


# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowercase_	   =     get_logger(__name__)


class 	A_ :




      '''simple docstring'''


      __snake_case 	=       """dummy_data"""
      __snake_case 	=       """datasets"""
      __snake_case 	=       False




      def __init__(    self:	Dict						,				a:	str						,				a:	str						,				a:	Union[Version, str]						,				a:	Optional[str] = None						,				a:	bool = False						,				a:	bool = True						,				a:	Optional[List[Callable]] = None						,				):
       __lowerCamelCase		:							List[str]		 =						0
       __lowerCamelCase		:							List[Any]		 =						dataset_name
       __lowerCamelCase		:							Tuple		 =						cache_dir
       __lowerCamelCase		:							Any		 =						use_local_dummy_data
       __lowerCamelCase		:							Optional[int]		 =						config
       # download_callbacks take a single url as input
       __lowerCamelCase		:							List[Callable]		 =						download_callbacks or []
       # if False, it doesn't load existing files and it returns the paths of the dummy files relative
       # to the dummy_data zip file root
       __lowerCamelCase		:							int		 =						load_existing_dummy_data

       # TODO(PVP, QL) might need to make this more general
       __lowerCamelCase		:							List[str]		 =						str(a    )
       # to be downloaded
       __lowerCamelCase		:							Optional[Any]		 =						None
       __lowerCamelCase		:							List[Any]		 =						None




      @property
      def 					_snake_case     (    self:	Tuple    ):
       if self._dummy_file is None:
        __lowerCamelCase		:							Optional[Any]		 =						self.download_dummy_data()
       return self._dummy_file




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       if self.config is not None:
        # structure is dummy / config_name / version_name
        return os.path.join('dummy'						,				self.config.name						,				self.version_name    )
       # structure is dummy / version_name
       return os.path.join('dummy'						,				self.version_name    )




      @property
      def 					_snake_case     (    self:	List[str]    ):
       return os.path.join(self.dummy_data_folder						,				'dummy_data.zip'    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						(
           self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
       )

       __lowerCamelCase		:							Tuple		 =						cached_path(
           a						,				cache_dir=self.cache_dir						,				extract_compressed_file=a						,				force_extract=a    )

       return os.path.join(a						,				self.dummy_file_name    )




      @property
      def 					_snake_case     (    self:	Dict    ):
       return os.path.join(self.datasets_scripts_dir						,				self.dataset_name						,				self.dummy_zip_file    )




      @property
      def 					_snake_case     (    self:	int    ):
       if self._bucket_url is None:
        __lowerCamelCase		:							List[str]		 =						hf_github_url(self.dataset_name						,				self.dummy_zip_file.replace(os.sep						,				'/'    )    )
       return self._bucket_url




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       # return full path if its a dir
       if os.path.isdir(self.dummy_file    ):
        return self.dummy_file
       # else cut off path to file -> example `xsum`.
       return "/".join(self.dummy_file.replace(os.sep						,				'/'    ).split('/'    )[:-1]    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Any						,				*a:	str    ):
       if self.load_existing_dummy_data:
        # dummy data is downloaded and tested
        __lowerCamelCase		:							List[str]		 =						self.dummy_file
       else:
        # dummy data cannot be downloaded and only the path to dummy file is returned
        __lowerCamelCase		:							Tuple		 =						self.dummy_file_name

       # special case when data_url is a dict
       if isinstance(a						,				a    ):
        return self.create_dummy_data_dict(a						,				a    )
       elif isinstance(a						,				(list, tuple)    ):
        return self.create_dummy_data_list(a						,				a    )
       else:
        return self.create_dummy_data_single(a						,				a    )




      def 					_snake_case     (    self:	Tuple						,				a:	Optional[int]						,				*a:	str    ):
       return self.download_and_extract(a    )




      def 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Dict    ):
       return self.download_and_extract(a    )




      def 					_snake_case     (    self:	Dict						,				a:	Tuple						,				*a:	Optional[Any]						,				**a:	List[str]    ):
       return path




      def 					_snake_case     (    self:	List[str]    ):
       return {}




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	int						,				a:	int    ):
       __lowerCamelCase		:							Union[str, Any]		 =						{}
       for key, single_urls in data_url.items():
        for download_callback in self.download_callbacks:
         if isinstance(a						,				a    ):
          for single_url in single_urls:
           download_callback(a    )
         else:
          __lowerCamelCase		:							Dict		 =						single_urls
          download_callback(a    )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
        if isinstance(a						,				a    ):
         __lowerCamelCase		:							List[str]		 =						[os.path.join(a						,				urllib.parse.quote_plus(Path(a    ).name    )    ) for x in single_urls]
        else:
         __lowerCamelCase		:							str		 =						single_urls
         __lowerCamelCase		:							Tuple		 =						os.path.join(a						,				urllib.parse.quote_plus(Path(a    ).name    )    )
        __lowerCamelCase		:							Tuple		 =						value

       # make sure that values are unique
       if all(isinstance(a						,				a    ) for i in dummy_data_dict.values()    ) and len(set(dummy_data_dict.values()    )    ) < len(
           dummy_data_dict.values()    ):
        # append key to value to make its name unique
        __lowerCamelCase		:							Any		 =						{key: value + key for key, value in dummy_data_dict.items()}

       return dummy_data_dict




      def 					_snake_case     (    self:	int						,				a:	Union[str, Any]						,				a:	Union[str, Any]    ):
       __lowerCamelCase		:							Any		 =						[]
       # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
       __lowerCamelCase		:							str		 =						all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}'						,				a    )    ) for url in data_url    )
       __lowerCamelCase		:							Dict		 =						all(
           url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'    ) for url in data_url    )
       if data_url and (is_tf_records or is_pubmed_records):
        __lowerCamelCase		:							List[str]		 =						[data_url[0]] * len(a    )
       for single_url in data_url:
        for download_callback in self.download_callbacks:
         download_callback(a    )
        # we force the name of each key to be the last file / folder name of the url path
        # if the url has arguments, we need to encode them with urllib.parse.quote_plus
        __lowerCamelCase		:							List[str]		 =						os.path.join(a						,				urllib.parse.quote_plus(single_url.split('/'    )[-1]    )    )
        dummy_data_list.append(a    )
       return dummy_data_list




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Union[str, Any]						,				a:	Optional[int]    ):
       for download_callback in self.download_callbacks:
        download_callback(a    )
       # we force the name of each key to be the last file / folder name of the url path
       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
       __lowerCamelCase		:							Any		 =						os.path.join(a						,				urllib.parse.quote_plus(data_url.split('/'    )[-1]    )    )
       if os.path.exists(a    ) or not self.load_existing_dummy_data:
        return value
       else:
        # Backward compatibility, maybe deprecate at one point.
        # For many datasets with single url calls to dl_manager.download_and_extract,
        # the dummy_data.zip file is actually the zipped downloaded file
        # while now we expected the dummy_data.zip file to be a directory containing
        # the downloaded file.
        return path_to_dummy_data




      def 					_snake_case     (    self:	Union[str, Any]    ):
       pass




      def 					_snake_case     (    self:	Optional[int]    ):
       pass




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Union[str, Any]    ):
       def _iter_archive_members(a:	List[str]    ):
        # this preserves the order of the members inside the ZIP archive
        __lowerCamelCase		:							List[str]		 =						Path(self.dummy_file    ).parent
        __lowerCamelCase		:							str		 =						path.relative_to(a    )
        with ZipFile(self.local_path_to_dummy_data    ) as zip_file:
         __lowerCamelCase		:							List[str]		 =						zip_file.namelist()
        for member in members:
         if member.startswith(relative_path.as_posix()    ):
          yield dummy_parent_path.joinpath(a    )

       __lowerCamelCase		:							Union[str, Any]		 =						Path(a    )
       __lowerCamelCase		:							List[str]		 =						_iter_archive_members(a    ) if self.use_local_dummy_data else path.rglob('*'    )
       for file_path in file_paths:
        if file_path.is_file() and not file_path.name.startswith(('.', '__')    ):
         yield file_path.relative_to(a    ).as_posix(), file_path.open('rb'    )




      def 					_snake_case     (    self:	Union[str, Any]						,				a:	Union[str, Any]    ):
       if not isinstance(a						,				a    ):
        __lowerCamelCase		:							Any		 =						[paths]
       for path in paths:
        if os.path.isfile(a    ):
         if os.path.basename(a    ).startswith(('.', '__')    ):
          return
         yield path
        else:
         for dirpath, dirnames, filenames in os.walk(a    ):
          if os.path.basename(a    ).startswith(('.', '__')    ):
           continue
          dirnames.sort()
          for filename in sorted(a    ):
           if filename.startswith(('.', '__')    ):
            continue
           yield os.path.join(a						,				a    )





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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Load configuration defined in the metadata file
 with open(SCREAMING_SNAKE_CASE__						) as metadata_file:
  __lowerCamelCase		:							List[str]		 =						json.load(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__      ,	**metadata['model_config']						)

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

 # Load the entity vocab file
 __lowerCamelCase		:							List[Any]		 =						load_entity_vocab(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Optional[int]		 =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

 # Add special tokens to the token vocabulary for downstream tasks
 __lowerCamelCase		:							str		 =						AddedToken('<ent>'      ,	lstrip=SCREAMING_SNAKE_CASE__      ,	rstrip=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						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__      ,	LukeTokenizer.vocab_files_names['entity_vocab_file']						)      ,	'w'						) as f:
  json.dump(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							str		 =						LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__						)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
 __lowerCamelCase		:							Optional[int]		 =						state_dict['entity_embeddings.entity_embeddings.weight']
 __lowerCamelCase		:							Union[str, Any]		 =						entity_emb[entity_vocab['[MASK]']]

 __lowerCamelCase		:							Optional[Any]		 =						LukeModel(config=SCREAMING_SNAKE_CASE__						).eval()

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

 __lowerCamelCase		:							Dict		 =						(
     '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 .'
 )
 __lowerCamelCase		:							Union[str, Any]		 =						(39, 42)
 __lowerCamelCase		:							Optional[Any]		 =						tokenizer(SCREAMING_SNAKE_CASE__      ,	entity_spans=[span]      ,	add_prefix_space=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						)

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

 # Verify word hidden states
 if model_size == "large":
  __lowerCamelCase		:							Dict		 =						torch.Size((1, 42, 1_024)						)
  __lowerCamelCase		:							int		 =						torch.tensor(
      [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
 else:  # base
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 42, 768)						)
  __lowerCamelCase		:							Tuple		 =						torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

 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":
  __lowerCamelCase		:							Union[str, Any]		 =						torch.Size((1, 1, 1_024)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
 else:  # base
  __lowerCamelCase		:							int		 =						torch.Size((1, 1, 768)						)
  __lowerCamelCase		:							Dict		 =						torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

 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

 # Finally, save our PyTorch model and tokenizer
 print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						{}
 with open(SCREAMING_SNAKE_CASE__      ,	'r'      ,	encoding='utf-8'						) as f:
  for index, line in enumerate(SCREAMING_SNAKE_CASE__						):
   __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						line.rstrip().split('\t'						)
   __lowerCamelCase		:							Any		 =						index

 return entity_vocab


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

