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

   # Load in the weights from the checkpoint_path
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')['''module''']

   # Load the entity vocab file
   lowerCAmelCase_			=      load_original_entity_vocab(__snake_case)
   # add an entry for [MASK2]
   lowerCAmelCase_			=      max(entity_vocab.values()) + 1
   config.entity_vocab_size += 1

   lowerCAmelCase_			=      XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

   # Add special tokens to the token vocabulary for downstream tasks
   lowerCAmelCase_			=      AddedToken('''<ent>'''	,   lstrip=__snake_case	,   rstrip=__snake_case)
   lowerCAmelCase_			=      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:
      lowerCAmelCase_			=      json.load(__snake_case)
   lowerCAmelCase_			=      '''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)

   lowerCAmelCase_			=      MLukeTokenizer.from_pretrained(__snake_case)

   # Initialize the embeddings of the special tokens
   lowerCAmelCase_			=      tokenizer.convert_tokens_to_ids(['''@'''])[0]
   lowerCAmelCase_			=      tokenizer.convert_tokens_to_ids(['''#'''])[0]

   lowerCAmelCase_			=      state_dict['''embeddings.word_embeddings.weight''']
   lowerCAmelCase_			=      word_emb[ent_init_index].unsqueeze(0)
   lowerCAmelCase_			=      word_emb[enta_init_index].unsqueeze(0)
   lowerCAmelCase_			=      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_			=      state_dict[bias_name]
      lowerCAmelCase_			=      decoder_bias[ent_init_index].unsqueeze(0)
      lowerCAmelCase_			=      decoder_bias[enta_init_index].unsqueeze(0)
      lowerCAmelCase_			=      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_			=      F'''encoder.layer.{layer_index}.attention.self.'''
         lowerCAmelCase_			=      state_dict[prefix + matrix_name]
         lowerCAmelCase_			=      state_dict[prefix + matrix_name]
         lowerCAmelCase_			=      state_dict[prefix + matrix_name]

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

   lowerCAmelCase_			=      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''')
   lowerCAmelCase_			=      OrderedDict()
   for key, value in state_dict.items():
      if not (key.startswith('''lm_head''') or key.startswith('''entity_predictions''')):
         lowerCAmelCase_			=      state_dict[key]
      else:
         lowerCAmelCase_			=      state_dict[key]

   lowerCAmelCase_	,lowerCAmelCase_			=      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
   lowerCAmelCase_			=      MLukeTokenizer.from_pretrained(__snake_case	,   task='''entity_classification''')

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

   lowerCAmelCase_			=      model(**__snake_case)

   # Verify word hidden states
   if model_size == "large":
      raise NotImplementedError
   else:  # base
      lowerCAmelCase_			=      torch.Size((1, 33, 768))
      lowerCAmelCase_			=      torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]])

   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
      lowerCAmelCase_			=      torch.Size((1, 1, 768))
      lowerCAmelCase_			=      torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]])

   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
   lowerCAmelCase_			=      MLukeTokenizer.from_pretrained(__snake_case)
   lowerCAmelCase_			=      '''Tokyo is the capital of <mask>.'''
   lowerCAmelCase_			=      (24, 30)
   lowerCAmelCase_			=      tokenizer(__snake_case	,   entity_spans=[span]	,   return_tensors='''pt''')

   lowerCAmelCase_			=      model(**__snake_case)

   lowerCAmelCase_			=      encoding['''input_ids'''][0].tolist()
   lowerCAmelCase_			=      input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>'''))
   lowerCAmelCase_			=      outputs.logits[0][mask_position_id].argmax(dim=-1)
   assert "Japan" == tokenizer.decode(__snake_case)

   lowerCAmelCase_			=      outputs.entity_logits[0][0].argmax().item()
   lowerCAmelCase_			=      [
       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 snake_case_      (					__snake_case  :							List[str])			->					Dict:
   lowerCAmelCase_			=      ['''[MASK]''', '''[PAD]''', '''[UNK]''']

   lowerCAmelCase_			=      [json.loads(__snake_case) for line in open(__snake_case)]

   lowerCAmelCase_			=      {}
   for entry in data:
      lowerCAmelCase_			=      entry['''id''']
      for entity_name, language in entry["entities"]:
         if entity_name in SPECIAL_TOKENS:
            lowerCAmelCase_			=      entity_id
            break
         lowerCAmelCase_			=      F'''{language}:{entity_name}'''
         lowerCAmelCase_			=      entity_id
   return new_mapping


if __name__ == "__main__":
 A_     :						Union[str, 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_     :						Optional[Any]    							=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 dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)







@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     list_field(
							    default=[],  metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    },  )

							UpperCAmelCase				   =     list_field(
							    default=[8],  metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )

							UpperCAmelCase				   =     list_field(
							    default=[8, 32, 128, 512],  metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},  )

							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Use FP16 to accelerate inference."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Benchmark training of model"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Verbose memory tracing"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    },  )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Trace memory line by line"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save result to a CSV file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save all print statements in a log file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Whether to print environment information"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""env_info_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving environment information."},  )
							UpperCAmelCase				   =     field(
							    default=F"""log_{round(time() )}.csv""",  metadata={"help": "Log filename used if print statements are saved in log."},  )
							UpperCAmelCase				   =     field(default=3,  metadata={"help": "Times an experiment will be run."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    },  )







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												warnings.warn(
												    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
												    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
												    ''' to benchmark Transformer models.''' ,			_A ,			)







							def  UpperCamelCase_						(   self      :     str		):
												return json.dumps(dataclasses.asdict(self		) ,			indent=2		)







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												if len(self.models		) <= 0:
																	raise ValueError(
																	    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																	    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''		)
												return self.models







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												if not self.multi_process:
																	return False
												elif self.is_tpu:
																	logger.info('''Multiprocessing is currently not possible on TPU.'''		)
																	return False
												else:
																	return True
