





'''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,
    )




"""simple docstring"""







import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


a__				:							Union[str, Any]						=     logging.get_logger(__name__)




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				os.getenv("SM_HP_MP_PARAMETERS"  ,						"{}"   )
					try:
										# Parse it and check the field "partitions" is included, it is required for model parallel.
										__SCREAMING_SNAKE_CASE						   =				json.loads(lowerCAmelCase_   )
										if "partitions" not in smp_options:
															return False
					except json.JSONDecodeError:
										return False

					# Get the sagemaker specific framework parameters from mpi_options variable.
					__SCREAMING_SNAKE_CASE						   =				os.getenv("SM_FRAMEWORK_PARAMS"  ,						"{}"   )
					try:
										# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
										__SCREAMING_SNAKE_CASE						   =				json.loads(lowerCAmelCase_   )
										if not mpi_options.get("sagemaker_mpi_enabled"  ,						lowerCAmelCase_   ):
															return False
					except json.JSONDecodeError:
										return False
					# Lastly, check if the `smdistributed` module is present.
					return importlib.util.find_spec("smdistributed"   ) is not None


if is_sagemaker_model_parallel_available():
		import smdistributed.modelparallel.torch as smp

		smp.init()




@dataclass
class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  str    					=			field(
	    default=""			,  metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}			,  )




	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	Any:
						super().__post_init__()
						warnings.warn(
						    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
						    "`TrainingArguments` instead."			,			UpperCAmelCase__			,			)




	@cached_property
	def   UpperCAmelCase_      ( self			:				List[str]   )       ->	"torch.device":
						logger.info("PyTorch: setting up devices"   )
						if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
											logger.warning(
											    "torch.distributed process group is initialized, but local_rank == -1. "
											    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"   )
						if self.no_cuda:
											__SCREAMING_SNAKE_CASE						   =				torch.device("cpu"   )
											__SCREAMING_SNAKE_CASE						   =				0
						elif is_sagemaker_model_parallel_available():
											__SCREAMING_SNAKE_CASE						   =				smp.local_rank()
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				1
						elif is_sagemaker_dp_enabled():
											import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

											torch.distributed.init_process_group(backend="smddp"			,			timeout=self.ddp_timeout_delta   )
											__SCREAMING_SNAKE_CASE						   =				int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"   )   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				1
						elif self.local_rank == -1:
											# if n_gpu is > 1 we'll use nn.DataParallel.
											# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
											# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
											# trigger an error that a device index is missing. Index 0 takes into account the
											# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
											# will use the first GPU in that env, i.e. GPU#1
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )
											# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
											# the default value.
											__SCREAMING_SNAKE_CASE						   =				torch.cuda.device_count()
						else:
											# Here, we'll use torch.distributed.
											# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
											if not torch.distributed.is_initialized():
																torch.distributed.init_process_group(backend="nccl"			,			timeout=self.ddp_timeout_delta   )
											__SCREAMING_SNAKE_CASE						   =				torch.device("cuda"			,			self.local_rank   )
											__SCREAMING_SNAKE_CASE						   =				1

						if device.type == "cuda":
											torch.cuda.set_device(UpperCAmelCase__   )

						return device




	@property
	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Any:
						if is_sagemaker_model_parallel_available():
											return smp.dp_size()

						return super().world_size




	@property
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	List[Any]:
						return not is_sagemaker_model_parallel_available()




	@property
	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	int:
						return False
