





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 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Any      )      ->	int:
    # Load configuration defined in the metadata file
    with open(_lowerCamelCase      ) as metadata_file:
        UpperCamelCase__							:				Tuple          =      json.load(_lowerCamelCase      )
    UpperCamelCase__							:				Dict          =      LukeConfig(use_entity_aware_attention=_lowerCamelCase		,   **metadata['''model_config''']      )

    # Load in the weights from the checkpoint_path
    UpperCamelCase__							:				List[Any]          =      torch.load(_lowerCamelCase		,   map_location='''cpu'''      )

    # Load the entity vocab file
    UpperCamelCase__							:				Optional[int]          =      load_entity_vocab(_lowerCamelCase      )

    UpperCamelCase__							:				List[Any]          =      RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']      )

    # Add special tokens to the token vocabulary for downstream tasks
    UpperCamelCase__							:				Optional[Any]          =      AddedToken('''<ent>'''		,   lstrip=_lowerCamelCase		,   rstrip=_lowerCamelCase      )
    UpperCamelCase__							:				Optional[int]          =      AddedToken('''<ent2>'''		,   lstrip=_lowerCamelCase		,   rstrip=_lowerCamelCase      )
    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(_lowerCamelCase      )
    with open(os.path.join(_lowerCamelCase		,   LukeTokenizer.vocab_files_names['''entity_vocab_file''']      )		,   '''w'''      ) as f:
        json.dump(_lowerCamelCase		,   _lowerCamelCase      )

    UpperCamelCase__							:				List[Any]          =      LukeTokenizer.from_pretrained(_lowerCamelCase      )

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

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

    UpperCamelCase__							:				int          =      LukeModel(config=_lowerCamelCase      ).eval()

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

    UpperCamelCase__							:				int          =      (
        """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 ."""
    )
    UpperCamelCase__							:				Dict          =      (39, 42)
    UpperCamelCase__							:				Optional[Any]          =      tokenizer(_lowerCamelCase		,   entity_spans=[span]		,   add_prefix_space=_lowerCamelCase		,   return_tensors='''pt'''      )

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

    # Verify word hidden states
    if model_size == "large":
        UpperCamelCase__							:				List[str]          =      torch.Size((1, 42, 1024)      )
        UpperCamelCase__							:				List[str]          =      torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
    else:  # base
        UpperCamelCase__							:				int          =      torch.Size((1, 42, 768)      )
        UpperCamelCase__							:				Tuple          =      torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]      )

    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]		,   _lowerCamelCase		,   atol=1e-4      ):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        UpperCamelCase__							:				Optional[int]          =      torch.Size((1, 1, 1024)      )
        UpperCamelCase__							:				Tuple          =      torch.tensor([[0.0466, -0.0106, -0.0179]]      )
    else:  # base
        UpperCamelCase__							:				Optional[int]          =      torch.Size((1, 1, 768)      )
        UpperCamelCase__							:				Dict          =      torch.tensor([[0.1457, 0.1044, 0.0174]]      )

    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]		,   _lowerCamelCase		,   atol=1e-4      ):
        raise ValueError

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	Dict:
    UpperCamelCase__							:				List[str]          =      {}
    with open(_lowerCamelCase		,   '''r'''		,   encoding='''utf-8'''      ) as f:
        for index, line in enumerate(_lowerCamelCase      ):
            UpperCamelCase__							:				Optional[int]          =      line.rstrip().split('''\t'''      )
            UpperCamelCase__							:				Any          =      index

    return entity_vocab


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


"""simple docstring"""




import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__=13				,     a__=7				,     a__=True				,     a__=True				,     a__=True				,     a__=True				,     a__=99				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=512				,     a__=16				,     a__=2				,     a__=0.0_2				,     a__=False				,     a__=True				,     a__="None"				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       Dict  			=     parent
       _lowerCAmelCase						:       str  			=     batch_size
       _lowerCAmelCase						:       List[Any]  			=     seq_length
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       Dict  			=     use_input_mask
       _lowerCAmelCase						:       int  			=     use_token_type_ids
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Optional[int]  			=     vocab_size
       _lowerCAmelCase						:       Tuple  			=     hidden_size
       _lowerCAmelCase						:       Tuple  			=     num_hidden_layers
       _lowerCAmelCase						:       Dict  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     intermediate_size
       _lowerCAmelCase						:       str  			=     hidden_act
       _lowerCAmelCase						:       Any  			=     hidden_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     type_vocab_size
       _lowerCAmelCase						:       Tuple  			=     type_sequence_label_size
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     num_choices
       _lowerCAmelCase						:       Tuple  			=     relative_attention
       _lowerCAmelCase						:       Tuple  			=     position_biased_input
       _lowerCAmelCase						:       Dict  			=     pos_att_type
       _lowerCAmelCase						:       Any  			=     scope






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

       _lowerCAmelCase						:       Optional[Any]  			=     None
       if self.use_input_mask:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2  )

       _lowerCAmelCase						:       str  			=     None
       if self.use_token_type_ids:
         _lowerCAmelCase						:       List[str]  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size  )

       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Any  			=     None
       if self.use_labels:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.type_sequence_label_size  )
         _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels  )
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.num_choices  )

       _lowerCAmelCase						:       List[str]  			=     self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






     def     __A  (		self  ):
       return DebertaVaConfig(
           vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     relative_attention=self.relative_attention				,     position_biased_input=self.position_biased_input				,     pos_att_type=self.pos_att_type				,     )






     def     __A  (		self				,     a__  ):
       self.parent.assertListEqual(list(result.loss.size()  )				,     []  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       Any  			=     model(a__  )[0]

       self.parent.assertListEqual(list(sequence_output.size()  )				,     [self.batch_size, self.seq_length, self.hidden_size]  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     DebertaVaForMaskedLM(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )

       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     self.num_labels
       _lowerCAmelCase						:       int  			=     DebertaVaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )
       self.parent.assertListEqual(list(result.logits.size()  )				,     [self.batch_size, self.num_labels]  )
       self.check_loss_output(a__  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.num_labels
       _lowerCAmelCase						:       str  			=     DebertaVaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     DebertaVaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     start_positions=a__				,     end_positions=a__				,     )
       self.parent.assertEqual(result.start_logits.shape				,     (self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape				,     (self.batch_size, self.seq_length)  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaForMultipleChoice(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     input_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       Optional[Any]  			=     token_type_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       str  			=     input_mask.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       List[str]  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__				,     )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       Union[str, Any]  			=     config_and_inputs
       _lowerCAmelCase						:       Union[str, Any]  			=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Union[str, Any]							=		(
         (
             DebertaVaModel,
             DebertaVaForMaskedLM,
             DebertaVaForSequenceClassification,
             DebertaVaForTokenClassification,
             DebertaVaForQuestionAnswering,
             DebertaVaForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			str							=		(
         {
             "feature-extraction": DebertaVaModel,
             "fill-mask": DebertaVaForMaskedLM,
             "question-answering": DebertaVaForQuestionAnswering,
             "text-classification": DebertaVaForSequenceClassification,
             "token-classification": DebertaVaForTokenClassification,
             "zero-shot": DebertaVaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:			Optional[Any]							=		True
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			Tuple							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     DebertaVaModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Tuple  			=     DebertaVaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







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






     @unittest.skip(reason="""Model not available yet"""  )
     def     __A  (		self  ):
       pass






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""  )

       _lowerCAmelCase						:       Dict  			=     torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
       _lowerCAmelCase						:       Any  			=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__  )[0]
       # compare the actual values for a slice.
       _lowerCAmelCase						:       str  			=     torch.tensor(
           [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]  )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,     a__				,     atol=1e-4  )				,     F"{output[:, 1:4, 1:4]}"  )




