
"""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 							A		(       snake_case   :Any       ,      snake_case   :Tuple       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[Any]       ,      snake_case   :List[str]	)   ->			List[Any]:
 # Load configuration defined in the metadata file
 with open(snake_case	) as metadata_file:
  __UpperCamelCase       						= json.load(snake_case	)
 __UpperCamelCase       						= LukeConfig(use_entity_aware_attention=snake_case       ,      **metadata['model_config']	)

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

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

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

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

 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case	)

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

 __UpperCamelCase       						= state_dict['embeddings.word_embeddings.weight']
 __UpperCamelCase       						= word_emb[ent_init_index].unsqueeze(0	)
 __UpperCamelCase       						= word_emb[enta_init_index].unsqueeze(0	)
 __UpperCamelCase       						= 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"]:
  __UpperCamelCase       						= state_dict[bias_name]
  __UpperCamelCase       						= decoder_bias[ent_init_index].unsqueeze(0	)
  __UpperCamelCase       						= decoder_bias[enta_init_index].unsqueeze(0	)
  __UpperCamelCase       						= 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"]:
   __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]']].unsqueeze(0	)
 __UpperCamelCase       						= torch.cat([entity_emb, entity_mask_emb]	)
 # add [MASK2] for 'entity_predictions.bias'
 __UpperCamelCase       						= state_dict['entity_predictions.bias']
 __UpperCamelCase       						= entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0	)
 __UpperCamelCase       						= torch.cat([entity_prediction_bias, entity_mask_bias]	)

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

 __UpperCamelCase						,			__UpperCamelCase       						= 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
 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case       ,      task='entity_classification'	)

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

 __UpperCamelCase       						= model(**snake_case	)

 # Verify word hidden states
 if model_size == "large":
  raise NotImplementedError
 else:  # base
  __UpperCamelCase       						= torch.Size((1, 3_3, 7_6_8)	)
  __UpperCamelCase       						= torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]]	)

 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
  __UpperCamelCase       						= torch.Size((1, 1, 7_6_8)	)
  __UpperCamelCase       						= torch.tensor([[-0.1_482, 0.0_609, 0.0_322]]	)

 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
 __UpperCamelCase       						= MLukeTokenizer.from_pretrained(snake_case	)
 __UpperCamelCase       						= 'Tokyo is the capital of <mask>.'
 __UpperCamelCase       						= (2_4, 3_0)
 __UpperCamelCase       						= tokenizer(snake_case       ,      entity_spans=[span]       ,      return_tensors='pt'	)

 __UpperCamelCase       						= model(**snake_case	)

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

 __UpperCamelCase       						= outputs.entity_logits[0][0].argmax().item()
 __UpperCamelCase       						= [
     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 							A		(       snake_case   :str	)   ->			List[str]:
 __UpperCamelCase       						= ['[MASK]', '[PAD]', '[UNK]']

 __UpperCamelCase       						= [json.loads(snake_case	) for line in open(snake_case	)]

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


if __name__ == "__main__":
      UpperCamelCase			:       List[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."
      )
      UpperCamelCase			:       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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:       Dict									=				{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				[
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[Any]									=				[
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

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



