



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   :  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 argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    parser.add_argument('''--user''', type=str, default='''ubuntu''')
    parser.add_argument('''--host''', type=str, default='''localhost''')
    parser.add_argument('''--key_path''', type=str, default=None)
    parser.add_argument('''--instance''', type=str, default='''V100:1''')
    parser.add_argument('''--provider''', type=str, default='''cheapest''')
    parser.add_argument('''--use_spot''', type=bool, default=False)
    parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
    __UpperCAmelCase       ,  __UpperCAmelCase	       =	parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
        )
    else:
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    __UpperCAmelCase	       =	args.example.rsplit('''/''', 1)[0]

    # Set up remote environment
    cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
    cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([F"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)



