


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken







@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      str  ,   __A			:      Tuple  ,   __A			:      Any  ,   __A			:      Optional[int]    )     ->		List[str]:

       """simple docstring"""

       with open(_a    ) as metadata_file:
              a_ :		List[str]							=  json.load(_a    )
       a_ :		str							=  LukeConfig(use_entity_aware_attention=_a  ,   **metadata['model_config']    )

       # Load in the weights from the checkpoint_path
       a_ :		Any							=  torch.load(_a  ,   map_location='cpu'    )["module"]

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

       a_ :		Optional[Any]							=  XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']    )

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

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

       a_ :		str							=  MLukeTokenizer.from_pretrained(_a    )

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

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

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

       a_ :		Any							=  LukeForMaskedLM(config=_a    ).eval()

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

       a_ :		int							=  model.load_state_dict(_a  ,   strict=_a    )

       if set(_a    ) != {"luke.embeddings.position_ids"}:
              raise ValueError(F"""Unexpected unexpected_keys: {unexpected_keys}"""    )
       if set(_a    ) != {
           "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
       a_ :		Any							=  MLukeTokenizer.from_pretrained(_a  ,   task='entity_classification'    )

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

       a_ :		Optional[int]							=  model(**_a    )

       # Verify word hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              a_ :		List[str]							=  torch.Size((1, 33, 7_68)    )
              a_ :		List[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]  ,   _a  ,   atol=1e-4    ):
              raise ValueError

       # Verify entity hidden states
       if model_size == "large":
              raise NotImplementedError
       else:  # base
              a_ :		Optional[Any]							=  torch.Size((1, 1, 7_68)    )
              a_ :		Optional[int]							=  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]  ,   _a  ,   atol=1e-4    ):
              raise ValueError

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

       a_ :		Tuple							=  model(**_a    )

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

       a_ :		Optional[Any]							=  outputs.entity_logits[0][0].argmax().item()
       a_ :		Optional[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(_a    )    )
       model.save_pretrained(_a    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Any:

       """simple docstring"""

       a_ :		Dict							=  ["[MASK]", "[PAD]", "[UNK]"]

       a_ :		Tuple							=  [json.loads(_a    ) for line in open(_a    )]

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


if __name__ == "__main__":
   UpperCAmelCase_				:			Union[str, Any]   =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
   parser.add_argument(
       '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
   )
   parser.add_argument(
       '--entity_vocab_path',
       default=None,
       type=str,
       help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
   )
   parser.add_argument(
       '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
   )
   UpperCAmelCase_				:			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,
   )








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							torch_builtin(SCREAMING_SNAKE_CASE__					)					)					)
             self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							gelu_new(SCREAMING_SNAKE_CASE__					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             a_ :		str							=  get_activation('gelu_10'					)

             a_ :		Tuple							=  torch_builtin(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  geluaa(SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  torch.where(y_gelu_aa < 10.0					,							1					,							0					)

             self.assertTrue(torch.max(SCREAMING_SNAKE_CASE__					).item() == 10.0					)
             self.assertTrue(torch.allclose(y_gelu * clipped_mask					,							y_gelu_aa * clipped_mask					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             get_activation('gelu'					)
             get_activation('gelu_10'					)
             get_activation('gelu_fast'					)
             get_activation('gelu_new'					)
             get_activation('gelu_python'					)
             get_activation('gelu_pytorch_tanh'					)
             get_activation('linear'					)
             get_activation('mish'					)
             get_activation('quick_gelu'					)
             get_activation('relu'					)
             get_activation('sigmoid'					)
             get_activation('silu'					)
             get_activation('swish'					)
             get_activation('tanh'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation('bogus'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Any							=  get_activation('gelu'					)
             a_ :		Any							=  1
             a_ :		int							=  get_activation('gelu'					)
             self.assertEqual(acta.a					,							1					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Tuple							=  acta.a


