



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 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Tuple	,					snake_case__	:   List[str]	,					snake_case__	:   Optional[Any]	,					snake_case__	:   Any     )     ->					str:
   # Load configuration defined in the metadata file
   with open(snake_case__     ) as metadata_file:
      UpperCamelCase   :  List[str]						     =     json.load(snake_case__     )
   UpperCamelCase   :  Union[str, Any]						     =     LukeConfig(use_entity_aware_attention=snake_case__	,					**metadata['model_config']     )

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

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

   UpperCamelCase   :  str						     =     XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']     )

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

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

   UpperCamelCase   :  Union[str, Any]						     =     MLukeTokenizer.from_pretrained(snake_case__     )

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

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

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

   UpperCamelCase   :  Tuple						     =     LukeForMaskedLM(config=snake_case__     ).eval()

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

   UpperCamelCase     ,					UpperCamelCase   :  int						     =     model.load_state_dict(snake_case__	,					strict=snake_case__     )

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

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

   UpperCamelCase   :  str						     =     model(**snake_case__     )

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      UpperCamelCase   :  Optional[int]						     =     torch.Size((1, 33, 768)     )
      UpperCamelCase   :  Union[str, Any]						     =     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]	,					snake_case__	,					atol=1E-4     ):
      raise ValueError

   # Verify entity hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      UpperCamelCase   :  Tuple						     =     torch.Size((1, 1, 768)     )
      UpperCamelCase   :  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]	,					snake_case__	,					atol=1E-4     ):
      raise ValueError

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

   UpperCamelCase   :  Any						     =     model(**snake_case__     )

   UpperCamelCase   :  Tuple						     =     encoding['input_ids'][0].tolist()
   UpperCamelCase   :  int						     =     input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'     )     )
   UpperCamelCase   :  Dict						     =     outputs.logits[0][mask_position_id].argmax(dim=-1     )
   assert "Japan" == tokenizer.decode(snake_case__     )

   UpperCamelCase   :  List[Any]						     =     outputs.entity_logits[0][0].argmax().item()
   UpperCamelCase   :  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(snake_case__     )     )
   model.save_pretrained(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					Optional[Any]:
   UpperCamelCase   :  Dict						     =     ['[MASK]', '[PAD]', '[UNK]']

   UpperCamelCase   :  Optional[Any]						     =     [json.loads(snake_case__     ) for line in open(snake_case__     )]

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


if __name__ == "__main__":
    __UpperCAmelCase	       =	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	       =	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 asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]=False     )     ->					Optional[Any]:
   try:
      UpperCamelCase   :  List[Any]						     =     os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      UpperCamelCase   :  List[Any]						     =     default
   else:
      # KEY is set, convert it to True or False.
      try:
         UpperCamelCase   :  Optional[Any]						     =     strtobool(snake_case__     )
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F"""If set, {key} must be yes or no."""     )
   return _value


__UpperCAmelCase	       =	parse_flag_from_env('''RUN_SLOW''', default=False)
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					str:
   return unittest.skip('Test was skipped'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(_run_slow_tests	,					'test is slow'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(not torch.cuda.is_available()	,					'test requires only a CPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(torch.cuda.is_available()	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					List[Any]:
   return unittest.skipUnless(is_xpu_available()	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_mps_available()	,					'test requires a `mps` backend support in `torch`'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					Tuple:
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()	,					'test requires the Hugging Face suite'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_bnb_available()	,					'test requires the bitsandbytes library'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   return unittest.skipUnless(is_tpu_available()	,					'test requires TPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[int]:
   return unittest.skipUnless(torch.cuda.device_count() == 1	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(torch.xpu.device_count() == 1	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Any:
   return unittest.skipUnless(torch.cuda.device_count() > 1	,					'test requires multiple GPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   return unittest.skipUnless(torch.xpu.device_count() > 1	,					'test requires multiple XPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Any:
   return unittest.skipUnless(is_safetensors_available()	,					'test requires safetensors'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(is_deepspeed_available()	,					'test requires DeepSpeed'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_torch_version('>='	,					'1.12.0'     )	,					'test requires torch version >= 1.12.0'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict=None	,					snake_case__	:   Union[str, Any]=None     )     ->					Optional[Any]:
   if test_case is None:
      return partial(snake_case__	,					version=snake_case__     )
   return unittest.skipUnless(is_torch_version('>='	,					snake_case__     )	,					F"""test requires torch version >= {version}"""     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_tensorboard_available()	,					'test requires Tensorboard'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					Tuple:
   return unittest.skipUnless(is_wandb_available()	,					'test requires wandb'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					List[Any]:
   return unittest.skipUnless(is_comet_ml_available()	,					'test requires comet_ml'     )(snake_case__     )


__UpperCAmelCase	       =	(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(
       _atleast_one_tracker_available	,					'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,					)(snake_case__     )



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  True
       @classmethod
       def  snake_case_	(      cls	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     tempfile.mkdtemp()
       @classmethod
       def  snake_case_	(      cls	)					->		Tuple:
          if os.path.exists(cls.tmpdir	):
             shutil.rmtree(cls.tmpdir	)






       def  snake_case_	(      self	)					->		str:
          if self.clear_on_setup:
             for path in Path(self.tmpdir	).glob('**/*'	):
                if path.is_file():
                   path.unlink()
                elif path.is_dir():
                   shutil.rmtree(SCREAMING_SNAKE_CASE_	)






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		Optional[int]:
          super().tearDown()
          # Reset the state of the AcceleratorState singleton.
          AcceleratorState._reset_state()
          PartialState._reset_state()






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     mocks if isinstance(SCREAMING_SNAKE_CASE_,      (tuple, list)	) else [mocks]
          for m in self.mocks:
             m.start()
             self.addCleanup(m.stop	)






def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Optional[int]:
   UpperCamelCase   :  Tuple						     =     AcceleratorState()
   UpperCamelCase   :  Tuple						     =     tensor[None].clone().to(state.device     )
   UpperCamelCase   :  str						     =     gather(snake_case__     ).cpu()
   UpperCamelCase   :  Union[str, Any]						     =     tensor[0].cpu()
   for i in range(tensors.shape[0]     ):
      if not torch.equal(tensors[i]	,					snake_case__     ):
         return False
   return True



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  List[Any]						     =     returncode
          UpperCamelCase   :  Tuple						     =     stdout
          UpperCamelCase   :  Any						     =     stderr






async def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Union[str, Any]:
   while True:
      UpperCamelCase   :  List[str]						     =     await stream.readline()
      if line:
         callback(snake_case__     )
      else:
         break
async def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   int=None	,					snake_case__	:   Dict=None	,					snake_case__	:   Any=None	,					snake_case__	:   Optional[Any]=False	,					snake_case__	:   Union[str, Any]=False     )     ->					_RunOutput:
   if echo:
      print('\nRunning: '	,					' '.join(snake_case__     )     )

   UpperCamelCase   :  Dict						     =     await asyncio.create_subprocess_exec(
       cmd[0]	,					*cmd[1:]	,					stdin=snake_case__	,					stdout=asyncio.subprocess.PIPE	,					stderr=asyncio.subprocess.PIPE	,					env=snake_case__	,					)

   # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
   # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
   #
   # If it starts hanging, will need to switch to the following code. The problem is that no data
   # will be seen until it's done and if it hangs for example there will be no debug info.
   # out, err = await p.communicate()
   # return _RunOutput(p.returncode, out, err)

   UpperCamelCase   :  Tuple						     =     []
   UpperCamelCase   :  int						     =     []

   def tee(snake_case__	:   List[Any]	,					snake_case__	:   Optional[int]	,					snake_case__	:   Optional[int]	,					snake_case__	:   List[Any]=""     ):
      UpperCamelCase   :  Union[str, Any]						     =     line.decode('utf-8'     ).rstrip()
      sink.append(snake_case__     )
      if not quiet:
         print(snake_case__	,					snake_case__	,					file=snake_case__     )

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stdout	,					label='stdout:'     )     )     ),
           asyncio.create_task(_read_stream(p.stderr	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stderr	,					label='stderr:'     )     )     ),
       ]	,					timeout=snake_case__	,					)
   return _RunOutput(await p.wait()	,					snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any=None	,					snake_case__	:   Tuple=None	,					snake_case__	:   Any=180	,					snake_case__	:   Any=False	,					snake_case__	:   Optional[int]=True     )     ->					_RunOutput:
   UpperCamelCase   :  int						     =     asyncio.get_event_loop()
   UpperCamelCase   :  Tuple						     =     loop.run_until_complete(
       _stream_subprocess(snake_case__	,					env=snake_case__	,					stdin=snake_case__	,					timeout=snake_case__	,					quiet=snake_case__	,					echo=snake_case__     )     )

   UpperCamelCase   :  str						     =     ' '.join(snake_case__     )
   if result.returncode > 0:
      UpperCamelCase   :  Union[str, Any]						     =     '\n'.join(result.stderr     )
      raise RuntimeError(
          F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
          F"""The combined stderr from workers follows:\n{stderr}"""     )

   return result



class     lowerCAmelCase_		(  a__							):
       pass






def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   str=False     )     ->					int:
   try:
      UpperCamelCase   :  Union[str, Any]						     =     subprocess.check_output(snake_case__	,					stderr=subprocess.STDOUT     )
      if return_stdout:
         if hasattr(snake_case__	,					'decode'     ):
            UpperCamelCase   :  Optional[int]						     =     output.decode('utf-8'     )
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          F"""Command `{" ".join(snake_case__     )}` failed with the following error:\n\n{e.output.decode()}"""     ) from e



