

'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__   		=      get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCAmelCase__   		=      25_0004
lowerCAmelCase__   		=      25_0020






@require_sentencepiece
@require_tokenizers
class      lowercase_      (UpperCAmelCase_							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Tuple							     =     MBartaaTokenizer
  SCREAMING_SNAKE_CASE       :    str							     =     MBartaaTokenizerFast
  SCREAMING_SNAKE_CASE       :    str							     =     True
  SCREAMING_SNAKE_CASE       :    Dict							     =     True






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowercase				    =					MBartaaTokenizer(__a   ,src_lang='''en_XX'''   ,tgt_lang='''ro_RO'''   ,keep_accents=__a     )
   tokenizer.save_pretrained(self.tmpdirname     )






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					'''<s>'''
   __lowercase				    =					0

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a     )   ,__a     )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a     )   ,__a     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					list(self.get_tokenizer().get_vocab().keys()     )

   self.assertEqual(vocab_keys[0]   ,'''<s>'''     )
   self.assertEqual(vocab_keys[1]   ,'''<pad>'''     )
   self.assertEqual(vocab_keys[-1]   ,'''<mask>'''     )
   self.assertEqual(len(__a     )   ,1_0_5_4     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   self.assertEqual(self.get_tokenizer().vocab_size   ,1_0_5_4     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					MBartaaTokenizer(__a   ,src_lang='''en_XX'''   ,tgt_lang='''ro_RO'''   ,keep_accents=__a     )

   __lowercase				    =					tokenizer.tokenize('''This is a test'''     )
   self.assertListEqual(__a   ,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(__a     )   ,[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]   ,)

   __lowercase				    =					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
   self.assertListEqual(
       __a   ,[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']   ,)
   __lowercase				    =					tokenizer.convert_tokens_to_ids(__a     )
   self.assertListEqual(
       __a   ,[
           value + tokenizer.fairseq_offset
           for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
       ]   ,)

   __lowercase				    =					tokenizer.convert_ids_to_tokens(__a     )
   self.assertListEqual(
       __a   ,[SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']   ,)






  @slow
  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=__a   ,model_name='''facebook/mbart-large-50'''   ,revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):



   if not self.test_slow_tokenizer:
    # as we don't have a slow version, we can't compare the outputs between slow and fast versions
    return

   __lowercase				    =					(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"     ):
     __lowercase				    =					self.rust_tokenizer_class.from_pretrained(__a   ,**__a     )
     __lowercase				    =					self.tokenizer_class.from_pretrained(__a   ,**__a     )

     __lowercase				    =					tempfile.mkdtemp()

     __lowercase				    =					tokenizer_r.save_pretrained(__a     )
     __lowercase				    =					tokenizer_p.save_pretrained(__a     )

     # Checks it save with the same files + the tokenizer.json file for the fast one
     self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )
     __lowercase				    =					tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f     )
     self.assertSequenceEqual(__a   ,__a     )

     # Checks everything loads correctly in the same way
     __lowercase				    =					tokenizer_r.from_pretrained(__a     )
     __lowercase				    =					tokenizer_p.from_pretrained(__a     )

     # Check special tokens are set accordingly on Rust and Python
     for key in tokenizer_pp.special_tokens_map:
      self.assertTrue(hasattr(__a   ,__a     )     )
      # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
      # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

     shutil.rmtree(__a     )

     # Save tokenizer rust, legacy_format=True
     __lowercase				    =					tempfile.mkdtemp()

     __lowercase				    =					tokenizer_r.save_pretrained(__a   ,legacy_format=__a     )
     __lowercase				    =					tokenizer_p.save_pretrained(__a     )

     # Checks it save with the same files
     self.assertSequenceEqual(__a   ,__a     )

     # Checks everything loads correctly in the same way
     __lowercase				    =					tokenizer_r.from_pretrained(__a     )
     __lowercase				    =					tokenizer_p.from_pretrained(__a     )

     # Check special tokens are set accordingly on Rust and Python
     for key in tokenizer_pp.special_tokens_map:
      self.assertTrue(hasattr(__a   ,__a     )     )

     shutil.rmtree(__a     )

     # Save tokenizer rust, legacy_format=False
     __lowercase				    =					tempfile.mkdtemp()

     __lowercase				    =					tokenizer_r.save_pretrained(__a   ,legacy_format=__a     )
     __lowercase				    =					tokenizer_p.save_pretrained(__a     )

     # Checks it saved the tokenizer.json file
     self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )

     # Checks everything loads correctly in the same way
     __lowercase				    =					tokenizer_r.from_pretrained(__a     )
     __lowercase				    =					tokenizer_p.from_pretrained(__a     )

     # Check special tokens are set accordingly on Rust and Python
     for key in tokenizer_pp.special_tokens_map:
      self.assertTrue(hasattr(__a   ,__a     )     )

     shutil.rmtree(__a     )






@require_torch
@require_sentencepiece
@require_tokenizers
class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    int							     =     'facebook/mbart-large-50-one-to-many-mmt'
  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     [
      ' UN Chief Says There Is No Military Solution in Syria',
      ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
  ]
  SCREAMING_SNAKE_CASE       :    str							     =     [
      'Şeful ONU declară că nu există o soluţie militară în Siria',
      'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
      ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
      ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
  ]
  SCREAMING_SNAKE_CASE       :    int							     =     [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]






  @classmethod
  def    SCREAMING_SNAKE_CASE ( cls  :	List[Any]     ):
   __lowercase				    =					MBartaaTokenizer.from_pretrained(
       cls.checkpoint_name   ,src_lang='''en_XX'''   ,tgt_lang='''ro_RO'''     )
   __lowercase				    =					1
   return cls






  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']   ,2_5_0_0_0_1     )
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']   ,2_5_0_0_0_4     )
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']   ,2_5_0_0_2_0     )
   self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']   ,2_5_0_0_3_8     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
   self.assertListEqual(self.expected_src_tokens   ,__a     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   self.assertIn(__a   ,self.tokenizer.all_special_ids     )
   __lowercase				    =					[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
   __lowercase				    =					self.tokenizer.decode(__a   ,skip_special_tokens=__a     )
   __lowercase				    =					self.tokenizer.decode(generated_ids[1:]   ,skip_special_tokens=__a     )
   self.assertEqual(__a   ,__a     )
   self.assertNotIn(self.tokenizer.eos_token   ,__a     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					['''this is gunna be a long sentence ''' * 2_0]
   assert isinstance(src_text[0]   ,__a     )
   __lowercase				    =					1_0
   __lowercase				    =					self.tokenizer(__a   ,max_length=__a   ,truncation=__a     ).input_ids[0]
   self.assertEqual(ids[0]   ,__a     )
   self.assertEqual(ids[-1]   ,2     )
   self.assertEqual(len(__a     )   ,__a     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']     )   ,[2_5_0_0_5_3, 2_5_0_0_0_1]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					tempfile.mkdtemp()
   __lowercase				    =					self.tokenizer.fairseq_tokens_to_ids
   self.tokenizer.save_pretrained(__a     )
   __lowercase				    =					MBartaaTokenizer.from_pretrained(__a     )
   self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,__a     )






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   __lowercase				    =					self.tokenizer(self.src_text   ,text_target=self.tgt_text   ,padding=__a   ,return_tensors='''pt'''     )
   __lowercase				    =					shift_tokens_right(batch['''labels''']   ,self.tokenizer.pad_token_id     )

   # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
   assert batch.input_ids[1][0] == EN_CODE
   assert batch.input_ids[1][-1] == 2
   assert batch.labels[1][0] == RO_CODE
   assert batch.labels[1][-1] == 2
   assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   __lowercase				    =					self.tokenizer(
       self.src_text   ,text_target=self.tgt_text   ,padding=__a   ,truncation=__a   ,max_length=len(self.expected_src_tokens     )   ,return_tensors='''pt'''   ,)
   __lowercase				    =					shift_tokens_right(batch['''labels''']   ,self.tokenizer.pad_token_id     )

   self.assertIsInstance(__a   ,__a     )

   self.assertEqual((2, 1_4)   ,batch.input_ids.shape     )
   self.assertEqual((2, 1_4)   ,batch.attention_mask.shape     )
   __lowercase				    =					batch.input_ids.tolist()[0]
   self.assertListEqual(self.expected_src_tokens   ,__a     )
   self.assertEqual(2   ,batch.decoder_input_ids[0, 0]     )  # decoder_start_token_id
   # Test that special tokens are reset
   self.assertEqual(self.tokenizer.prefix_tokens   ,[EN_CODE]     )
   self.assertEqual(self.tokenizer.suffix_tokens   ,[self.tokenizer.eos_token_id]     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					self.tokenizer(self.src_text   ,padding=__a   ,truncation=__a   ,max_length=3   ,return_tensors='''pt'''     )
   __lowercase				    =					self.tokenizer(
       text_target=self.tgt_text   ,padding=__a   ,truncation=__a   ,max_length=1_0   ,return_tensors='''pt'''     )
   __lowercase				    =					targets['''input_ids''']
   __lowercase				    =					shift_tokens_right(__a   ,self.tokenizer.pad_token_id     )

   self.assertEqual(batch.input_ids.shape[1]   ,3     )
   self.assertEqual(batch.decoder_input_ids.shape[1]   ,1_0     )






  @require_torch
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.tokenizer._build_translation_inputs(
       '''A test'''   ,return_tensors='''pt'''   ,src_lang='''en_XX'''   ,tgt_lang='''ar_AR'''     )

   self.assertEqual(
       nested_simplify(__a     )   ,{
           # en_XX, A, test, EOS
           '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
           '''attention_mask''': [[1, 1, 1, 1]],
           # ar_AR
           '''forced_bos_token_id''': 2_5_0_0_0_1,
       }   ,)






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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



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

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

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

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

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		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__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

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

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

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

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

      lowerCamelCase             =		(
          "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             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		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
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		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] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		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] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

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

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		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_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )