






'''simple docstring'''
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							GPTaTokenizer
 snake_case_  							=							GPTaTokenizerFast
 snake_case_  							=							True
 snake_case_  							=							{"""add_prefix_space""": True}
 snake_case_  							=							False




 def      __magic_name__	(      self	:							Any      )	->						Tuple:
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE__     :				List[Any]									=[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
           '''<|endoftext|>''',
       ]
       SCREAMING_SNAKE_CASE__     :				Any									=dict(zip(__lowercase					,      range(len(__lowercase      )      )      )      )
       SCREAMING_SNAKE_CASE__     :				Any									=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       SCREAMING_SNAKE_CASE__     :				Tuple									={'''unk_token''': '''<unk>'''}

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['''vocab_file''']      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES['''merges_file''']      )
       with open(self.vocab_file					,      '''w'''					,      encoding='''utf-8'''      ) as fp:
             fp.write(json.dumps(__lowercase      ) + '''\n'''      )
       with open(self.merges_file					,      '''w'''					,      encoding='''utf-8'''      ) as fp:
             fp.write('''\n'''.join(__lowercase      )      )




 def      __magic_name__	(      self	:							Any					,      **__lowercase	:							Optional[Any]      )	->						str:
       kwargs.update(self.special_tokens_map      )
       return GPTaTokenizer.from_pretrained(self.tmpdirname					,      **__lowercase      )




 def      __magic_name__	(      self	:							int					,      **__lowercase	:							Optional[Any]      )	->						Tuple:
       kwargs.update(self.special_tokens_map      )
       return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,      **__lowercase      )




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							Tuple      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Any									='''lower newer'''
       SCREAMING_SNAKE_CASE__     :				List[str]									='''lower newer'''
       return input_text, output_text




 def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=GPTaTokenizer(self.vocab_file					,      self.merges_file					,      **self.special_tokens_map      )
       SCREAMING_SNAKE_CASE__     :				Tuple									='''lower newer'''
       SCREAMING_SNAKE_CASE__     :				List[str]									=['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tokenizer.tokenize(__lowercase					,      add_prefix_space=__lowercase      )
       self.assertListEqual(__lowercase					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=tokens + [tokenizer.unk_token]
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase      )					,      __lowercase      )




 def      __magic_name__	(      self	:							Tuple      )	->						Union[str, Any]:
       if not self.test_rust_tokenizer:
             return

       SCREAMING_SNAKE_CASE__     :				List[str]									=self.get_tokenizer()
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_rust_tokenizer(add_prefix_space=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				int									='''lower newer'''

       # Testing tokenization
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tokenizer.tokenize(__lowercase					,      add_prefix_space=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=rust_tokenizer.tokenize(__lowercase      )
       self.assertListEqual(__lowercase					,      __lowercase      )

       # Testing conversion to ids without special tokens
       SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer.encode(__lowercase					,      add_special_tokens=__lowercase					,      add_prefix_space=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=rust_tokenizer.encode(__lowercase					,      add_special_tokens=__lowercase      )
       self.assertListEqual(__lowercase					,      __lowercase      )

       # Testing conversion to ids with special tokens
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.get_rust_tokenizer(add_prefix_space=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=tokenizer.encode(__lowercase					,      add_prefix_space=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=rust_tokenizer.encode(__lowercase      )
       self.assertListEqual(__lowercase					,      __lowercase      )

       # Testing the unknown token
       SCREAMING_SNAKE_CASE__     :				Dict									=tokens + [rust_tokenizer.unk_token]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase      )					,      __lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]					,      *__lowercase	:							Tuple					,      **__lowercase	:							Dict      )	->						Tuple:
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[str]=15      )	->						Any:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.rust_tokenizer_class.from_pretrained(__lowercase					,      **__lowercase      )

                   # Simple input
                   SCREAMING_SNAKE_CASE__     :				Dict									='''This is a simple input'''
                   SCREAMING_SNAKE_CASE__     :				List[Any]									=['''This is a simple input 1''', '''This is a simple input 2''']
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=('''This is a simple input''', '''This is a pair''')
                   SCREAMING_SNAKE_CASE__     :				List[str]									=[
                       ('''This is a simple input 1''', '''This is a simple input 2'''),
                       ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                   ]

                   # Simple input tests
                   self.assertRaises(__lowercase					,      tokenizer_r.encode					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''      )

                   # Simple input
                   self.assertRaises(__lowercase					,      tokenizer_r.encode_plus					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''      )

                   # Simple input
                   self.assertRaises(
                       __lowercase					,      tokenizer_r.batch_encode_plus					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''					,      )

                   # Pair input
                   self.assertRaises(__lowercase					,      tokenizer_r.encode					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''      )

                   # Pair input
                   self.assertRaises(__lowercase					,      tokenizer_r.encode_plus					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''      )

                   # Pair input
                   self.assertRaises(
                       __lowercase					,      tokenizer_r.batch_encode_plus					,      __lowercase					,      max_length=__lowercase					,      padding='''max_length'''					,      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=GPTaTokenizer.from_pretrained(self.tmpdirname					,      pad_token='''<pad>'''      )

       # Simple input
       SCREAMING_SNAKE_CASE__     :				Optional[int]									='''This is a simple input'''
       SCREAMING_SNAKE_CASE__     :				str									=['''This is a simple input looooooooong''', '''This is a simple input''']
       SCREAMING_SNAKE_CASE__     :				List[Any]									=('''This is a simple input''', '''This is a pair''')
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[
           ('''This is a simple input loooooong''', '''This is a simple input'''),
           ('''This is a simple pair loooooong''', '''This is a simple pair'''),
       ]

       SCREAMING_SNAKE_CASE__     :				Dict									=tokenizer.pad_token_id

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tokenizer(__lowercase					,      padding='''max_length'''					,      max_length=30					,      return_tensors='''np'''      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tokenizer(__lowercase					,      padding=__lowercase					,      truncate=__lowercase					,      return_tensors='''np'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer(*__lowercase					,      padding='''max_length'''					,      max_length=60					,      return_tensors='''np'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer(__lowercase					,      padding=__lowercase					,      truncate=__lowercase					,      return_tensors='''np'''      )

       # s
       # test single string max_length padding
       self.assertEqual(out_s['''input_ids'''].shape[-1]					,      30      )
       self.assertTrue(pad_token_id in out_s['''input_ids''']      )
       self.assertTrue(0 in out_s['''attention_mask''']      )

       # s2
       # test automatic padding
       self.assertEqual(out_sa['''input_ids'''].shape[-1]					,      33      )
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]      )
       self.assertFalse(0 in out_sa['''attention_mask'''][0]      )
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]      )
       self.assertTrue(0 in out_sa['''attention_mask'''][1]      )

       # p
       # test single pair max_length padding
       self.assertEqual(out_p['''input_ids'''].shape[-1]					,      60      )
       self.assertTrue(pad_token_id in out_p['''input_ids''']      )
       self.assertTrue(0 in out_p['''attention_mask''']      )

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa['''input_ids'''].shape[-1]					,      52      )
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]      )
       self.assertFalse(0 in out_pa['''attention_mask'''][0]      )
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]      )
       self.assertTrue(0 in out_pa['''attention_mask'''][1]      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Dict									='''$$$'''
       SCREAMING_SNAKE_CASE__     :				List[Any]									=GPTaTokenizer.from_pretrained(self.tmpdirname					,      bos_token=__lowercase					,      add_bos_token=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				int									='''This is a simple input'''
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=['''This is a simple input 1''', '''This is a simple input 2''']

       SCREAMING_SNAKE_CASE__     :				List[Any]									=tokenizer.bos_token_id

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=tokenizer(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=tokenizer(__lowercase      )

       self.assertEqual(out_s.input_ids[0]					,      __lowercase      )
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer.decode(out_s.input_ids      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tokenizer.batch_decode(out_sa.input_ids      )

       self.assertEqual(decode_s.split()[0]					,      __lowercase      )
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )




 def      __magic_name__	(      self	:							Tuple      )	->						List[Any]:
       pass




 def      __magic_name__	(      self	:							Any      )	->						int:
       # TODO: change to self.get_tokenizers() when the fast version is implemented
       SCREAMING_SNAKE_CASE__     :				Dict									=[self.get_tokenizer(do_lower_case=__lowercase					,      add_bos_token=__lowercase      )]
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"      ):
                   SCREAMING_SNAKE_CASE__     :				List[str]									='''Encode this.'''
                   SCREAMING_SNAKE_CASE__     :				List[Any]									='''This one too please.'''
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.encode(__lowercase					,      add_special_tokens=__lowercase      )
                   encoded_sequence += tokenizer.encode(__lowercase					,      add_special_tokens=__lowercase      )
                   SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer.encode_plus(
                       __lowercase					,      __lowercase					,      add_special_tokens=__lowercase					,      return_special_tokens_mask=__lowercase					,      )
                   SCREAMING_SNAKE_CASE__     :				Any									=encoded_sequence_dict['''input_ids''']
                   SCREAMING_SNAKE_CASE__     :				Tuple									=encoded_sequence_dict['''special_tokens_mask''']
                   self.assertEqual(len(__lowercase      )					,      len(__lowercase      )      )

                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowercase      )
                   ]
                   SCREAMING_SNAKE_CASE__     :				int									=[x for x in filtered_sequence if x is not None]
                   self.assertEqual(__lowercase					,      __lowercase      )






@require_tokenizers
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							List[Any]      )	->						Tuple:
       # More context:
       # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
       # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
       # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

       SCREAMING_SNAKE_CASE__     :				List[str]									=AutoTokenizer.from_pretrained('''facebook/opt-350m'''					,      from_slow=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[str]									='''A photo of a cat'''

       SCREAMING_SNAKE_CASE__     :				Dict									=tokenizer.encode(
           __lowercase					,      )
       self.assertEqual(__lowercase					,      [2, 2_50, 13_45, 9, 10, 47_58]      )
       tokenizer.save_pretrained('''test_opt'''      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoTokenizer.from_pretrained('''./test_opt'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tokenizer.encode(
           __lowercase					,      )
       self.assertEqual(__lowercase					,      [2, 2_50, 13_45, 9, 10, 47_58]      )




 def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=AutoTokenizer.from_pretrained('''facebook/opt-350m'''					,      use_slow=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									='''A photo of a cat'''

       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer.encode(
           __lowercase					,      )
       # Same as above
       self.assertEqual(__lowercase					,      [2, 2_50, 13_45, 9, 10, 47_58]      )




 @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''      )
 def      __magic_name__	(      self	:							List[str]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=AutoTokenizer.from_pretrained('''facebook/opt-350m'''					,      from_slow=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				str									='''bos'''
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tokenizer.get_vocab()['''bos''']

       SCREAMING_SNAKE_CASE__     :				Any									='''A photo of a cat'''
       SCREAMING_SNAKE_CASE__     :				Any									=tokenizer.encode(
           __lowercase					,      )
       # We changed the bos token
       self.assertEqual(__lowercase					,      [3_19_57, 2_50, 13_45, 9, 10, 47_58]      )
       tokenizer.save_pretrained('''./tok'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=AutoTokenizer.from_pretrained('''./tok'''      )
       self.assertTrue(tokenizer.is_fast      )
       SCREAMING_SNAKE_CASE__     :				str									=tokenizer.encode(
           __lowercase					,      )
       self.assertEqual(__lowercase					,      [3_19_57, 2_50, 13_45, 9, 10, 47_58]      )







'''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  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Any,	UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      with open(UpperCamelCase__							) as metadata_file:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=LukeConfig(use_entity_aware_attention=UpperCamelCase__,	**metadata['''model_config''']							)

      # Load in the weights from the checkpoint_path
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.load(UpperCamelCase__,	map_location='''cpu'''							)['''module''']

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

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

      # Add special tokens to the token vocabulary for downstream tasks
      SCREAMING_SNAKE_CASE__     :				List[Any]									=AddedToken('''<ent>''',	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=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__     :				Optional[Any]									=json.load(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				int									='''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__     :				Optional[Any]									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)

      # Initialize the embeddings of the special tokens
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer.convert_tokens_to_ids(['''@''']							)[0]
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer.convert_tokens_to_ids(['''#''']							)[0]

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

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

      SCREAMING_SNAKE_CASE__     :				int									=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__     :				Tuple									=OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('''lm_head'''							) or key.startswith('''entity_predictions'''							)):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=state_dict[key]
            else:
                  SCREAMING_SNAKE_CASE__     :				Any									=state_dict[key]

      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				Any									=MLukeTokenizer.from_pretrained(UpperCamelCase__,	task='''entity_classification'''							)

      SCREAMING_SNAKE_CASE__     :				str									='''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__     :				Union[str, Any]									=(0, 9)
      SCREAMING_SNAKE_CASE__     :				str									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**UpperCamelCase__							)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 3_3, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]							)

      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__     :				Any									=torch.Size((1, 1, 7_6_8)							)
            SCREAMING_SNAKE_CASE__     :				int									=torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]							)

      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__     :				str									=MLukeTokenizer.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									='''Tokyo is the capital of <mask>.'''
      SCREAMING_SNAKE_CASE__     :				Dict									=(2_4, 3_0)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=tokenizer(UpperCamelCase__,	entity_spans=[span],	return_tensors='''pt'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(**UpperCamelCase__							)

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

      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.entity_logits[0][0].argmax().item()
      SCREAMING_SNAKE_CASE__     :				Dict									=[
          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  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=['''[MASK]''', '''[PAD]''', '''[UNK]''']

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

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


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