



'''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       lowerCamelCase__       (						_A ,			_A ,			_A ,			_A ,			_A				):






  with open(__lowercase				) as metadata_file:
    a						:					List[Any]        =     json.load(__lowercase				)
  a						:					List[str]        =     LukeConfig(use_entity_aware_attention=__lowercase ,			**metadata['model_config']				)

  # Load in the weights from the checkpoint_path
  a						:					Optional[int]        =     torch.load(__lowercase ,			map_location='cpu'				)

  # Load the entity vocab file
  a						:					Optional[Any]        =     load_entity_vocab(__lowercase				)

  a						:					Optional[int]        =     RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']				)

  # Add special tokens to the token vocabulary for downstream tasks
  a						:					Tuple        =     AddedToken('<ent>' ,			lstrip=__lowercase ,			rstrip=__lowercase				)
  a						:					Any        =     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						:					Any        =     LukeTokenizer.from_pretrained(__lowercase				)

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

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

  a						:					Optional[int]        =     LukeModel(config=__lowercase				).eval()

  a   ,	a						:					Any        =     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						:					Dict        =     LukeTokenizer.from_pretrained(__lowercase ,			task='entity_classification'				)

  a						:					str        =     (
      '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						:					Union[str, Any]        =     (39, 42)
  a						:					Optional[int]        =     tokenizer(__lowercase ,			entity_spans=[span] ,			add_prefix_space=__lowercase ,			return_tensors='pt'				)

  a						:					List[Any]        =     model(**__lowercase				)

  # Verify word hidden states
  if model_size == "large":
    a						:					List[str]        =     torch.Size((1, 42, 1024)				)
    a						:					Dict        =     torch.tensor(
        [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]				)
  else:  # base
    a						:					Dict        =     torch.Size((1, 42, 768)				)
    a						:					List[Any]        =     torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]				)

  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						:					Optional[Any]        =     torch.Size((1, 1, 1024)				)
    a						:					int        =     torch.tensor([[0.0466, -0.0106, -0.0179]]				)
  else:  # base
    a						:					Optional[Any]        =     torch.Size((1, 1, 768)				)
    a						:					Any        =     torch.tensor([[0.1457, 0.1044, 0.0174]]				)

  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       lowerCamelCase__       (						_A				):
  a						:					Any        =     {}
  with open(__lowercase ,			'r' ,			encoding='utf-8'				) as f:
    for index, line in enumerate(__lowercase				):
      a   ,	a						:					int        =     line.rstrip().split('\t'				)
      a						:					Dict        =     index

  return entity_vocab


if __name__ == "__main__":
     lowerCAmelCase:					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.'
     )
     lowerCAmelCase:					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'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
