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

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

     # Load the entity vocab file
     snake_case_					       =					load_entity_vocab(UpperCamelCase__					)

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

     # Add special tokens to the token vocabulary for downstream tasks
     snake_case_					       =					AddedToken('<ent>'						,					lstrip=UpperCamelCase__						,					rstrip=UpperCamelCase__					)
     snake_case_					       =					AddedToken('<ent2>'						,					lstrip=UpperCamelCase__						,					rstrip=UpperCamelCase__					)
     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(UpperCamelCase__					)
     with open(os.path.join(UpperCamelCase__						,					LukeTokenizer.vocab_files_names['entity_vocab_file']					)						,					'w'					) as f:
          json.dump(UpperCamelCase__						,					UpperCamelCase__					)

     snake_case_					       =					LukeTokenizer.from_pretrained(UpperCamelCase__					)

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

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

     snake_case_					       =					LukeModel(config=UpperCamelCase__					).eval()

     snake_case_					       =					model.load_state_dict(UpperCamelCase__						,					strict=UpperCamelCase__					)
     if not (len(UpperCamelCase__					) == 1 and missing_keys[0] == "embeddings.position_ids"):
          raise ValueError(f'Missing keys {", ".join(UpperCamelCase__					)}. 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
     snake_case_					       =					LukeTokenizer.from_pretrained(UpperCamelCase__						,					task='entity_classification'					)

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

     snake_case_					       =					model(**UpperCamelCase__					)

     # Verify word hidden states
     if model_size == "large":
          snake_case_					       =					torch.Size((1, 42, 1024)					)
          snake_case_					       =					torch.tensor(
              [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]					)
     else:  # base
          snake_case_					       =					torch.Size((1, 42, 768)					)
          snake_case_					       =					torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]					)

     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]						,					UpperCamelCase__						,					atol=1e-4					):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          snake_case_					       =					torch.Size((1, 1, 1024)					)
          snake_case_					       =					torch.tensor([[0.0_466, -0.0_106, -0.0_179]]					)
     else:  # base
          snake_case_					       =					torch.Size((1, 1, 768)					)
          snake_case_					       =					torch.tensor([[0.1_457, 0.1_044, 0.0_174]]					)

     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]						,					UpperCamelCase__						,					atol=1e-4					):
          raise ValueError

     # Finally, save our PyTorch model and tokenizer
     print('Saving PyTorch model to {}'.format(UpperCamelCase__					)					)
     model.save_pretrained(UpperCamelCase__					)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Union[str, Any]:
     snake_case_					       =					{}
     with open(UpperCamelCase__						,					'r'						,					encoding='utf-8'					) as f:
          for index, line in enumerate(UpperCamelCase__					):
               snake_case_					       =					line.rstrip().split('\t'					)
               snake_case_					       =					index

     return entity_vocab


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,
    )






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


