


"""simple docstring"""







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 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				,      SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  with open(SCREAMING_SNAKE_CASE				) as metadata_file:
    lowerCAmelCase     :     Tuple								=							json.load(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[Any]								=							LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE				,      **metadata["model_config"]				)

  # Load in the weights from the checkpoint_path
  lowerCAmelCase     :     Optional[int]								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)["module"]

  # Load the entity vocab file
  lowerCAmelCase     :     List[str]								=							load_original_entity_vocab(SCREAMING_SNAKE_CASE				)
  # add an entry for [MASK2]
  lowerCAmelCase     :     Dict								=							max(entity_vocab.values()				) + 1
  config.entity_vocab_size += 1

  lowerCAmelCase     :     List[Any]								=							XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]				)

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

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

  lowerCAmelCase     :     List[Any]								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				)

  # Initialize the embeddings of the special tokens
  lowerCAmelCase     :     Union[str, Any]								=							tokenizer.convert_tokens_to_ids(["@"]				)[0]
  lowerCAmelCase     :     Tuple								=							tokenizer.convert_tokens_to_ids(["#"]				)[0]

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

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

  lowerCAmelCase     :     Dict								=							LukeForMaskedLM(config=SCREAMING_SNAKE_CASE				).eval()

  state_dict.pop("entity_predictions.decoder.weight"				)
  state_dict.pop("lm_head.decoder.weight"				)
  state_dict.pop("lm_head.decoder.bias"				)
  lowerCAmelCase     :     str								=							OrderedDict()
  for key, value in state_dict.items():
    if not (key.startswith("lm_head"				) or key.startswith("entity_predictions"				)):
      lowerCAmelCase     :     str								=							state_dict[key]
    else:
      lowerCAmelCase     :     int								=							state_dict[key]

  lowerCAmelCase						,  lowerCAmelCase     :     Optional[int]								=							model.load_state_dict(SCREAMING_SNAKE_CASE				,      strict=SCREAMING_SNAKE_CASE				)

  if set(SCREAMING_SNAKE_CASE				) != {"luke.embeddings.position_ids"}:
    raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""				)
  if set(SCREAMING_SNAKE_CASE				) != {
      "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
  lowerCAmelCase     :     str								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				,      task="entity_classification"				)

  lowerCAmelCase     :     str								=							"ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
  lowerCAmelCase     :     Dict								=							(0, 9)
  lowerCAmelCase     :     Optional[int]								=							tokenizer(SCREAMING_SNAKE_CASE				,      entity_spans=[span]				,      return_tensors="pt"				)

  lowerCAmelCase     :     Optional[Any]								=							model(**SCREAMING_SNAKE_CASE				)

  # Verify word hidden states
  if model_size == "large":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :     Tuple								=							torch.Size((1, 3_3, 7_6_8)				)
    lowerCAmelCase     :     Dict								=							torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]				)

  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":
    raise NotImplementedError
  else:  # base
    lowerCAmelCase     :     Tuple								=							torch.Size((1, 1, 7_6_8)				)
    lowerCAmelCase     :     Optional[int]								=							torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]				)

  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

  # Verify masked word/entity prediction
  lowerCAmelCase     :     Tuple								=							MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							"Tokyo is the capital of <mask>."
  lowerCAmelCase     :     Optional[int]								=							(2_4, 3_0)
  lowerCAmelCase     :     Any								=							tokenizer(SCREAMING_SNAKE_CASE				,      entity_spans=[span]				,      return_tensors="pt"				)

  lowerCAmelCase     :     Any								=							model(**SCREAMING_SNAKE_CASE				)

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

  lowerCAmelCase     :     Any								=							outputs.entity_logits[0][0].argmax().item()
  lowerCAmelCase     :     List[str]								=							[
      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(SCREAMING_SNAKE_CASE				)				)
  model.save_pretrained(SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							["[MASK]", "[PAD]", "[UNK]"]

  lowerCAmelCase     :     Tuple								=							[json.loads(SCREAMING_SNAKE_CASE				) for line in open(SCREAMING_SNAKE_CASE				)]

  lowerCAmelCase     :     Any								=							{}
  for entry in data:
    lowerCAmelCase     :     Tuple								=							entry["id"]
    for entity_name, language in entry["entities"]:
      if entity_name in SPECIAL_TOKENS:
        lowerCAmelCase     :     Tuple								=							entity_id
        break
      lowerCAmelCase     :     Tuple								=							f"""{language}:{entity_name}"""
      lowerCAmelCase     :     Tuple								=							entity_id
  return new_mapping


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.'''
     )
     lowerCAmelCase__      				=   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 inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					inspect.getfile(accelerate.test_utils	)
  __UpperCAmelCase				:				List[str]   =					os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_cli.py''']	)

  __UpperCAmelCase				:				Dict   =					['''accelerate''', '''launch''']
  __UpperCAmelCase				:				Dict   =					Path.home() / '''.cache/huggingface/accelerate'''
  __UpperCAmelCase				:				Dict   =					'''default_config.yaml'''
  __UpperCAmelCase				:				Optional[Any]   =					config_folder / config_file
  __UpperCAmelCase				:				Dict   =					config_folder / '''_default_config.yaml'''

  __UpperCAmelCase				:				Any   =					Path('''tests/test_configs'''	)


  @classmethod
  def   __lowercase		(		cls      :     int       ):


     '''simple docstring'''






     if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path       )


  @classmethod
  def   __lowercase		(		cls      :     List[Any]       ):


     '''simple docstring'''






     if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Dict			=   self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]							,env=os.environ.copy()       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     for config in sorted(self.test_config_path.glob('**/*.yaml'       )       ):
        with self.subTest(config_file=_a       ):
           execute_subprocess_async(
               self.base_cmd + ['--config_file', str(_a       ), self.test_file_path]							,env=os.environ.copy()       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     execute_subprocess_async(['accelerate', 'test']							,env=os.environ.copy()       )







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					'''test-tpu'''
  __UpperCAmelCase				:				Any   =					'''us-central1-a'''
  __UpperCAmelCase				:				List[Any]   =					'''ls'''
  __UpperCAmelCase				:				Any   =					['''accelerate''', '''tpu-config''']
  __UpperCAmelCase				:				Dict   =					'''cd /usr/share'''
  __UpperCAmelCase				:				Any   =					'''tests/test_samples/test_command_file.sh'''
  __UpperCAmelCase				:				List[Any]   =					'''Running gcloud compute tpus tpu-vm ssh'''


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Any			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']							,return_stdout=_a       )
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	List[str]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Any			=   run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)



