

  SCREAMING_SNAKE_CASE__      							=				LukeForMaskedLM(config=__UpperCamelCase     ).eval()

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

  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				model.load_state_dict(__UpperCamelCase			,					strict=__UpperCamelCase     )

  if set(__UpperCamelCase     ) != {"luke.embeddings.position_ids"}:
    raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""     )
  if set(__UpperCamelCase     ) != {
      "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
  SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase			,					task="""entity_classification"""     )

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

  SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 33, 7_68)     )
    SCREAMING_SNAKE_CASE__      							=				torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]     )

  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]			,					__UpperCamelCase			,					atol=1E-4     ):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    SCREAMING_SNAKE_CASE__      							=				torch.Size((1, 1, 7_68)     )
    SCREAMING_SNAKE_CASE__      							=				torch.tensor([[-0.1482, 0.0609, 0.0322]]     )

  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]			,					__UpperCamelCase			,					atol=1E-4     ):
    raise ValueError

  # Verify masked word/entity prediction
  SCREAMING_SNAKE_CASE__      							=				MLukeTokenizer.from_pretrained(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				"""Tokyo is the capital of <mask>."""
  SCREAMING_SNAKE_CASE__      							=				(24, 30)
  SCREAMING_SNAKE_CASE__      							=				tokenizer(__UpperCamelCase			,					entity_spans=[span]			,					return_tensors="""pt"""     )

  SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				encoding["""input_ids"""][0].tolist()
  SCREAMING_SNAKE_CASE__      							=				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""     )     )
  SCREAMING_SNAKE_CASE__      							=				outputs.logits[0][mask_position_id].argmax(dim=-1     )
  assert "Japan" == tokenizer.decode(__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				outputs.entity_logits[0][0].argmax().item()
  SCREAMING_SNAKE_CASE__      							=				[
      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(__UpperCamelCase     )     )
  model.save_pretrained(__UpperCamelCase     )



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				["""[MASK]""", """[PAD]""", """[UNK]"""]

  SCREAMING_SNAKE_CASE__      							=				[json.loads(__UpperCamelCase     ) for line in open(__UpperCamelCase     )]

  SCREAMING_SNAKE_CASE__      							=				{}
  for entry in data:
    SCREAMING_SNAKE_CASE__      							=				entry["""id"""]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        SCREAMING_SNAKE_CASE__      							=				entity_id
        break
      SCREAMING_SNAKE_CASE__      							=				f"""{language}:{entity_name}"""
      SCREAMING_SNAKE_CASE__      							=				entity_id
  return new_mapping


if __name__ == "__main__":
   __lowerCamelCase      :			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.'''
   )
   __lowerCamelCase      :			int							       =		parser.parse_args()
   convert_luke_checkpoint(
       args.checkpoint_path,
       args.metadata_path,
       args.entity_vocab_path,
       args.pytorch_dump_folder_path,
       args.model_size,
   )

import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     self.assertEqual(x.component(0				)	,     1				)
     self.assertEqual(x.component(2				)	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Vector()




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([0, 0, 0, 0, 0, 1]				)
     self.assertEqual(str(_lowercase				)	,     """(0,0,0,0,0,1)"""				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3, 4]				)
     self.assertEqual(len(_lowercase				)	,     4				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3, 4, 5]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, -1, 1, -1, 2, -3, 4, -5]				)
     self.assertAlmostEqual(x.euclidean_length()	,     2.2_36	,     3				)
     self.assertAlmostEqual(y.euclidean_length()	,     7.4_16	,     3				)
     self.assertEqual(z.euclidean_length()	,     0				)
     self.assertAlmostEqual(w.euclidean_length()	,     7.6_16	,     3				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 1, 1]				)
     self.assertEqual((x + y).component(0				)	,     2				)
     self.assertEqual((x + y).component(1				)	,     3				)
     self.assertEqual((x + y).component(2				)	,     4				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 1, 1]				)
     self.assertEqual((x - y).component(0				)	,     0				)
     self.assertEqual((x - y).component(1				)	,     1				)
     self.assertEqual((x - y).component(2				)	,     2				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([2, -1, 4]				)  # for test of dot product
     SCREAMING_SNAKE_CASE__      							=				Vector([1, -2, -1]				)
     self.assertEqual(str(x * 3.0				)	,     """(3.0,6.0,9.0)"""				)
     self.assertEqual((a * b)	,     0				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(str(zero_vector(10				)				).count("""0"""				)	,     10				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     self.assertEqual(str(unit_basis_vector(3	,     1				)				)	,     """(0,1,0)"""				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 1]				)
     self.assertEqual(str(axpy(2	,     _lowercase	,     _lowercase				)				)	,     """(3,4,7)"""				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 0, 0, 0, 0]				)
     SCREAMING_SNAKE_CASE__      							=				x.copy()
     self.assertEqual(str(_lowercase				)	,     str(_lowercase				)				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 0]				)
     x.change_component(0	,     0				)
     x.change_component(1	,     1				)
     self.assertEqual(str(_lowercase				)	,     """(0,1,0)"""				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""	,     str(_lowercase				)				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
     for x in range(a.height()				):
       for y in range(a.width()				):
         self.assertEqual(minors[x][y]	,     a.minor(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
     for x in range(a.height()				):
       for y in range(a.width()				):
         self.assertEqual(cofactors[x][y]	,     a.cofactor(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual(-5	,     a.determinant()				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     self.assertEqual("""(14,32,50)"""	,     str(a * x				)				)
     self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""	,     str(a * 2				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     a.change_component(0	,     2	,     5				)
     self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""	,     str(_lowercase				)				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual(7	,     a.component(2	,     1				)	,     0.01				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]	,     3	,     3				)
     self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""	,     str(a + b				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]	,     3	,     3				)
     self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""	,     str(a - b				)				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     self.assertEqual(
         """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""	,     str(square_zero_matrix(5				)				)	,     )


if __name__ == "__main__":
   unittest.main()
