'''simple docstring'''





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_    ( snake_case_	:			int ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Any:


     '''simple docstring'''




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

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

     # Load the entity vocab file
     UpperCAmelCase_	   =       load_entity_vocab(snake_case_      )

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

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

     UpperCAmelCase_	   =       LukeTokenizer.from_pretrained(snake_case_      )

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

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

     UpperCAmelCase_	   =       LukeModel(config=snake_case_      ).eval()

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

     UpperCAmelCase_	   =       (
         "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_	   =       (39, 42)
     UpperCAmelCase_	   =       tokenizer(snake_case_ ,   entity_spans=[span] ,   add_prefix_space=snake_case_ ,   return_tensors="pt"      )

     UpperCAmelCase_	   =       model(**snake_case_      )

     # Verify word hidden states
     if model_size == "large":
          UpperCAmelCase_	   =       torch.Size((1, 42, 10_24)      )
          UpperCAmelCase_	   =       torch.tensor(
              [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]      )
     else:  # base
          UpperCAmelCase_	   =       torch.Size((1, 42, 7_68)      )
          UpperCAmelCase_	   =       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] ,   snake_case_ ,   atol=1E-4      ):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          UpperCAmelCase_	   =       torch.Size((1, 1, 10_24)      )
          UpperCAmelCase_	   =       torch.tensor([[0.0466, -0.0106, -0.0179]]      )
     else:  # base
          UpperCAmelCase_	   =       torch.Size((1, 1, 7_68)      )
          UpperCAmelCase_	   =       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] ,   snake_case_ ,   atol=1E-4      ):
          raise ValueError

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





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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

     return entity_vocab


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Dict  =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.'
    )
    SCREAMING_SNAKE_CASE_:					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,
    )





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




