
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  lowercase  : str     =							"""pt"""
elif is_tf_available():
  lowercase  : Any     =							"""tf"""
else:
  lowercase  : Tuple     =							"""jax"""

class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						str=    PerceiverTokenizer
    _a		:						str=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Tuple	     =				PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer_class.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=False		,snake_case=20		,snake_case=5				):



           '''simple docstring'''







           lowercase							:					Any	     =				[]
           for i in range(len(snake_case				)				):
                  try:
                         lowercase							:					List[str]	     =				tokenizer.decode([i]		,clean_up_tokenization_spaces=snake_case				)
                  except UnicodeDecodeError:
                         pass
                  toks.append((i, tok)				)

           lowercase							:					Optional[Any]	     =				list(filter(lambda snake_case				: re.match(r"""^[ a-zA-Z]+$"""		,t[1]				)		,snake_case				)				)
           lowercase							:					str	     =				list(filter(lambda snake_case				: [t[0]] == tokenizer.encode(t[1]		,add_special_tokens=snake_case				)		,snake_case				)				)
           if max_length is not None and len(snake_case				) > max_length:
                  lowercase							:					Tuple	     =				toks[:max_length]
           if min_length is not None and len(snake_case				) < min_length and len(snake_case				) > 0:
                  while len(snake_case				) < min_length:
                         lowercase							:					Tuple	     =				toks + toks
        # toks_str = [t[1] for t in toks]
           lowercase							:					List[str]	     =				[t[0] for t in toks]

           # Ensure consistency
           lowercase							:					Any	     =				tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				)
           if " " not in output_txt and len(snake_case				) > 1:
                  lowercase							:					int	     =				(
                      tokenizer.decode([toks_ids[0]]		,clean_up_tokenization_spaces=snake_case				)
                      + """ """
                      + tokenizer.decode(toks_ids[1:]		,clean_up_tokenization_spaces=snake_case				)
                  )
           if with_prefix_space:
                  lowercase							:					Optional[int]	     =				""" """ + output_txt
           lowercase							:					Any	     =				tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
           return output_txt, output_ids
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.perceiver_tokenizer
           lowercase							:					int	     =				"""Unicode €."""
           lowercase							:					Any	     =				tokenizer(snake_case				)
           lowercase							:					Dict	     =				[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded["""input_ids"""]		,snake_case				)

           # decoding
           lowercase							:					Any	     =				tokenizer.decode(snake_case				)
           self.assertEqual(snake_case		,"""[CLS]Unicode €.[SEP]"""				)

           lowercase							:					Any	     =				tokenizer("""e è é ê ë"""				)
           lowercase							:					int	     =				[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded["""input_ids"""]		,snake_case				)
           # decoding
           lowercase							:					Tuple	     =				tokenizer.decode(snake_case				)
           self.assertEqual(snake_case		,"""[CLS]e è é ê ë[SEP]"""				)

           # encode/decode, but with `encode` instead of `__call__`
           self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""				)				)		,"""[CLS]e è é ê ë[SEP]"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.perceiver_tokenizer
           lowercase							:					Optional[int]	     =				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           # fmt: off
           lowercase							:					Tuple	     =				[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           lowercase							:					Tuple	     =				tokenizer(snake_case		,padding=snake_case		,return_tensors=snake_case				)
           self.assertIsInstance(snake_case		,snake_case				)

           if FRAMEWORK != "jax":
                  lowercase							:					List[Any]	     =				list(batch.input_ids.numpy()[0]				)
           else:
                  lowercase							:					int	     =				list(batch.input_ids.tolist()[0]				)

           self.assertListEqual(snake_case		,snake_case				)

           self.assertEqual((2, 38)		,batch.input_ids.shape				)
           self.assertEqual((2, 38)		,batch.attention_mask.shape				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.perceiver_tokenizer
           lowercase							:					List[str]	     =				["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           lowercase							:					Union[str, Any]	     =				tokenizer(snake_case		,padding=snake_case		,return_tensors=snake_case				)
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn("""input_ids"""		,snake_case				)
           self.assertIn("""attention_mask"""		,snake_case				)
           self.assertNotIn("""decoder_input_ids"""		,snake_case				)
           self.assertNotIn("""decoder_attention_mask"""		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.perceiver_tokenizer
           lowercase							:					int	     =				[
               """Summary of the text.""",
               """Another summary.""",
           ]
           lowercase							:					Any	     =				tokenizer(
               text_target=snake_case		,max_length=32		,padding="""max_length"""		,truncation=snake_case		,return_tensors=snake_case				)
           self.assertEqual(32		,targets["""input_ids"""].shape[1]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"				):
                         self.assertNotEqual(tokenizer.model_max_length		,42				)

        # Now let's start the test
           lowercase							:					str	     =				self.get_tokenizers()
           for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"				):
                         # Isolate this from the other tests because we save additional tokens/etc
                         lowercase							:					Any	     =				tempfile.mkdtemp()

                         lowercase							:					Any	     =				""" He is very happy, UNwant\u00E9d,running"""
                         lowercase							:					Tuple	     =				tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
                         tokenizer.save_pretrained(snake_case				)

                         lowercase							:					Tuple	     =				tokenizer.__class__.from_pretrained(snake_case				)
                         lowercase							:					Tuple	     =				after_tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
                         self.assertListEqual(snake_case		,snake_case				)

                         shutil.rmtree(snake_case				)

           lowercase							:					Tuple	     =				self.get_tokenizers(model_max_length=42				)
           for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"				):
                         # Isolate this from the other tests because we save additional tokens/etc
                         lowercase							:					Tuple	     =				tempfile.mkdtemp()

                         lowercase							:					Optional[int]	     =				""" He is very happy, UNwant\u00E9d,running"""
                         tokenizer.add_tokens(["""bim""", """bambam"""]				)
                         lowercase							:					Any	     =				tokenizer.additional_special_tokens
                         additional_special_tokens.append("""new_additional_special_token"""				)
                         tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}				)
                         lowercase							:					Any	     =				tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
                         tokenizer.save_pretrained(snake_case				)

                         lowercase							:					List[str]	     =				tokenizer.__class__.from_pretrained(snake_case				)
                         lowercase							:					Tuple	     =				after_tokenizer.encode(snake_case		,add_special_tokens=snake_case				)
                         self.assertListEqual(snake_case		,snake_case				)
                         self.assertIn("""new_additional_special_token"""		,after_tokenizer.additional_special_tokens				)
                         self.assertEqual(after_tokenizer.model_max_length		,42				)

                         lowercase							:					List[str]	     =				tokenizer.__class__.from_pretrained(snake_case		,model_max_length=43				)
                         self.assertEqual(tokenizer.model_max_length		,43				)

                         shutil.rmtree(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				[]
           if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

           if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

           for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(snake_case				)

                         with open(os.path.join(snake_case		,"""special_tokens_map.json"""				)		,encoding="""utf-8"""				) as json_file:
                                lowercase							:					Dict	     =				json.load(snake_case				)

                         with open(os.path.join(snake_case		,"""tokenizer_config.json"""				)		,encoding="""utf-8"""				) as json_file:
                                lowercase							:					Tuple	     =				json.load(snake_case				)

                         lowercase							:					Dict	     =				[f"<extra_id_{i}>" for i in range(125				)]

                         lowercase							:					int	     =				added_tokens_extra_ids + [
                             """an_additional_special_token"""
                         ]
                         lowercase							:					List[str]	     =				added_tokens_extra_ids + [
                             """an_additional_special_token"""
                         ]

                         with open(os.path.join(snake_case		,"""special_tokens_map.json"""				)		,"""w"""		,encoding="""utf-8"""				) as outfile:
                                json.dump(snake_case		,snake_case				)
                         with open(os.path.join(snake_case		,"""tokenizer_config.json"""				)		,"""w"""		,encoding="""utf-8"""				) as outfile:
                                json.dump(snake_case		,snake_case				)

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         lowercase							:					Optional[int]	     =				tokenizer_class.from_pretrained(
                             snake_case		,)
                         self.assertIn(
                             """an_additional_special_token"""		,tokenizer_without_change_in_init.additional_special_tokens				)
                         self.assertEqual(
                             ["""an_additional_special_token"""]		,tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]				)				)		,)

                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         lowercase							:					str	     =				added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""		,lstrip=snake_case				)]
                         lowercase							:					Optional[int]	     =				tokenizer_class.from_pretrained(
                             snake_case		,additional_special_tokens=snake_case		,)

                         self.assertIn("""a_new_additional_special_token"""		,tokenizer.additional_special_tokens				)
                         self.assertEqual(
                             ["""a_new_additional_special_token"""]		,tokenizer.convert_ids_to_tokens(
                                 tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]				)				)		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)		,"""�"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				self.get_tokenizers(fast=snake_case		,do_lower_case=snake_case				)
           for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"				):
                         lowercase							:					Dict	     =				["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                         lowercase							:					Any	     =				tokenizer.convert_tokens_to_string(snake_case				)

                         self.assertIsInstance(snake_case		,snake_case				)


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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       # Load configuration defined in the metadata file
       with open(SCREAMING_SNAKE_CASE__    ) as metadata_file:
              lowercase							:					Union[str, Any]	     =				json.load(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Tuple	     =				LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ ,     **metadata["""model_config"""]    )

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

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

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

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

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

       lowercase							:					int	     =				MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )

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

       lowercase							:					int	     =				state_dict["""embeddings.word_embeddings.weight"""]
       lowercase							:					Union[str, Any]	     =				word_emb[ent_init_index].unsqueeze(0    )
       lowercase							:					List[str]	     =				word_emb[enta_init_index].unsqueeze(0    )
       lowercase							:					str	     =				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"]:
              lowercase							:					List[Any]	     =				state_dict[bias_name]
              lowercase							:					Any	     =				decoder_bias[ent_init_index].unsqueeze(0    )
              lowercase							:					Tuple	     =				decoder_bias[enta_init_index].unsqueeze(0    )
              lowercase							:					int	     =				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"]:
                     lowercase							:					Union[str, Any]	     =				f"encoder.layer.{layer_index}.attention.self."
                     lowercase							:					List[str]	     =				state_dict[prefix + matrix_name]
                     lowercase							:					Any	     =				state_dict[prefix + matrix_name]
                     lowercase							:					str	     =				state_dict[prefix + matrix_name]

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

       lowercase							:					List[str]	     =				LukeForMaskedLM(config=SCREAMING_SNAKE_CASE__    ).eval()

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

       lowercase     ,      lowercase							:					int	     =				model.load_state_dict(SCREAMING_SNAKE_CASE__ ,     strict=SCREAMING_SNAKE_CASE__    )

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

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

       lowercase							:					Any	     =				model(**SCREAMING_SNAKE_CASE__    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					List[Any]	     =				torch.Size((1, 33, 768)    )
              lowercase							:					str	     =				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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              lowercase							:					Optional[int]	     =				torch.Size((1, 1, 768)    )
              lowercase							:					List[Any]	     =				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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    ):
              raise ValueError

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

       lowercase							:					Dict	     =				model(**SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				encoding["""input_ids"""][0].tolist()
       lowercase							:					List[Any]	     =				input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""    )    )
       lowercase							:					Dict	     =				outputs.logits[0][mask_position_id].argmax(dim=-1    )
       assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				outputs.entity_logits[0][0].argmax().item()
       lowercase							:					int	     =				[
           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(SCREAMING_SNAKE_CASE__    )    )
       model.save_pretrained(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       lowercase							:					Optional[int]	     =				["""[MASK]""", """[PAD]""", """[UNK]"""]

       lowercase							:					List[str]	     =				[json.loads(SCREAMING_SNAKE_CASE__    ) for line in open(SCREAMING_SNAKE_CASE__    )]

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


if __name__ == "__main__":
  lowercase  : 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."""
  )
  lowercase  : 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,
  )
