


'''simple docstring'''


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


lowerCamelCase__					=   get_logger(__name__)






class 		lowerCAmelCase__	:
			lowerCAmelCase       :		str				      =   "dummy_data"
			lowerCAmelCase       :		List[str]				      =   "datasets"
			lowerCAmelCase       :		Dict				      =   False




			def __init__(						self     :   str		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Union[Version, str]		,     lowerCamelCase__     :   Optional[str] = None		,     lowerCamelCase__     :   bool = False		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Optional[List[Callable]] = None		,     )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					0
					_UpperCAmelCase						:				Optional[Any]	    =					dataset_name
					_UpperCAmelCase						:				str	    =					cache_dir
					_UpperCAmelCase						:				int	    =					use_local_dummy_data
					_UpperCAmelCase						:				Dict	    =					config
					# download_callbacks take a single url as input
					_UpperCAmelCase						:				str	    =					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
					_UpperCAmelCase						:				Optional[Any]	    =					load_existing_dummy_data

					# TODO(PVP, QL) might need to make this more general
					_UpperCAmelCase						:				Tuple	    =					str(__A       )
					# to be downloaded
					_UpperCAmelCase						:				Optional[int]	    =					None
					_UpperCAmelCase						:				Optional[Any]	    =					None




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->Dict:



					'''simple docstring'''







					if self._dummy_file is None:
							_UpperCAmelCase						:				List[str]	    =					self.download_dummy_data()
					return self._dummy_file




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->Dict:



					'''simple docstring'''







					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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Optional[int]:



					'''simple docstring'''







					return os.path.join(self.dummy_data_folder		,     "dummy_data.zip"       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
					)

					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   List[str]       )      ->Optional[int]:



					'''simple docstring'''







					return os.path.join(self.datasets_scripts_dir		,     self.dataset_name		,     self.dummy_zip_file       )




			@property
			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->str:



					'''simple docstring'''







					if self._bucket_url is None:
							_UpperCAmelCase						:				str	    =					hf_github_url(self.dataset_name		,     self.dummy_zip_file.replace(os.sep		,     "/"       )       )
					return self._bucket_url




			@property
			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Any:



					'''simple docstring'''







					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 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Optional[int]		,     *lowerCamelCase__     :   Optional[int]       )      ->Any:



					'''simple docstring'''







					if self.load_existing_dummy_data:
							# dummy data is downloaded and tested
							_UpperCAmelCase						:				Union[str, Any]	    =					self.dummy_file
					else:
							# dummy data cannot be downloaded and only the path to dummy file is returned
							_UpperCAmelCase						:				Optional[Any]	    =					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 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   Dict		,     *lowerCamelCase__     :   Any       )      ->Optional[int]:



					'''simple docstring'''







					return self.download_and_extract(__A       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Dict       )      ->Union[str, Any]:



					'''simple docstring'''







					return self.download_and_extract(__A       )




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Any		,     *lowerCamelCase__     :   List[str]		,     **lowerCamelCase__     :   Optional[Any]       )      ->Optional[Any]:



					'''simple docstring'''







					return path




			def 							lowerCAmelCase__			(						self     :   str       )      ->Optional[int]:



					'''simple docstring'''







					return {}




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Any       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					{}
					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:
											_UpperCAmelCase						:				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       ):
									_UpperCAmelCase						:				List[str]	    =					[os.path.join(__A		,     urllib.parse.quote_plus(Path(__A       ).name       )       ) for x in single_urls]
							else:
									_UpperCAmelCase						:				Tuple	    =					single_urls
									_UpperCAmelCase						:				int	    =					os.path.join(__A		,     urllib.parse.quote_plus(Path(__A       ).name       )       )
							_UpperCAmelCase						:				Any	    =					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
							_UpperCAmelCase						:				Optional[Any]	    =					{key: value + key for key, value in dummy_data_dict.items()}

					return dummy_data_dict




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   Dict		,     lowerCamelCase__     :   Optional[Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					[]
					# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
					_UpperCAmelCase						:				Tuple	    =					all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"		,     __A       )       ) for url in data_url       )
					_UpperCAmelCase						:				Any	    =					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):
							_UpperCAmelCase						:				Any	    =					[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
							_UpperCAmelCase						:				Optional[Any]	    =					os.path.join(__A		,     urllib.parse.quote_plus(single_url.split("/"       )[-1]       )       )
							dummy_data_list.append(__A       )
					return dummy_data_list




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   List[str]       )      ->Optional[int]:



					'''simple docstring'''







					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
					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   str       )      ->Any:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   Tuple		,     lowerCamelCase__     :   Dict       )      ->Tuple:



					'''simple docstring'''







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

					_UpperCAmelCase						:				str	    =					Path(__A       )
					_UpperCAmelCase						:				Union[str, Any]	    =					_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 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Optional[Any]       )      ->Optional[Any]:



					'''simple docstring'''







					if not isinstance(__A		,     __A       ):
							_UpperCAmelCase						:				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       )










'''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 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  Any     ) ->      Optional[Any]:



       """simple docstring"""






       with open(__lowercase     ) as metadata_file:
              __UpperCamelCase       =  json.load(__lowercase     )
       __UpperCamelCase       =  LukeConfig(use_entity_aware_attention=__lowercase				,  **metadata['model_config']     )

       # Load in the weights from the checkpoint_path
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )

       # Load the entity vocab file
       __UpperCamelCase       =  load_entity_vocab(__lowercase     )

       __UpperCamelCase       =  RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

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

       __UpperCamelCase       =  LukeTokenizer.from_pretrained(__lowercase     )

       # 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=__lowercase     ).eval()

       __UpperCamelCase       ,						__UpperCamelCase       =  model.load_state_dict(__lowercase				,  strict=__lowercase     )
       if not (len(__lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
              raise ValueError(F'''Missing keys {', '.join(__lowercase     )}. 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(__lowercase				,  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(__lowercase				,  entity_spans=[span]				,  add_prefix_space=__lowercase				,  return_tensors='pt'     )

       __UpperCamelCase       =  model(**__lowercase     )

       # Verify word hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 42, 1024)     )
              __UpperCamelCase       =  torch.tensor(
                  [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 42, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]     )

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

       # Verify entity hidden states
       if model_size == "large":
              __UpperCamelCase       =  torch.Size((1, 1, 1024)     )
              __UpperCamelCase       =  torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]     )
       else:  # base
              __UpperCamelCase       =  torch.Size((1, 1, 768)     )
              __UpperCamelCase       =  torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]     )

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

       # Finally, save our PyTorch model and tokenizer
       print('Saving PyTorch model to {}'.format(__lowercase     )     )
       model.save_pretrained(__lowercase     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      List[str]:



       """simple docstring"""






       __UpperCamelCase       =  {}
       with open(__lowercase				,  'r'				,  encoding='utf-8'     ) as f:
              for index, line in enumerate(__lowercase     ):
                     __UpperCamelCase       ,						__UpperCamelCase       =  line.rstrip().split('\t'     )
                     __UpperCamelCase       =  index

       return entity_vocab


if __name__ == "__main__":
    a__							:							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.'''
    )
    a__							:							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,
    )
