


'''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 		_A			(lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Tuple			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:List[Any]			,  lowerCAmelCase__					:Tuple						)				->					List[str]:




     '''simple docstring'''



     with open(lowercase__						) as metadata_file:
          _a      =							json.load(lowercase__						)
     _a      =							LukeConfig(use_entity_aware_attention=lowercase__			,  **metadata['model_config']						)

     # Load in the weights from the checkpoint_path
     _a      =							torch.load(lowercase__			,  map_location='cpu'						)

     # Load the entity vocab file
     _a      =							load_entity_vocab(lowercase__						)

     _a      =							RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

     # Add special tokens to the token vocabulary for downstream tasks
     _a      =							AddedToken('<ent>'			,  lstrip=lowercase__			,  rstrip=lowercase__						)
     _a      =							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__						)

     _a      =							LukeTokenizer.from_pretrained(lowercase__						)

     # Initialize the embeddings of the special tokens
     _a      =							state_dict['embeddings.word_embeddings.weight']
     _a      =							word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
     _a      =							word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
     _a      =							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"]:
               _a      =							f'encoder.layer.{layer_index}.attention.self.'
               _a      =							state_dict[prefix + matrix_name]
               _a      =							state_dict[prefix + matrix_name]
               _a      =							state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
     _a      =							state_dict['entity_embeddings.entity_embeddings.weight']
     _a      =							entity_emb[entity_vocab['[MASK]']]

     _a      =							LukeModel(config=lowercase__						).eval()

     _a ,				_a      =							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
     _a      =							LukeTokenizer.from_pretrained(lowercase__			,  task='entity_classification'						)

     _a      =							(
         '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 .'
     )
     _a      =							(39, 42)
     _a      =							tokenizer(lowercase__			,  entity_spans=[span]			,  add_prefix_space=lowercase__			,  return_tensors='pt'						)

     _a      =							model(**lowercase__						)

     # Verify word hidden states
     if model_size == "large":
          _a      =							torch.Size((1, 42, 10_24)						)
          _a      =							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
          _a      =							torch.Size((1, 42, 7_68)						)
          _a      =							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":
          _a      =							torch.Size((1, 1, 10_24)						)
          _a      =							torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]						)
     else:  # base
          _a      =							torch.Size((1, 1, 7_68)						)
          _a      =							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 		_A			(lowerCAmelCase__					:Optional[int]						)				->					Dict:




     '''simple docstring'''



     _a      =							{}
     with open(lowercase__			,  'r'			,  encoding='utf-8'						) as f:
          for index, line in enumerate(lowercase__						):
               _a ,				_a      =							line.rstrip().split('\t'						)
               _a      =							index

     return entity_vocab


if __name__ == "__main__":
 a_   :      Optional[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_   :      int				    =      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'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

